# Best Large Language Models (LLMs) Software

## How Many Large Language Models (LLMs) Software Products Does G2 Track?

**Total Products under this Category:** 24

### Category Stats (Jul 2026)

- **Average Rating:** 4.38/5 The average rating of products in this category, based on all submitted ratings
- **Top Trending Product:** Grok (+1.13%) - Among all products in this category, Grok recorded the largest rating increase compared to last month

_Last updated: July 25, 2026_

## How Does G2 Rank Large Language Models (LLMs) Software Products?

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 3,800+ Authentic Reviews
- 24+ Products
- Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.

## G2 Grid® for Large Language Models (LLMs) Software
 ![G2 Grid® for Large Language Models (LLMs) Software plotting products by satisfaction and market presence](https://www.g2.com/categories/large-language-models-llms/grids.png?focus%5B%5D=1251865&focus%5B%5D=1579500&focus%5B%5D=1332376&focus%5B%5D=1535296&focus%5B%5D=1535318&focus%5B%5D=1579482&focus%5B%5D=1481152)

Highlighted products: ChatGPT, Claude, Gemini, Deepseek, Mistral AI, Grok, and Llama.

Underlying data: [Grid® JSON](https://www.g2.com/categories/large-language-models-llms/grids.json?focus%5B%5D=chatgpt&focus%5B%5D=claude-2025-12-11&focus%5B%5D=google-gemini&focus%5B%5D=deepseek&focus%5B%5D=mistral-ai&focus%5B%5D=xai-grok&focus%5B%5D=llama)

**Sponsored**

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### [ChatGPT](https://www.g2.com/products/chatgpt/reviews)

ChatGPT is an advanced AI language model developed by OpenAI, designed to assist users in generating human-like text based on the input it receives. It serves as a versatile tool for a wide range of applications, including drafting emails, writing code, creating content, and providing detailed explanations on various topics. ChatGPT is continually evolving to enhance user experience and meet diverse needs. Key Features and Functionality: - Natural Language Understanding: ChatGPT can comprehend and generate text that closely resembles human conversation, making interactions intuitive and engaging. - Versatile Applications: It supports tasks such as content creation, coding assistance, learning new concepts, and more, catering to both personal and professional use cases. - Continuous Improvement: OpenAI regularly updates ChatGPT to improve its performance, accuracy, and safety, ensuring it remains a reliable tool for users. Primary Value and User Solutions: ChatGPT addresses the need for efficient and accessible assistance in various domains. By leveraging its advanced language processing capabilities, it helps users save time, enhance productivity, and access information seamlessly. Whether it's drafting documents, learning new subjects, or automating routine tasks, ChatGPT provides a valuable resource that adapts to individual requirements, making it an indispensable tool in today's digital landscape.

**Average Rating:** 4.6/5.0

**Total Reviews:** 2,699

#### How Do G2 Users Rate ChatGPT?

- **Quality of Support:** 8.4/10 (Category avg: 7.5/10)
- **Content Moderation:** 8.4/10 (Category avg: 8.4/10)
- **Contextual Understanding:** 8.6/10 (Category avg: 8.7/10)
- **Bias Mitigation:** 7.7/10 (Category avg: 7.6/10)

#### Who Is the Company Behind ChatGPT?

- **Seller:** [OpenAI](https://www.g2.com/sellers/openai)
- **Year Founded:** 2015
- **HQ Location:** San Francisco, CA
- **Twitter:** @OpenAI (4,941,980 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/openai/ (8,807 employees on LinkedIn®)

#### Who Uses This Product?

- **Who Uses This:** Student, Software Engineer
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 54% Small, 28% Medium

#### What Do G2 Reviewers Say About ChatGPT?

_AI-generated summary from verified user reviews_

##### Pros

- Users find ChatGPT's **ease of use** refreshing, streamlining complex tasks and enhancing daily productivity effortlessly.
- Users love the **quick and conversational responses** from ChatGPT, making information access seamless and efficient.
- Users find ChatGPT to be a **helpful creative assistant** , enhancing productivity with fresh ideas and effective solutions.
- Users value the **time-saving capabilities** of ChatGPT, enhancing productivity and streamlining workflows across diverse tasks.
- Users find that ChatGPT's **time-saving capabilities** streamline their workflows, enhancing creativity and productivity in design tasks.

##### Cons

- Users note the **limitations in accuracy and source referencing** of ChatGPT, requiring occasional verification with external tools.
- Users find the **context understanding** of ChatGPT lacking, leading to confusion and repetition during conversations.
- Users are frustrated by the **limited context window** in ChatGPT, hindering its ability to process extensive inputs effectively.
- Users experience **inaccuracy** in ChatGPT, noting repetitive responses and occasional poor understanding of complex topics.
- Users face **inaccurate responses** from ChatGPT, often requiring rephrasing for better context and clarity in answers.

#### What Are Recent G2 Reviews of ChatGPT?

**["Saves me alot of time on everyday writing and ideas"](https://www.g2.com/survey_responses/chatgpt-review-13164284)**

**Rating:** 5.0/5.0 stars

_— Ravi T._

[Read full review](https://www.g2.com/survey_responses/chatgpt-review-13164284)

**["An Essential Part of My Software Engineering Workflow"](https://www.g2.com/survey_responses/chatgpt-review-13160961)**

**Rating:** 4.5/5.0 stars

_— Hemant J._

[Read full review](https://www.g2.com/survey_responses/chatgpt-review-13160961)

### [Gemini](https://www.g2.com/products/google-gemini/reviews)

Gemini is a family of multimodal, generative AI models. These models were developed by Google DeepMind and Google Research. They are designed to understand, operate across, and combine different types of information. This includes text, images, audio, video, and code. Gemini serves as a versatile, everyday AI assistant and powers a conversational chatbot. Key Product Features & Capabilities Multimodal Understanding: Gemini understands and combines text, images, audio, video, and code. It can analyze complex documents, code repositories, and long videos. Conversational AI: Gemini allows for natural conversations. It functions as an intelligent assistant that can brainstorm, plan, and discuss topics. Deep Research & Analysis: Gemini can analyze websites and user files to generate reports. It can also create audio overviews of the information. Agentic Capabilities: Users can create custom "Gems" (specialized AI experts). The models can act as agents to take actions in tools like Chrome. Integrated Productivity: Gemini is integrated into Gmail, Google Docs, Drive, and Meet. This helps summarize, write, edit, and organize information. Creative Tools: Features include image generation and video creation, enabling the generation of 8-second videos with sound. Long Context Window: High-end models feature up to a 1 million-token context window. This is capable of analyzing large amounts of data.

**Average Rating:** 4.4/5.0

**Total Reviews:** 363

#### How Do G2 Users Rate Gemini?

- **Quality of Support:** 8.6/10 (Category avg: 7.5/10)
- **Content Moderation:** 8.3/10 (Category avg: 8.4/10)
- **Contextual Understanding:** 8.3/10 (Category avg: 8.7/10)
- **Bias Mitigation:** 7.9/10 (Category avg: 7.6/10)

#### Who Is the Company Behind Gemini?

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google (31,899,995 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (341,888 employees on LinkedIn®)
- **Ownership:** NASDAQ:GOOG

#### Who Uses This Product?

- **Who Uses This:** Software Engineer, Research Analyst
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 49% Small, 30% Medium

#### What Do G2 Reviewers Say About Gemini?

_AI-generated summary from verified user reviews_

##### Pros

- Users commend Gemini for its **ease of use** , enabling smooth operations and efficient documentation and problem-solving.
- Users find Gemini's **usefulness for brainstorming and summarizing** notes exceptional, making it a go-to for quick answers.
- Users love Gemini for its **helpfulness** in email creation, brainstorming, and simplifying data analysis tasks.
- Users value the **seamless content creation** capabilities of Gemini, allowing for diverse and impressive visual outputs.
- Users love the **speed** of Gemini, delivering creative suggestions and solutions in mere seconds.

##### Cons

- Users find Gemini's **limitations in accuracy and responsiveness** frustrating, often preferring alternatives for concise answers.
- Users find the **inaccuracy** in image generation and data problematic, affecting trust and usability significantly.
- Users find **usage limitations** in Gemini, particularly in terms of context awareness and integration capabilities.
- Users are frustrated by Gemini's **technical issues** , including inaccurate responses and unreliable code generation, affecting usability.
- Users find that Gemini has a **limited ability to understand context** , resulting in generic responses and missed nuances.

#### What Are Recent G2 Reviews of Gemini?

**["Powerful Multimodal AI with Seamless Google Integration"](https://www.g2.com/survey_responses/gemini-review-13123392)**

**Rating:** 4.0/5.0 stars

_— Himanshu G._

[Read full review](https://www.g2.com/survey_responses/gemini-review-13123392)

**["Fast, Simple, and Reliable for Everyday Answers"](https://www.g2.com/survey_responses/gemini-review-13170999)**

**Rating:** 4.5/5.0 stars

_— Amit C._

[Read full review](https://www.g2.com/survey_responses/gemini-review-13170999)

### [Deepseek](https://www.g2.com/products/deepseek/reviews)

DeepSeek LLM is a series of high-performance, open-source large language models from China-based DeepSeek AI.

**Average Rating:** 4.5/5.0

**Total Reviews:** 20

#### How Do G2 Users Rate Deepseek?

- **Quality of Support:** 7.3/10 (Category avg: 7.5/10)
- **Content Moderation:** 8.8/10 (Category avg: 8.4/10)
- **Contextual Understanding:** 8.5/10 (Category avg: 8.7/10)
- **Bias Mitigation:** 7.9/10 (Category avg: 7.6/10)

#### Who Is the Company Behind Deepseek?

- **Seller:** [DeepSeek](https://www.g2.com/sellers/deepseek)
- **Year Founded:** 2023
- **HQ Location:** Hangzhou
- **LinkedIn® Page:** https://www.linkedin.com/company/deepseek-ai/ (157 employees on LinkedIn®)

#### Who Uses This Product?

- **Top Industries:** Computer Software
- **Company Size:** 70% Small, 20% Medium

#### What Do G2 Reviewers Say About Deepseek?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **fast and accessible performance improvements** of DeepSeek, enhancing their daily tasks with accurate results.
- Users find Deepseek to be exceptionally **easy to use** , with a simple interface and fast responses for various tasks.
- Users find Deepseek's **accuracy** impressive, delivering precise answers and effective solutions for a variety of tasks.
- Users value Deepseek for its **effective content creation capabilities** , enabling efficient generation of ideas and summaries for social media.
- Users value the **creativity enhancement** of DeepSeek, appreciating its ability to generate fresh and diverse content ideas.

##### Cons

- Users often experience **context understanding issues** with Deepseek, affecting the accuracy and relevance of its responses.
- Users often express concerns about **low accuracy** in Deepseek's outputs, impacting the reliability of generated information.
- Users report **technical issues** , particularly with real-time data and missing image/video generation features, impacting satisfaction.
- Users are concerned about **bias and censorship** in Deepseek, questioning its reliability for unbiased information generation.
- Users express significant concerns about **data security** and privacy risks due to data storage practices in China.

#### What Are Recent G2 Reviews of Deepseek?

**["Creative and Efficient AI Assistant for Learning and Productivity"](https://www.g2.com/survey_responses/deepseek-review-12827799)**

**Rating:** 4.0/5.0 stars

_— Yashpal D._

[Read full review](https://www.g2.com/survey_responses/deepseek-review-12827799)

**["Fast, Reliable AI with Deep Thinking and Instant/Expert Outputs"](https://www.g2.com/survey_responses/deepseek-review-12922276)**

**Rating:** 4.5/5.0 stars

_— Konjengbam M._

[Read full review](https://www.g2.com/survey_responses/deepseek-review-12922276)

## FAQs About Large Language Models (LLMs) Software

Generated using AI

Last updated: June 3, 2026

### Best Large Language Models avoiding vendor lock-in concerns about data portability and long-term independence

Based on G2 reviews, these products are commonly mentioned for flexible workflows and broad day-to-day adoption.

- [ChatGPT](https://www.g2.com/products/chatgpt) — broad drafting, coding, and research workflows.
- [Gemini](https://www.g2.com/products/google-gemini) — document analysis and workspace-based productivity.
- [Claude](https://www.g2.com/products/claude-2025-12-11) — long documents, coding, and structured writing.
- [Deepseek](https://www.g2.com/products/deepseek) — lower-cost reasoning and coding support.

### Large Language Models with flexible pricing that doesn't explode as token usage increases over time unexpectedly

According to verified users, pricing concerns usually show up as usage caps, paid-plan limits, or pressure to upgrade during heavier workloads. In recent G2 reviews, buyers most often describe value in terms of time saved on drafting, research, coding, summarization, and documentation rather than raw token economics. Reviewers mention that lower-cost or free-access options can be useful for everyday tasks, but they also note tradeoffs like weaker integrations, inconsistent depth, or the need to verify outputs. For teams expecting sustained usage, the most grounded takeaway from G2 reviews is to compare plan limits, workflow fit, and how often users hit caps during normal work rather than assuming one pricing model will stay efficient at scale.

### What are the best Large Language Models for small marketing teams automating customer messaging workflows

Based on G2 reviews, these products appear often in messaging, content, and workflow-related use cases.

- [ChatGPT](https://www.g2.com/products/chatgpt) — email drafting, messaging, and content ideas.
- [Claude](https://www.g2.com/products/claude-2025-12-11) — polished writing and customer communication support.
- [Gemini](https://www.g2.com/products/google-gemini) — Gmail-connected drafting and daily communication tasks.
- [Deepseek](https://www.g2.com/products/deepseek) — email drafting and content curation.

### What features matter most in llm software

According to verified users, the most valued features in llm software are fast response times, clear explanations, strong context handling, easy setup, and versatility across writing, research, coding, summarization, and analysis. Recent G2 reviews also point to workflow features such as file handling, chat history, memory, document summarization, image support, and the ability to refine outputs through follow-up questions. For workplace use, buyers repeatedly mention integrations with tools like email, documents, spreadsheets, project tools, and internal workflows as important. At the same time, reviewers consistently flag limits around accuracy, outdated information, context drift in long conversations, and plan or usage caps, so reliability and usability matter as much as feature breadth.

### How do teams use Large Language Models for documentation

G2 reviewers mention that teams use Large Language Models to speed up documentation work across reports, SOPs, technical documents, summaries, presentations, emails, and customer-facing materials. In recent reviews, users describe turning rough notes into structured drafts, summarizing long files, refining tone, and preparing repeatable documentation faster than manual workflows. Technical teams also mention using these tools for code explanations, report preparation, ticket refinement, and knowledge-base style outputs. The buyer takeaway is that documentation value comes from reducing first-draft time and organizing complex information quickly, but reviewers still recommend human review for specialized, business-critical, or rapidly changing content because answers can sometimes be too generic, inaccurate, or inconsistent.

### [Mistral AI](https://www.g2.com/products/mistral-ai/reviews)

Mistral AI is a French artificial intelligence company specializing in developing open-source large language models (LLMs) and AI solutions tailored for diverse applications. Founded in 2023, Mistral AI focuses on creating efficient, high-performance models that empower developers and enterprises to build intelligent applications across various domains. Key Features and Functionality: - Diverse Model Offerings: Mistral AI provides a range of models, including: - Mistral Large 2: A top-tier reasoning model designed for complex tasks, supporting multiple languages and a large context window of 128K tokens. - Codestral: A specialized model optimized for coding tasks, trained on over 80 programming languages, and featuring a 32K token context window. - Pixtral Large: A multimodal model capable of analyzing and understanding both text and images. - Developer Platform (La Plateforme): Offers APIs for accessing and customizing Mistral's models, enabling deployment in various environments such as on-premises or cloud. - Le Chat: A multilingual AI assistant available on mobile platforms, known for its speed and functionalities like web search, document understanding, and code assistance. Primary Value and Solutions: Mistral AI addresses the growing demand for customizable and efficient AI models by providing open-source solutions that offer greater flexibility and control to users. Their models are designed to be deployed across various platforms, ensuring privacy and adaptability to specific enterprise needs. By focusing on open and efficient AI models, Mistral AI empowers developers and businesses to integrate advanced AI capabilities into their applications, enhancing productivity and innovation.

**Average Rating:** 4.2/5.0

**Total Reviews:** 16

#### How Do G2 Users Rate Mistral AI?

- **Quality of Support:** 7.0/10 (Category avg: 7.5/10)
- **Content Moderation:** 9.2/10 (Category avg: 8.4/10)
- **Contextual Understanding:** 8.9/10 (Category avg: 8.7/10)
- **Bias Mitigation:** 5.0/10 (Category avg: 7.6/10)

#### Who Is the Company Behind Mistral AI?

- **Seller:** [Mistral](https://www.g2.com/sellers/mistral)
- **Year Founded:** 2023
- **HQ Location:** Paris, Île-de-France, France
- **Twitter:** @MistralAI (195,825 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/mistralai/ (1,114 employees on LinkedIn®)

#### Who Uses This Product?

- **Company Size:** 63% Small, 31% Medium

#### What Do G2 Reviewers Say About Mistral AI?

_AI-generated summary from verified user reviews_

##### Pros

- Users value the **free API access** of Mistral AI, which facilitates testing and comparison with other models.
- Users value the **knowledge access** of Mistral AI, enjoying its informative and efficient features with the free API.

##### Cons

- Users find a **lack of creativity** in Mistral AI, prompting them to seek alternative AI models for specific tasks.
- Users find Mistral AI's **limited capabilities** inadequate for specific tasks, often resorting to other models for better results.

#### What Are Recent G2 Reviews of Mistral AI?

**["Open, Developer-Friendly AI Ecosystem That Speeds Up Coding and Multi-Tasking"](https://www.g2.com/survey_responses/mistral-ai-review-13135922)**

**Rating:** 5.0/5.0 stars

_— Ashwath S._

[Read full review](https://www.g2.com/survey_responses/mistral-ai-review-13135922)

**["Codes on easy roads"](https://www.g2.com/survey_responses/mistral-ai-review-13109853)**

**Rating:** 5.0/5.0 stars

_— Fazila K._

[Read full review](https://www.g2.com/survey_responses/mistral-ai-review-13109853)

### [Grok](https://www.g2.com/products/xai-grok/reviews)

Grok is your truth-seeking AI companion for unfiltered answers with advanced capabilities in reasoning, coding, and visual processing.

**Average Rating:** 4.2/5.0

**Total Reviews:** 29

#### How Do G2 Users Rate Grok?

- **Quality of Support:** 7.6/10 (Category avg: 7.5/10)
- **Content Moderation:** 8.1/10 (Category avg: 8.4/10)
- **Contextual Understanding:** 8.3/10 (Category avg: 8.7/10)
- **Bias Mitigation:** 7.5/10 (Category avg: 7.6/10)

#### Who Is the Company Behind Grok?

- **Seller:** [xAI](https://www.g2.com/sellers/xai)
- **Year Founded:** 2022
- **HQ Location:** Asnières-sur-Seine, FR
- **LinkedIn® Page:** https://www.linkedin.com/company/generative-ai-chatgpt/ (3 employees on LinkedIn®)

#### Who Uses This Product?

- **Top Industries:** Computer Software
- **Company Size:** 66% Small, 28% Medium

#### What Do G2 Reviewers Say About Grok?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of Grok, allowing quick access to features with minimal training required.
- Users value the **rapid research capabilities** of Grok, enabling swift understanding of complex health and nutrition topics.
- Users value Grok for its **fast and powerful research capabilities** , enhancing their workflow and clarity in complex topics.
- Users value Grok for its **fast response time** , making research and content preparation efficient and effective.
- Users appreciate the **versatility** of Grok, enabling rapid content creation and diverse applications for their professional needs.

##### Cons

- Users report **low accuracy** in Grok's responses, leading to frustration and wasted time due to misinformation.
- Users frequently face **technical issues** with Grok, wasting hours on repeated questions and receiving broken links.
- Users note Grok's **limited context understanding** , often leading to inaccurate responses and insufficient depth for complex analyses.
- Users often experience **inaccurate responses** from Grok, leading to confusion and reliability issues in various tasks.
- Users report experiencing **hallucinations** from Grok, leading to confidence in false statements and unreliable information.

#### What Are Recent G2 Reviews of Grok?

**["Reliable, Accurate Responses That Save Time and Boost Efficiency"](https://www.g2.com/survey_responses/grok-review-12865049)**

**Rating:** 4.5/5.0 stars

_— Riya D._

[Read full review](https://www.g2.com/survey_responses/grok-review-12865049)

**["Grok Makes Brainstorming and Project Planning Fast and Straightforward"](https://www.g2.com/survey_responses/grok-review-13119924)**

**Rating:** 5.0/5.0 stars

_— Muzammil M._

[Read full review](https://www.g2.com/survey_responses/grok-review-13119924)

### [Llama](https://www.g2.com/products/llama/reviews)

Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model developed by Meta, designed to handle both text and image inputs while generating multilingual text and code outputs across 12 languages. Built on a mixture-of-experts (MoE) architecture with 128 experts, it activates 17 billion parameters per forward pass out of a total of 400 billion, ensuring efficient processing. Optimized for vision-language tasks, Maverick is instruction-tuned to exhibit assistant-like behavior, perform image reasoning, and facilitate general-purpose multimodal interactions. It features early fusion for native multimodality and supports a context window of up to 1 million tokens. Trained on approximately 22 trillion tokens from a curated mix of public, licensed, and Meta-platform data, with a knowledge cutoff in August 2024, Maverick was released on April 5, 2025, under the Llama 4 Community License. It is well-suited for research and commercial applications requiring advanced multimodal understanding and high model throughput. Key Features and Functionality: - Multimodal Input Support: Processes both text and image inputs, enabling comprehensive understanding and generation capabilities. - Multilingual Output: Generates text and code outputs in 12 languages, including Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. - Mixture-of-Experts Architecture: Utilizes 128 experts with 17 billion active parameters per forward pass, optimizing computational efficiency and performance. - Instruction-Tuned: Fine-tuned for assistant-like behavior, image reasoning, and general-purpose multimodal interactions, enhancing its applicability across various tasks. - Extended Context Window: Supports a context length of up to 1 million tokens, facilitating the processing of extensive and complex inputs. Primary Value and User Solutions: Llama 4 Maverick 17B Instruct addresses the growing demand for advanced AI models capable of understanding and generating content across multiple modalities and languages. Its multimodal and multilingual capabilities make it an invaluable tool for developers and researchers working on applications that require nuanced language understanding, image processing, and code generation. The model's instruction-tuned nature ensures it can perform a wide range of tasks with high accuracy, from serving as an intelligent assistant to executing complex reasoning tasks. Its efficient architecture and extended context window allow for the handling of large-scale data inputs, making it suitable for both research and commercial applications that demand high throughput and advanced multimodal understanding.

**Average Rating:** 4.3/5.0

**Total Reviews:** 151

#### How Do G2 Users Rate Llama?

- **Quality of Support:** 7.1/10 (Category avg: 7.5/10)
- **Content Moderation:** 7.6/10 (Category avg: 8.4/10)
- **Contextual Understanding:** 8.5/10 (Category avg: 8.7/10)
- **Bias Mitigation:** 7.8/10 (Category avg: 7.6/10)

#### Who Is the Company Behind Llama?

- **Seller:** [Meta](https://www.g2.com/sellers/meta-3e2ff094-c346-4bd2-a24c-d2001c194c6e)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

#### Who Uses This Product?

- **Who Uses This:** Software Engineer
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 58% Small, 24% Medium

#### What Do G2 Reviewers Say About Llama?

_AI-generated summary from verified user reviews_

##### Pros

- Users praise the **high accuracy** of Meta LLaMA 3, enhancing interactions with its advanced contextual understanding.
- Users find Llama 3 to be **exceptionally easy to use** , streamlining complex tasks with fast, accurate responses.
- Users commend the **speed and accuracy** of Meta Llama 3, finding it significantly improves response quality for tasks.
- Users value the **open-source nature** of Llama, enabling cost-effective hosting and fostering innovative AI tool development.
- Users appreciate the **helpfulness** of Llama, noting its quick responses and advanced contextual understanding for various applications.

##### Cons

- Users face **limitations in capabilities** , particularly in text recognition, table creation, and indexing support for specific use cases.
- Users find Llama's **slow performance** frustrating, especially compared to other AI models like OpenAI's offerings.
- Users report **poor response quality** from Llama, with issues like repetition and duplicative answers from prompts.
- Users report **inaccuracies** in responses from Llama, necessitating careful verification to ensure reliability.
- Users note a **limited understanding** in Llama 3, particularly during complex interactions and context retention.

#### What Are Recent G2 Reviews of Llama?

**["AI model that fits perfectly into our daily workflow for our telecare endusers"](https://www.g2.com/survey_responses/llama-review-11831972)**

**Rating:** 4.5/5.0 stars

_— Rui R._

[Read full review](https://www.g2.com/survey_responses/llama-review-11831972)

**["efficient AI model for business content and customer communication"](https://www.g2.com/survey_responses/llama-review-11831905)**

**Rating:** 4.5/5.0 stars

_— Miguel R._

[Read full review](https://www.g2.com/survey_responses/llama-review-11831905)

### [bloom](https://www.g2.com/products/hugging-face-bloom/reviews)

The BLOOM model has been proposed with its various versions through the BigScience Workshop. BigScience is inspired by other open science initiatives where researchers have pooled their time and resources to collectively achieve a higher impact. The architecture of BLOOM is essentially similar to GPT3 (auto-regressive model for next token prediction), but has been trained on 46 different languages and 13 programming languages. Several smaller versions of the models have been trained on the same dataset. BLOOM is available in the following versions:

**Average Rating:** 4.5/5.0

**Total Reviews:** 2

#### How Do G2 Users Rate bloom?

- **Quality of Support:** 5.8/10 (Category avg: 7.5/10)
- **Content Moderation:** 10.0/10 (Category avg: 8.4/10)
- **Contextual Understanding:** 10.0/10 (Category avg: 8.7/10)
- **Bias Mitigation:** 10.0/10 (Category avg: 7.6/10)

#### Who Is the Company Behind bloom?

- **Seller:** [Hugging Face](https://www.g2.com/sellers/hugging-face)
- **Year Founded:** 2016
- **HQ Location:** United States
- **Twitter:** @huggingface (708,886 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/huggingface/ (636 employees on LinkedIn®)

#### Who Uses This Product?

- **Company Size:** 50% Small, 50% Medium

#### What Are Recent G2 Reviews of bloom?

**["Bloom’s Intuitive, Adaptive Performance for Low-Latency API Calls"](https://www.g2.com/survey_responses/bloom-review-13137611)**

**Rating:** 4.0/5.0 stars

_— Prashant J._

[Read full review](https://www.g2.com/survey_responses/bloom-review-13137611)

**["Bloom Was a Lifesaver in College for Q&A, Translation, and Code"](https://www.g2.com/survey_responses/bloom-review-13135682)**

**Rating:** 5.0/5.0 stars

_— Saragena S._

[Read full review](https://www.g2.com/survey_responses/bloom-review-13135682)

### [Phi](https://www.g2.com/products/phi/reviews)

Phi-4 is a state-of-the-art language model developed by Microsoft Research, designed to deliver advanced reasoning capabilities within a compact architecture. With 14 billion parameters, this dense decoder-only Transformer model is optimized for text-based inputs, particularly excelling in chat-based prompts. Trained on a diverse dataset comprising 9.8 trillion tokens—including synthetic datasets, filtered public domain content, academic literature, and Q&A datasets—Phi-4 emphasizes high-quality data to enhance its reasoning abilities. The model underwent rigorous enhancement and alignment processes, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures. Released on December 12, 2024, under the MIT license, Phi-4 is tailored for applications requiring efficient performance in memory or compute-constrained environments, latency-sensitive scenarios, and tasks demanding advanced reasoning and logic. Key Features and Functionality: - Advanced Reasoning: Phi-4 is engineered to perform complex reasoning tasks, making it suitable for applications that require logical processing and decision-making. - Efficient Architecture: With 14 billion parameters, the model offers a balance between performance and resource utilization, catering to environments with memory and compute constraints. - Extensive Training Data: The model is trained on a vast dataset of 9.8 trillion tokens, including high-quality synthetic data, filtered public domain content, academic books, and Q&A datasets, ensuring a comprehensive understanding of diverse topics. - Optimized for Chat Prompts: Phi-4 excels in generating coherent and contextually relevant responses to chat-based inputs, enhancing user interaction experiences. - Safety and Alignment: The model incorporates supervised fine-tuning and direct preference optimization to adhere to instructions accurately and maintain robust safety measures. Primary Value and User Solutions: Phi-4 addresses the need for a powerful yet efficient language model capable of advanced reasoning in resource-constrained environments. Its optimized architecture and extensive training enable developers to integrate sophisticated AI capabilities into applications without compromising performance. By focusing on high-quality data and safety measures, Phi-4 ensures reliable and contextually appropriate responses, making it a valuable tool for enhancing user engagement and decision-making processes in various applications.

**Average Rating:** 4.0/5.0

**Total Reviews:** 1

#### How Do G2 Users Rate Phi?

- **Quality of Support:** 8.3/10 (Category avg: 7.5/10)
- **Contextual Understanding:** 8.3/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Phi?

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft (13,091,739 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (231,632 employees on LinkedIn®)
- **Ownership:** MSFT

#### Who Uses This Product?

- **Company Size:** 100% Large

#### What Do G2 Reviewers Say About Phi?

_AI-generated summary from verified user reviews_

##### Pros

- Users value the **easy integrations** with Microsoft Azure, enhancing efficiency and compatibility with various tools.
- Users find Phi to be highly **efficient** and cost-effective, outperforming many models of its size.

##### Cons

- Users find Phi's performance for complex tasks is limited compared to **larger models like GPT-4**.

#### What Are Recent G2 Reviews of Phi?

**["Phi 3-Model: Small, Powerful and cost-effective AI for modern applications"](https://www.g2.com/survey_responses/phi-review-10262654)**

**Rating:** 4.0/5.0 stars

_— Verified User in Information Technology and Services_

[Read full review](https://www.g2.com/survey_responses/phi-review-10262654)

### [Amazon Nova](https://www.g2.com/products/amazon-nova/reviews)

Amazon Nova is a suite of advanced foundation models developed by Amazon, designed to deliver state-of-the-art intelligence and industry-leading price performance. Integrated within Amazon Bedrock, these models support a wide range of tasks across multiple modalities, including text, image, and video processing. Amazon Nova aims to simplify the development of generative AI applications by offering versatile and cost-effective solutions for businesses and developers.

#### Who Is the Company Behind Amazon Nova?

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud (2,232,483 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (147,094 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMZN

### [Command](https://www.g2.com/products/command/reviews)

Command A is Cohere's most advanced large language model, specifically engineered to meet the complex demands of enterprise applications. With 111 billion parameters and a context length of 256,000 tokens, it excels in tasks such as tool use, retrieval-augmented generation , agent-based workflows, and multilingual processing across 23 languages. Designed for efficient deployment, Command A operates effectively on just two GPUs, making it a cost-effective solution for businesses seeking high-performance AI capabilities. Key Features and Functionality: - High Performance: Delivers top-tier results in enterprise tasks, including tool integration, RAG, and agentic operations. - Extended Context Length: Supports up to 256,000 tokens, enabling the processing of extensive documents and complex datasets. - Multilingual Support: Proficient in 23 languages, facilitating global business applications. - Efficient Deployment: Operates on minimal hardware—specifically, two A100 or H100 GPUs—reducing infrastructure costs. - Data Security: Designed for on-premise or Virtual Private Cloud deployment, ensuring sensitive data remains within the organization's control. Primary Value and User Solutions: Command A addresses the critical need for enterprises to integrate advanced AI into their operations without compromising on performance, scalability, or data security. By automating complex workflows, enhancing content generation, and supporting multilingual communication, it empowers organizations to boost productivity and maintain a competitive edge in the global market. Its efficient deployment requirements make it accessible to businesses seeking powerful AI solutions without significant hardware investments.

#### Who Is the Company Behind Command?

- **Seller:** [Cohere](https://www.g2.com/sellers/cohere-59b8d282-7088-4aee-90d5-f9f5facc7da2)
- **Year Founded:** 2019
- **HQ Location:** Toronto, Ontario, Canada
- **LinkedIn® Page:** https://www.linkedin.com/company/cohere-ai/ (818 employees on LinkedIn®)

### [Falcon](https://www.g2.com/products/synerise-falcon/reviews)

Cutting-edge AI-driven infrastructure tailored for collecting, analyzing, and interpreting behavioral data. By leveraging the power of AI and machine learning, we transform raw behavioral data into actionable intelligence, enabling organizations to make data-driven decisions with unprecedented accuracy and efficiency.

#### Who Is the Company Behind Falcon?

- **Seller:** [Synerise](https://www.g2.com/sellers/synerise)
- **Year Founded:** 2013
- **HQ Location:** San Francisco, California
- **Twitter:** @Synerise (4,971 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/synerise (195 employees on LinkedIn®)

### [GLM](https://www.g2.com/products/glm/reviews)

Zhipu AI is a Chinese artificial intelligence company specializing in the development of large language and multimodal models. Established in 2019 as a spinoff from Tsinghua University's Computer Science Department, Zhipu AI focuses on advancing cognitive intelligence through innovative AI technologies. Their flagship products include the GLM series of models, such as GLM-4 and ChatGLM, which are designed to perform a wide range of tasks, including text generation, image understanding, and programming assistance. These models are accessible via their open platform, supporting diverse AI applications across various industries. Zhipu AI's mission is to teach machines to think like humans, thereby empowering businesses and individuals with cutting-edge AI solutions.

#### Who Is the Company Behind GLM?

- **Seller:** [Zhipu AI](https://www.g2.com/sellers/zhipu-ai)
- **HQ Location:** Beijing, CN
- **LinkedIn® Page:** https://www.linkedin.com/company/zdotai/ (79 employees on LinkedIn®)

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 ![Bijou Barry](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Bijou Barry")
BB

Researched and written by [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)

Updated April 9, 2026

Large language models (LLMs) are advanced AI systems engineered to comprehend, interpret, and generate human-like text, leveraging transformer architectures and massive training datasets to perform tasks including translation, summarization, question answering, sentiment analysis, and content generation, and integrating into applications to automate language-heavy workflows.

### Core Capabilities of LLM Software

To qualify for inclusion in the Large Language Models (LLM) category, a product must:

- Offer a large-scale language model capable of comprehending and generating human-like text, made available for commercial use
- Provide a language model with a parameter size greater than 10 billion
- Provide robust and secure APIs or integration tools enabling businesses to incorporate the model into existing systems
- Have comprehensive mechanisms in place for data privacy, ethical use, and content moderation
- Deliver reliable customer support, extensive documentation, and consistent updates to ensure ongoing relevance

### Common Use Cases for LLM Software

Developers and enterprises use LLMs as a foundational layer to power a wide range of language-driven applications. Common use cases include:

- Powering conversational interfaces, customer support chatbots, and internal knowledge assistants
- Automating content generation, summarization, and translation at scale across business workflows
- Supporting reasoning-driven insights through advanced LLMs with multi-step logical reasoning capabilities

### How LLM Software Differs from Other Tools

LLMs are designed to be versatile and foundational, distinct from the [AI chatbots](https://www.g2.com/categories/ai-chatbots) category, which focuses on standalone platforms for end-user interaction with LLMs, and the [synthetic media](https://www.g2.com/categories/synthetic-media) category, which covers tools for creating AI-generated media. LLMs can be open-source (freely downloadable and modifiable) or closed-source/proprietary (available only via API). Some LLMs include reasoning capabilities for complex problem-solving, while base models focus on next-token prediction for faster, pattern-based responses.

### Insights from G2 on LLM Software

Based on category trends on G2, output quality and API integration flexibility stand out as the most valued capabilities. Accelerated language feature development and broad applicability across use cases stand out as primary drivers of adoption.

Show More

* * *

## How Do You Choose the Right Large Language Models (LLMs) Software?

### What You Should Know About Large Language Models (LLMs)

Large language models (LLMs) are machine learning models developed to understand and interact with human language at scale. These advanced [artificial intelligence (AI)](https://www.g2.com/articles/what-is-artificial-intelligence) systems are trained on vast amounts of text data to predict plausible language and maintain a natural flow.

### **What are large language models (LLMs)?**

LLMs are a type of [Generative AI](https://www.g2.com/categories/generative-ai)models that use [deep learning](https://www.g2.com/articles/deep-learning) and large text-based data sets to perform various [natural language processing (NLP)](https://www.g2.com/glossary/natural-language-processing-definition) tasks.

These models analyze probability distributions over word sequences, allowing them to predict the most likely next word within a sentence based on context. This capability fuels content creation, document summarization, language translation, and code generation. 

The term "large” refers to the number of parameters in the model, which are essentially the weights it learns during training to predict the next token in a sequence, or it can also refer to the size of the dataset used for training.

### **How do large language models (LLMs) work?**

LLMs are designed to understand the probability of a single token or sequence of tokens in a longer sequence. The model learns these probabilities by repeatedly analyzing examples of text and understanding which words and tokens are more likely to follow others. 

The training process for LLMs is multi-stage and involves [unsupervised learning](https://learn.g2.com/unsupervised-learning), self-supervised learning, and deep learning. A key component of this process is the self-attention mechanism, which helps LLMs understand the relationship between words and concepts. It assigns a weight or score to each token within the data to establish its relationship with other tokens.

Here’s a brief rundown of the whole process:

- A large amount of language data is fed to the LLM from various sources such as books, websites, code, and other forms of written text.
- The model comprehends the building blocks of language and identifies how words are used and sequenced through pattern recognition with unsupervised learning.
- Self-supervised learning is used to understand context and word relationships by predicting the following words.
- Deep learning with [neural networks](https://www.g2.com/glossary/artificial-neural-network-definition) learns language's overall meaning and structure, going beyond just predicting the next word.
- The self-attention mechanism refines the understanding by assigning a score to each token to establish its influence on other tokens. During training, scores (or weights) are learned, indicating the relevance of all tokens in the sequence to the current token being processed and giving more attention to relevant tokens during prediction.

### What are the common features of large language models (LLMs)?

LLMs are equipped with features such as text generation, summarization, and [sentiment analysis](https://www.g2.com/glossary/sentiment-analysis-definition) to complete a wide range of NLP tasks.

- **Human-like text generation** across various genres and formats, from business reports to technical emails to basic scripts tailored to specific instructions. 
- **Multilingual support** for translating comments, documentation, and user interfaces into multiple languages, facilitating global applications and seamless cross-lingual communication.
- **Understanding context** for accurately comprehending language nuances and providing appropriate responses during conversations and analyses.
- **Content summarization** recapitulates complex technical documents, research papers, or API references for easy understanding of key points.
- **Sentiment analysis** categorizes opinions expressed in text as positive, negative, or neutral, making them useful for social media monitoring, customer feedback analysis, and market research.  
- [Conversational AI](https://www.g2.com/glossary/conversational-ai-definition) **and** [chatbots](https://www.g2.com/categories/ai-chatbots) powered by LLM simulate human-like dialogue, understand user intent, answer user questions, or provide basic troubleshooting steps.
- **Code completion** analyzes an existing code to report typos and suggests completions. Some advanced LLMs can even generate entire functions based on the context. It increases development speed, boosts productivity, and tackles repetitive coding tasks.
- **Error identification** looks for grammatical errors or inconsistencies in writing and bugs or anomalies in code to help maintain high code and writing quality and reduce debugging time.
- **Adaptability** allows LLMs to be fine-tuned for specific applications and perform better in legal document analysis or technical support tasks.
- [Scalability](https://www.g2.com/glossary/scalability) processes vast amounts of information quickly and accommodates the needs of both small businesses and large enterprises.

### Who uses large language models (LLMs)?_ _

LLMs are becoming increasingly popular across various industries because they can process and generate text in creative ways. Below are some businesses that interact with LLMs more often.

- **Content creation and media companies** produce significant content, such as news articles, blogs, and marketing materials, by utilizing LLMs to automate and enhance their content creation processes.
- **Customer service providers** with large customer service operations, including call centers, online support, and chat services, power intelligent chatbots, and virtual assistants using LLMs to improve response times and customer satisfaction.
- **E-commerce and retail**  **platforms** use LLMs to generate product descriptions and offer personalized shopping experiences and customer service interactions, enhancing the overall shopping experience.
- [Financial services providers](https://www.g2.com/categories/business-finance) like banks, investment firms, and insurance companies benefit from LLMs by automating report generation, providing customer support, and personalizing financial advice, thus improving efficiency and customer engagement.
- **Education and e-learning platforms** offering educational content and tutoring services use LLMs to create personalized learning experiences, automate grading, and provide instant feedback to students.
- **Healthcare providers** use LLMs for patient support, medical documentation, and research, LLMs can analyze and interpret medical texts, support diagnosis processes, and offer personalized patient advice.
- **Technology and** [software development companies](https://www.g2.com/categories/software-developer-services) can use LLMs to generate documentation, provide coding assistance, and automate customer support, especially for troubleshooting and handling technical queries.

### Types of large language models (LLMs)

Language models can basically be classified into two main categories — statistical models and language models designed on deep neural networks.

#### Statistical language models

These probabilistic models use statistical techniques to predict the likelihood of a word or sequence of words appearing in a given context. They analyze large corpora of text to learn the patterns of language. 

N-gram models and hidden Markov models (HMMs) are two examples. 

N-gram models analyze sequences of words (n-grams) to predict the probability of the next word appearing. The probability of a word's occurrence is estimated based on the occurrence of the words preceding it within a fixed window of size 'n.' 

For example, consider the sentence, "The cat sat on the mat." In a trigram (3-gram) model, the probability of the word "mat" occurring after the sequence "sat on the" is calculated based on the frequency of this sequence in the training data.

#### Neural language models

Neural language models utilize neural networks to understand language patterns and word relationships to generate text. They surpass traditional statistical models in detecting complex relationships and dependencies within text. 

[Transformer models](https://www.g2.com/articles/transformer-models) like GPT use self-attention mechanisms to assess the significance of each word in a sentence, predicting the following word based on contextual dependencies. For example, if we consider the phrase "The cat sat on the," the transformer model might predict "mat" as the next word based on the context provided. 

Among large language models, there are also two primary types — open-domain models and domain-specific models.

- **Open-domain models** are designed to perform various tasks without needing customization, making them useful for brainstorming, idea generation, and writing assistance. Examples of open-domain models include [generative pre-trained transformer (GPT)](https://www.g2.com/glossary/gpt-3-definition)and bidirectional encoder representations from transformers (BERT). 
- **Domain-specific models:** Domain-specific models are customized for specific fields, offering precise and accurate outputs. These models are particularly useful in medicine, law, and scientific research, where expertise is crucial. They are trained or fine-tuned on datasets relevant to the domain in question. Examples of domain-specific LLMs include BioBERT (for biomedical texts) and FinBERT (for financial texts).

### Benefits of large language models (LLMs)

LLMs come with a suite of benefits that can transform countless aspects of how businesses and individuals work. Listed below are some common advantages.

- **Increased productivity:** LLMs simplify workflows and accelerate project completion by automating repetitive tasks.
- **Improved accuracy:** Minimizing inaccuracies is crucial in financial analysis, legal document review, and research domains. LLMs enhance work quality by reducing errors in tasks like data entry and analysis.
- **Cost-effectiveness:** LLMs reduce resource requirements, leading to substantial cost savings for businesses of all sizes.
- **Accelerated development cycles:** The process from code generation and debugging to research and documentation gets faster for software development tasks, leading to quicker product launches.
- **Enhanced customer engagement:** LLM-powered chatbots like [ChatGPT](https://www.g2.com/articles/chatgpt) enable swift responses to customer inquiries, round-the-clock support, and personalized marketing, creating a more immersive brand interaction.
- **Advanced research capabilities:** With LLMs capable of summarizing complex data and sourcing relevant information, research processes become simplified.
- **Data-driven insights:** Trained to analyze large datasets, LLMs can extract trends and insights that support data-driven decision-making.

### Applications of large language models

LLMs are used in various domains to solve complex problems, reduce the amount of manual work, and open up new possibilities for businesses and people.

- **Keyword research:** Analyzing vast amounts of search data helps identify trends and recommend keywords to optimize content for search engines.
- **Market research:** Processing user feedback, social media conversations, and market reports uncover insights into consumer behavior, sentiment, and emerging market trends.
- [Content creation](https://learn.g2.com/content-creation) **:** Generating written content such as articles, product descriptions, and social media posts, saves time and resources while maintaining a consistent voice.
- [Malware analysis](https://www.g2.com/glossary/malware-analysis-definition) **:** Identifying potential malware signatures, suggesting preventive measures by analyzing patterns and code, and generating reports help assist cybersecurity professionals.
- **Translation:** Enabling more accurate and natural-sounding translations, LLMs provide multilingual context-aware translation services.
- **Code development:** Writing and reviewing code, suggesting syntax corrections, auto-completing code blocks, and generating code snippets within a given context.
- **Sentiment analysis:** Analyzing text data to understand the emotional tone and sentiment behind words.
- **Customer support:** Engaging with users, answering questions, providing recommendations, and automating customer support tasks, enhance the customer experience with quick responses and 24/7 support.

### How much does LLM software cost?

The cost of an LLM depends on multiple factors, like type of license, word usage, token usage, and API call consumptions. The top contenders of LLMs are GPT-4, GPT-Turbo, Llama 3.1, Gemini, and Claude, which offer different payment plans like subscription-based billing for small, mid, and enterprise businesses, tiered billing based on features, tokens, and API integrations and pay-per-use based on actual usage and model capacity and enterprise custom pricing for larger organizations. 

Mostly, LLM software is priced according to the number of tokens consumed and words processed by the model. For example, GPT-4 by OpenAI charges $0.03 per 1000 input tokens and $0.06 for output. Llama 3.1 and Gemini are open-source LLMs that charge between $0.05 to $0.10 per 1000 input tokens and an average of 100 API calls. While the pricing portfolio for every LLM software varies depending on your business type, version, and input data quality, it has become evidently more affordable and budget-friendly with no compromise to processing quality.

### Limitations of large language model (LLM) software

While LLMs have boundless benefits, inattentive usage can also lead to grave consequences. Below are the limitations of LLMs that teams should steer clear of:

- **Plagiarism:** Copying and pasting text from the LLM platform directly on your blog or other marketing media will raise a case of plagiarism. As the data processed by the LLM is mostly internet-scraped, the chances of content duplication and replication become significantly higher. 
- **Content bias:** LLM platforms can alter or change the cause of events, narratives, incidents, statistics, and numbers, as well as inflate data that can be highly misleading and dangerous. Because of limited training abilities, these platforms have a strong chance of generating factually incorrect content that offends people.
- **Hallucination:** LLMs even hallucinate and don't correctly register the user's input prompt. Though they might have gotten similar prompts before and know how to answer, they reply in a hallucinated state and don't give you access to data. Writing a follow-up prompt can get LLMs out of this stage and functional again. 
- **Cybersecurity and data privacy:** LLMs transfer critical, company-sensitive data to public cloud storage systems that make your data more prone to data breaches, vulnerabilities, and zero-day attacks. 
- **Skills gap** : Deploying and maintaining LLMs requires specialized knowledge, and there may be a skills gap in current teams that needs to be addressed through hiring or training.

### How to choose the best large language model (LLM) for your business?

Selecting the right LLM software can impact the success of your projects. To choose the model that suits your needs best, consider the following criteria:

- **Use case** : Each model has strengths, whether generating content, providing coding assistance, creating chatbots for customer support, or analyzing data. Determine the primary task the LLM will perform and look for models that excel in that specific use case.
- **Model size and capacity** : Consider the model's size, which often correlates with capacity and processing needs. Larger models can perform various tasks but require more computational resources. Smaller models may be more cost-effective and sufficient for less complex tasks.
- **Accuracy** : Evaluate the LLM's accuracy by reviewing benchmarks or conducting tests. Accuracy is critical — an error-prone model could negatively impact user experience and work efficiency.
- **Performance:** Assess the model's speed and responsiveness, especially if real-time processing is required.
- **Training data and pre-training** : Determine the breadth and diversity of the training data. Models pre-trained on extensive, varied datasets tend to work better across inputs. However, models trained on niche datasets may perform better for specialized applications.
- **Customization** : If your application has unique needs, consider whether the LLM allows for customization or fine-tuning with your data to better tailor its outputs.
- **Cost** : Factor in the total cost of ownership, including initial licensing fees, computational costs for training and inference, and any ongoing fees for updates or maintenance.
- [Data security](https://www.g2.com/glossary/data-security-definition): Look for models that offer security features and compliance with data protection laws relevant to your region or industry.
- **Availability and licensing** : Some models are open-source, while others may require a commercial license. Licensing terms can dictate the scope of use, such as whether it's available for commercial applications or has any usage limits.

It's worthwhile to test multiple models in a controlled environment to directly compare how they meet your specific criteria before making a final decision.

### LLM implementation

The implementation of an LLM is a continuous process. Regular assessments, upgrades, and re-training are necessary to ensure the technology meets its intended objectives. Here's how to approach the implementation process:

- **Define objectives and scope** : Clearly define your project goals and success metrics from the outset to specify what you wish to achieve using an LLM. Identify areas where automation or cognitive enhancements can add value.
- **Data privacy and compliance** : Choose an LLM with solid security measures that comply with data protection regulations relevant to your industry, such as GDPR. Establish data handling procedures that preserve user privacy.
- **Model selection** : Evaluate whether a general-purpose model like GPT-3 better suits your needs or if a domain-specific model would provide more precise functionality. 
- **Integration and infrastructure** : Determine whether you will use the LLM as a cloud service or host it on-premises, considering the computational and memory requirements, potential scalability needs, and latency sensitivities. Account for the API endpoints, SDKs, or libraries you'll need.
- **Training and fine-tuning** : Allocate resources for training and validation and tune the model through continuous learning from new data.
- **Content moderation and quality control** : Implement systems to oversee the LLM-generated content to ensure that the outputs align with your organizational standards and suit your audience.
- **Continuous evaluation and improvement** : Build an evaluation framework to regularly assess your LLM's performance against your objectives. Capture user feedback, monitor performance metrics, and be ready to re-train or update your model to adapt to evolving data patterns or business needs.

### Software and services related to large language models (LLMs)

Below are some related software and services that can be used with or without large language model software to accomplish daily tasks. 

- [AI writing assistants](https://www.g2.com/categories/ai-writing-assistant) or AI text generators are specifically designed LLMs that generate text sequences of any size based on an input prompt. These tools can create summaries, write essays, reports, language-specific case studies, etc. 
- [AI code generators](https://www.g2.com/categories/ai-code-generation) can create, compile, modify, and debug code snippets for software engineers and developers. These platforms save teams from the hassle of researching the web or studying object-oriented programming concepts.
- [AI chatbot platforms](https://www.g2.com/categories/ai-chatbots) help design conversational interfaces that integrate with website chatbots or in-app chatbots to provide personalized assistance to consumers.
- [Synthetic media](https://www.g2.com/categories/synthetic-media) tools are powered by AI and deploy images, videos, voice data, or numeric data to build various media types. Sales and marketing teams use them to create podcasts, video trailers, and content-focused media.

### Alternatives to LLM software

There are several other alternatives to explore in place of a large language model software that can be tailored to specific departmental workflows. 

- [Natural language understanding (NLU) tools](https://www.g2.com/categories/natural-language-understanding-nlu) facilitate computer comprehension of human language. NLU enables machines to understand, interpret, and derive meaning from human language. It involves text understanding, semantic analysis, entity recognition, sentiment analysis, and more. NLU is crucial for various applications, such as virtual assistants, chatbots, sentiment analysis tools, and information retrieval systems.
- [Natural language generation (NLG) tools](https://www.g2.com/categories/natural-language-generation-nlg) convert structured information into coherent human language text. It is used in language translation, summarization, report generation, conversational agents, and content creation.

### LLM trends

The large language model space is constantly evolving, and what's current now could change in the near future as new research and developments occur. Here are some trends that are currently ruling the LLM domain.

- **Increasing personalization:** LLMs' ability to understand and generate human-like text drives the growing use of personalized content, recommendations, and interactions in customer services, marketing, education, and e-commerce.
- **Ethical AI and bias mitigation** : There's a strong focus on developing methods to reduce biases in LLMs and ensure their use aligns with ethical guidelines, reflecting a broader trend towards responsible AI.
- **Multimodal models** : A significant trend is the integration of LLMs with other types of AI models, such as those capable of processing images, audio, and video. This leads to more comprehensive AI systems capable of understanding and generating content across different formats.
- **Sustainable and cost-effective LLMs** : Efforts to make LLMs more energy-efficient and cost-effective are ongoing. New models are being designed to reduce the environmental impact and computational resources required for training and inference.

_Researched and written by_ [_Matthew Miller_](https://learn.g2.com/author/matthew-miller)

_Reviewed and edited by_ [_Sinchana Mistry_](https://learn.g2.com/author/sinchana-mistry)