Large Language Models (LLMs) Software Resources
Articles, Glossary Terms, Discussions, and Reports to expand your knowledge on Large Language Models (LLMs) Software
Resource pages are designed to give you a cross-section of information we have on specific categories. You'll find articles from our experts, feature definitions, discussions from users like you, and reports from industry data.
Large Language Models (LLMs) Software Articles
What Is Machine Translation? How It Works and Applications
Large Language Models (LLMs) Software Glossary Terms
Large Language Models (LLMs) Software Discussions
A few hours ago, OpenAI released GPT-4.5, currently only for developers and users on the PRO plan.
"GPT-4.5 does not include reasoning, as it was designed to be a more general-purpose, innately smarter model."
https://help.openai.com/en/articles/10658365-gpt-4-5-in-chatgpt
Has anyone started testing it yet? What do you think?
(One of my considerations about the topic, is the cost)
MODEL | Input Cost (per 1M tokens) | Output Cost (per 1M tokens) | Context Window
GPT-4.5 = $75.00 | $150.00 | 128k tokens
GPT-4.o = $2.50 | $10.00 | 128k tokens
Claude 3.7 Sonnet = $3.00 | $15.00 | 200k tokens
I’m helping a few engineering teams experiment with LLMs for generating code, reviewing pull requests, and catching issues earlier in the dev cycle. To see what models teams trust most, I reviewed G2 data for the Large Language Models category focused on developer use cases. Here’s what comes up.
Top LLM tools (by G2 Score)- Gemini: Best for teams that want code generation with strong reasoning across large codebases.
- Meta Llama 3: Best for teams that want open models they can fine-tune for internal dev workflows.
- BERT: Best for teams that want code understanding and classification tasks.
- GPT-4: Best for teams that want high-quality code generation and detailed code reviews.
- GPT-3: Best for teams that want fast code suggestions and boilerplate generation.
- Megatron-LM: Best for teams that want large-scale custom models trained on internal code.
Anyone relying on LLMs for code reviews today? I also see IDE-native tools mentioned alongside these models. Any other tool to include? What’s been your experience?
Do you let LLMs write production code—or only assist?
Hi,
I’m working with support and CX teams that serve customers across regions and languages, and real-time translation plus response quality is a constant challenge. So I checked G2 rankings in the Large Language Models category to see which LLMs teams lean on for multilingual support use cases. Here’s what stands out.
Top LLM tools (by G2 Score)- Gemini: Best for teams that want real-time multilingual understanding and response generation.
- Meta Llama 3: Best for teams that want language flexibility with control over localization.
- BERT: Best for teams that want language understanding and intent detection across markets.
- GPT-4: Best for teams that want high-quality multilingual responses in live support scenarios.
- GPT-3: Best for teams that want scalable multilingual chat and support automation.
- Megatron-LM: Best for teams that want custom multilingual models trained on proprietary data.
Anyone using LLMs for multilingual support today? I also see translation APIs paired with LLMs a lot. Any other tool to include? What’s been your experience?
What’s harder in multilingual support—translation accuracy or cultural context?


