--- title: Codestral Reviews meta_title: 'Codestral Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 15 reviews by the users' company size, role or industry to find out how Codestral works for a business like yours. aggregate_rating: rating_value: 4.4 review_count: 15 scale: '5' date_modified: '2026-08-14' parent_category: name: Generative AI url: https://www.g2.com/categories/generative-ai --- # Codestral Reviews **Vendor:** Mistral **Category:** [AI Coding Assistants Software](https://www.g2.com/categories/ai-coding-assistants) **Average Rating:** 4.4/5.0 **Total Reviews:** 15 ## About Codestral Codestral is an open-weight generative AI model developed by Mistral AI, specifically designed for code generation tasks. It assists developers in writing and interacting with code through a unified instruction and completion API endpoint. Proficient in over 80 programming languages—including Python, Java, C, C++, JavaScript, and Bash—Codestral also supports less common languages like Swift and Fortran, making it versatile across various coding environments. Key Features and Functionality: - Multi-Language Support: Trained on a diverse dataset encompassing more than 80 programming languages, ensuring adaptability to different development projects. - Code Completion and Generation: Capable of completing coding functions, writing tests, and filling in partial code using a fill-in-the-middle mechanism, thereby streamlining the coding process. - Integration with Development Environments: Accessible via a dedicated endpoint (`codestral.mistral.ai`), facilitating seamless integration into various Integrated Development Environments (IDEs). Primary Value and User Solutions: Codestral significantly enhances developer productivity by automating routine coding tasks, reducing the time and effort required for code completion and test generation. Its extensive language support and advanced code understanding minimize errors and bugs, allowing developers to focus on complex problem-solving and innovation. By integrating smoothly into existing workflows, Codestral democratizes coding, making advanced AI-assisted development accessible to a broader range of users. ## Codestral Reviews ### 1. Fast, Accurate Code Generation That Boosts Developer Productivity **Rating:** 4.5/5.0 stars **Reviewed by:** Subhashree S. | Developer, Enterprise (> 1000 emp.) **Reviewed Date:** August 06, 2026 **What do you like best about Codestral?** Codestral stands out for its fast, accurate code generation and low-latency completions, making it feel responsive during development. It performs well across multiple programming languages and is especially useful for code completion, debugging, refactoring, test generation, and explaining unfamiliar code. Its support for fill-in-the-middle (FIM) completion makes editing existing code much more natural, and the large context window helps it understand bigger codebases without losing context. Overall, it boosts developer productivity while reducing repetitive coding tasks **What do you dislike about Codestral?** While Codestral is fast and capable, it can occasionally generate incorrect or overly confident code for complex business logic, so manual review and testing are still necessary. Its understanding of highly project-specific architectures can be limited without sufficient context, and very large repositories may still require breaking tasks into smaller chunks. Additionally, some advanced features depend on the development environment or IDE integration, and generated code may sometimes need refinement to match coding standards and best practices. **What problems is Codestral solving and how is that benefiting you?** Codestral helps reduce the time spent on repetitive development tasks such as writing boilerplate code, generating unit tests, explaining existing code, and suggesting fixes for common bugs. It also speeds up onboarding to unfamiliar codebases by providing contextual code explanations and relevant completions. As a result, development cycles are shorter, productivity improves, and I can spend more time focusing on application design, business logic, and code quality instead of routine coding tasks. ### 2. Fast, High-Quality Code Generation That Boosts Developer Productivity **Rating:** 4.5/5.0 stars **Reviewed by:** Ravindra N. | SDET - 2, Oil & Energy, Enterprise (> 1000 emp.) **Reviewed Date:** August 05, 2026 **What do you like best about Codestral?** What I like most about Codestral is its strong code generation capabilities combined with fast inference and excellent support for multiple programming languages. It understands developer intent well and produces clean, readable code for a wide range of software engineering tasks. High-quality code generation across multiple programming languages. Fast response times that keep development workflows efficient. Helpful assistance with debugging, refactoring, and code explanations. Strong context awareness for implementing features and solving coding problems. Effective at generating boilerplate code and accelerating prototyping. For me, the most valuable feature is the balance between speed and code quality. It provides accurate, context-aware suggestions quickly, making it easy to stay productive without interrupting the development flow. The biggest benefit is increased developer productivity. Codestral reduces the time spent on repetitive coding, helps resolve issues faster, and enables quicker feature delivery while maintaining high-quality code. **What do you dislike about Codestral?** The biggest drawback is the need to validate generated code for edge cases and business logic. While the suggestions are generally accurate and save time, I still review them to ensure they meet the project's requirements, coding standards, and security expectations. The quality of responses depends heavily on the context and clarity of the prompt. Support for very large codebases and long-context reasoning could be stronger. **What problems is Codestral solving and how is that benefiting you?** Codestral solves the challenge of speeding up software development by assisting with code generation, debugging, refactoring, and code comprehension. Instead of spending time on repetitive coding tasks or searching for implementation examples, it provides context-aware suggestions that help developers work more efficiently. Generates code for new features and repetitive programming tasks. Helps debug issues by identifying potential errors and suggesting fixes. Explains unfamiliar code, making large codebases easier to understand. Assists with refactoring to improve code quality and maintainability. Accelerates prototyping and experimentation with new ideas. In my workflow, Codestral helps me move from concept to implementation much faster. I use it to generate boilerplate code, troubleshoot bugs, and explore different implementation approaches, which allows me to focus more on application architecture and business logic. The biggest benefit is higher developer productivity and faster feature delivery. Codestral reduces repetitive work, shortens development cycles, and helps produce cleaner, more maintainable code while keeping developers in control of the final implementation. ### 3. Codestral Delivers Clean, Context-Aware Code Generation That Speeds Development **Rating:** 4.5/5.0 stars **Reviewed by:** LOKESH G. | Engineer.SGB TCS-FS CORE BANKING,Production, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** August 04, 2026 **What do you like best about Codestral?** Codestral excels at code generation and code completion, and it understands programming context across multiple languages. It consistently produces clean, readable code, helps speed up development, and fits smoothly into existing coding workflows. Overall, it’s a valuable tool for prototyping and debugging, while also supporting stronger developer productivity. **What do you dislike about Codestral?** Code quality can sometimes be inconsistent on complex or highly specialized tasks, and the generated code still needs thorough review and testing. Handling longer contexts and intricate, multi-file projects can be challenging as well, and some advanced enterprise features may be more limited compared with certain competing coding-focused models. **What problems is Codestral solving and how is that benefiting you?** Codestral helps automate code generation, code completion, debugging, and code explanation, which reduces repetitive development work and speeds up software delivery. It boosts my productivity by helping me write boilerplate code, spot bugs, generate tests, and make sense of unfamiliar codebases, so I can spend more time on higher-value development tasks. ### 4. Exceptional FIM Performance and a Massive 32k Context Window **Rating:** 4.5/5.0 stars **Reviewed by:** Ishan T. | PROJECT MANAGEMENT COUNSULTANT, Consulting, Mid-Market (51-1000 emp.) **Reviewed Date:** July 17, 2026 **What do you like best about Codestral?** What stands out best is Codestral’s exceptional Fill-in-the-Middle (FIM) performance and its massive 32k context window. Natively optimized to predict and insert missing code right inside existing files, it operates with incredibly low latency. Its ability to ingest large portions of a codebase all at once means it doesn't lose track of multi-file structures, making it a highly reliable co-pilot for complex repository refactoring. **What do you dislike about Codestral?** What stands out worst is Codestral’s heavy hardware requirement for local deployment. While it is open-weight, running the model locally with its full 32k context window active demands significant GPU memory and processing power. Additionally, its specific commercial restrictions require paid licensing for enterprise product deployment, making it less accessible for startups wanting to embed it into proprietary software. **What problems is Codestral solving and how is that benefiting you?** Codestral solves developer context fatigue, multi-language context switching, and slow code-completion latency. It eliminates friction by natively understanding massive multi-file codebases all at once. This benefits software teams by accelerating daily coding velocity, automating repetitive boilerplate logic, and providing instant, accurate debugging across diverse, specialized programming ecosystems. ### 5. Highly Efficient, Ultra-Fast Coding Model That Punches Above Its Weight **Rating:** 5.0/5.0 stars **Reviewed by:** Nirmal K. | Manager, E-Learning, Small-Business (50 or fewer emp.) **Reviewed Date:** August 13, 2026 **What do you like best about Codestral?** At just 22 billion parameters, it is a highly efficient model. It offers extremely fast inference speeds and low latency (critical for IDE autocomplete) while routinely beating much larger general-purpose models on coding benchmarks like HumanEval and MBPP. **What do you dislike about Codestral?** Despite its massive context window, deep technical benchmarks reveal that it can occasionally struggle with multi-file coordination. When asked to scaffold or refactor complex logic spanning three or more interconnected files, it sometimes loses context or misses import consistencies compared to flagship frontier models. **What problems is Codestral solving and how is that benefiting you?** The latest iterations (such as Codestral 25.01) feature an enormous 256K-token context window. This allows developers to feed the model entire repositories, extensive API documentation, or massive server logs in a single prompt for highly contextual refactoring. ### 6. Codestral in VS Code: Accurate, Seamless Code Generation and Refactoring **Rating:** 4.0/5.0 stars **Reviewed by:** Balasubramani M. | Software engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** August 04, 2026 **What do you like best about Codestral?** The speed and accuracy of inline code completions in my IDE stand out significantly. It excels at generating complex function implementations, standard boilerplate, and refactoring existing routines across languages. Having intelligent code completion directly in the editor stream reduces context switching and speeds up routine coding tasks. **What do you dislike about Codestral?** While inline completions are fast, handling large multi-file refactoring or very repository-specific architectural constraints can sometimes require extra manual prompt adjustment. Occasionally, suggestions for niche or less common syntax require brief double-checking before committing. **What problems is Codestral solving and how is that benefiting you?** I use Codestral primarily for full-stack web development projects to streamline API route setups, component boilerplate creation, and database query formatting. It significantly reduces development time on repetitive code, allowing me to focus more on core application logic and system design. ### 7. Fast, Accurate Code Completions with Low-Latency IDE Integration **Rating:** 4.5/5.0 stars **Reviewed by:** Muhammed A. | Technical Project Manager , Information Technology and Services, Small-Business (50 or fewer emp.) **Reviewed Date:** August 01, 2026 **What do you like best about Codestral?** Codestral generates fast, accurate code completions and snippets across a wide range of languages, making it feel responsive during real-time coding sessions. It integrates well with IDEs and existing dev tools, and its low latency makes it practical for inline suggestions rather than just batch code generation. **What do you dislike about Codestral?** It's less capable than the larger Devstral models on complex, multi-file agentic tasks, so it works best for focused completions rather than full repo-level changes. Documentation on fine-tuning for specific languages could be more detailed. **What problems is Codestral solving and how is that benefiting you?** It speeds up day-to-day coding by handling repetitive boilerplate and suggesting completions in context, reducing the time developers spend on routine code. This has improved overall coding speed without needing a heavier, more expensive model for simple tasks. ### 8. Fast, Accurate Fill-in-the-Middle Completions That Fit How You Actually Code **Rating:** 4.0/5.0 stars **Reviewed by:** Luca P. | Chief Operations Officer DEQUA Studio | Formerly CTO in MarTech, Marketing and Advertising, Mid-Market (51-1000 emp.) **Reviewed Date:** July 23, 2026 **What do you like best about Codestral?** The fill-in-the-middle completion is the reason Codestral runs behind my editor all day. Most models are trained to continue code from the end of a file, which is not how anyone actually writes software. I put my cursor in the middle of a function, start typing, and Codestral predicts what belongs there using both the code above and the code below the cursor. That distinction sounds academic until you use it for a week. Completions land inside existing structures instead of trailing off past them, closing brackets stay balanced, and the suggestion respects the return type declared three lines down. General purpose models fake this with prompt gymnastics. Codestral was trained for it, and it shows in the acceptance rate of what it proposes. Latency is the other half of that story, and for autocomplete it matters more than raw intelligence. A suggestion that arrives half a second after I have already typed the next token is a suggestion I never see. Codestral starts streaming fast enough that the ghost text is usually there before my fingers catch up, and the 25.01 update made the whole loop noticeably quicker again through a reworked tokenizer and architecture. Completions now generate at roughly twice the speed of the original release. In an autocomplete workflow that speed compounds hundreds of times a day, and it is the difference between a tool I tolerate and a tool I forget is there. The dedicated endpoint deserves a specific mention because it solved an annoyance I did not expect a model vendor to think about. Codestral ships with its own endpoint, separate from the main API, with keys managed at the personal level rather than under the organization's rate limits. My editor plugin hammers that endpoint with small, frequent requests all day, and none of it competes with the batch jobs and application traffic running through our main account. Whoever designed that split has clearly watched an IDE integration eat an org quota before. The context window jump in 25.01 changed what I can point the model at. The original shipped with 32k tokens, which covered a file and its imports and not much more. The current version takes 256k, so I can hand it a service plus the modules it touches and get completions that reference symbols defined outside the open file. I still would not feed it an entire monorepo, but the chunking strategies I used to maintain for context assembly went in the bin. Language coverage is broad enough that I stopped checking. Python, TypeScript, Java, C++, Bash, SQL, all solid, and it holds up on the less fashionable corners too. I have had usable completions in old Bash provisioning scripts and in a Fortran routine a client swore nobody would ever touch again. Somewhere north of eighty languages are in the training set, and the practical effect is one model across a polyglot codebase instead of a different assistant per stack. A few integration points I lean on regularly: - Continue.dev in VS Code, where Codestral is a first class provider and the FIM wiring works without custom configuration - The OpenAI compatible endpoint, which lets me drop it into editors and internal scripts that were built against that API shape - LangChain, where the instruct variant handles tool use well enough that I run it inside a small self correcting code generation chain for internal tooling Self hosting is where Codestral separates itself from the closed alternatives. The weights are open and the model is a dense 22B, which means predictable memory requirements and predictable throughput. It fits on a single high memory GPU without the deployment gymnastics that mixture of experts models demand. For one client engagement with a strict rule about code leaving their network, we stood up a self hosted instance and the editor experience was functionally identical to the hosted API. The relicensing of the current generation under Apache 2.0 removed the last real obstacle here, since the earlier non production license kept the original weights out of anything commercial. The dual API surface is a design decision I have come to appreciate. The same model answers on an instruct route for conversational work and a raw completion route for FIM, so one deployment covers both the chat panel and the inline ghost text. Code correction lives on the instruct side and it is better than I expected for a model this size. I paste a stack trace with the offending function, and the fix it proposes is usually the actual fix rather than a rewrite of everything in sight. It stays surgical, which is precisely what I want from a correction tool. Test generation is quietly one of the better features. Point the instruct endpoint at a function and ask for tests, and it produces cases that cover the obvious paths plus usually one edge I had not bothered with. Not every suggestion survives review. Enough do that writing tests stopped being the task I deferred to Friday. The economics close the argument. Per token pricing sits well below the frontier models, and for the volume an autocomplete integration generates, that gap is not a rounding error. The free personal tier on the dedicated endpoint is genuinely usable for evaluation rather than a crippled demo. **What do you dislike about Codestral?** The honest framing is that Codestral is a completion engine, not a coding agent, and it punishes you for forgetting that. Ask it to autocomplete a function and it is excellent. Ask it to execute a refactor that spans four files and a database migration and it produces something plausible looking that falls apart at the seams between files. The frontier models are simply better at long horizon, multi file reasoning, and the benchmark gap on agentic coding tasks reflects what I see in practice. My working arrangement is a two tier setup: Codestral handles the inline completions and the quick single file generation, and a heavier model gets the architectural work. That arrangement works well, but it means Codestral alone is not a complete answer, and anyone evaluating it as their only coding model should know that going in. The licensing history is a trap for the inattentive. The original 2024 weights shipped under Mistral's non production license, which prohibits commercial use without a separate agreement, while the current generation moved to Apache 2.0. Both sets of weights exist in the wild, and if your team pulled the model before the relicense, you may be running weights you cannot legally ship a product on. We audited our own deployment for exactly this reason and found one internal tool still pointed at the old checkpoint. The fix took ten minutes. Discovering the problem is the part I would not trust every team to do. Setup friction is real if you go beyond the blessed integrations. Continue.dev works out of the box, but wiring Codestral into a less common editor meant reading up on the FIM token format, getting prefix and suffix delimiters right, and debugging why completions came back with the surrounding code echoed into them. None of it is hard once you know the shape of the problem. It is the kind of hour that a more packaged product would have absorbed for me, and it is the tax you pay for using an engine rather than a finished assistant. The 256k context, generous as it is, still runs out on genuinely large repositories. For whole codebase questions on a monorepo I fall back to a retrieval step that selects the relevant modules before anything reaches the model, which is exactly the plumbing the bigger window was supposed to retire. It retired it for medium projects. For the largest ones the assembly work came back, just less often. Two smaller frictions worth knowing about. The API infrastructure is hosted in Europe, which is a feature for me and a mild latency penalty for colleagues working from the US west coast, where the round trip is visibly longer than to providers with regional endpoints. And on deeply nested or unusually structured code, the completions occasionally lose the thread and need a manual pass, more often than the headline benchmark numbers would suggest. Neither is disqualifying. Both are the sort of thing you only learn after the first month. **What problems is Codestral solving and how is that benefiting you?** The core problem is that autocomplete has a latency budget that most capable models cannot meet. Before Codestral I had tried routing inline completions through a general purpose model, and the suggestions were smart and consistently late. The completion would materialize after I had already typed past the point it was completing, which makes even a correct suggestion worthless. Codestral fits inside the budget. Suggestions arrive while they are still relevant, I accept far more of them, and the assistant went from a novelty I demoed to clients into the thing that quietly writes a meaningful share of my boilerplate. Data residency stopped being a blocker for AI assisted work on sensitive engagements. Several of our clients have contractual language about where source code can travel, and a closed model behind a US hosted API fails that test before the conversation starts. With Codestral I have two answers that pass review: the EU hosted API for the clients who accept a European processor, and a fully self hosted deployment on hardware we control for the ones who accept nothing else. The before state was that certain projects simply excluded AI tooling. The after state is that the same tooling runs everywhere, with the deployment model chosen per contract. Cost tiering is the problem nobody talks about until the first serious invoice. Routing every keystroke level completion through a frontier model is technically possible and financially silly, because completions are high volume and individually low stakes. Splitting the workload so Codestral absorbs the high frequency traffic cut the model spend on our heaviest project to a fraction of the single model setup, without any perceptible drop in the quality of day to day completions. The expensive model still earns its keep on the hard problems. It just stopped being billed for autocomplete. Test coverage on internal projects went from aspirational to normal. The friction of writing tests was never intellectual, it was the tedium of scaffolding the same setup and assertion patterns over and over. Generating the first draft of a test file and editing it down is simply a faster loop than writing from a blank buffer, and the practical result is that modules which would have shipped untested now ship with a baseline suite. Code review conversations changed accordingly, because the question moved from whether tests exist to whether the right cases are covered. Internal tooling became cheap to build. A code review helper that flags obvious issues before a human looks, a script that drafts docstrings for undocumented modules, a small chain that generates and self corrects snippets for our project scaffolding. Each of these existed as an idea for a long time and died on the per token math of a frontier model. With Codestral behind them through LangChain, the marginal cost is low enough that we build these things speculatively and keep the ones that stick. Getting oriented in an unfamiliar codebase got faster, which matters in agency work where a new client repo lands on my desk every few weeks. The old ritual was hours of reading before I trusted myself to change anything. Now the first pass goes through the instruct endpoint: I feed it the modules I need to touch, ask how a flow hangs together, and get a serviceable explanation grounded in the actual code rather than in generalities. The large context window is what makes this workable, because I can include enough surrounding material that the answers reference real symbols instead of guessing. It does not replace reading the code. It replaces the first two hours of reading the wrong code. The polyglot problem dissolved. Our work crosses TypeScript frontends, Python services, SQL, shell scripting, and the occasional legacy language a client brings along, and the old pattern was a different assistant with different behavior in each context. One model with credible coverage across all of them means the muscle memory transfers, the configuration lives in one place, and switching repos no longer means switching tools. It is a mundane benefit and I notice it every single day, which is more than I can say for most features I have paid for. ### 9. Excellent AI Assistant for Boosting Daily Coding Productivity **Rating:** 4.5/5.0 stars **Reviewed by:** Swetha R. | AIML, Small-Business (50 or fewer emp.) **Reviewed Date:** July 18, 2026 **What do you like best about Codestral?** : I highly appreciate its highly accurate inline code completions and how seamlessly it integrates into modern IDEs like VS Code. The context-awareness saves me a massive amount of boilerplate typing every day. **What do you dislike about Codestral?** Occasionally, the multi-line suggestions can lose the context of the larger project structure, leading to minor syntax errors that require manual correction. The resource usage can also spike during heavy indexing sessions. **What problems is Codestral solving and how is that benefiting you?** It solves the issue of repetitive typing and speeds up the implementation of standard design patterns. This benefits me by significantly reducing development time, allowing me to focus on core system architecture and logic. ### 10. Highly Efficient AI Model for Modern Code Generation **Rating:** 5.0/5.0 stars **Reviewed by:** Togo T. | Front-End Developer, Small-Business (50 or fewer emp.) **Reviewed Date:** July 18, 2026 **What do you like best about Codestral?** The model provides excellent context window handling and remarkably fast code completion speeds. It understands front-end structures, JavaScript logic, and HTML/CSS syntax deeply, delivering precise snippets inside my editor without major delays. **What do you dislike about Codestral?** It can occasionally require very specific prompting to avoid generic solutions for complex architectural problems or deeply nested layout structures. **What problems is Codestral solving and how is that benefiting you?** It solves the problem of spending too much time writing boilerplate code and searching for syntax solutions. It acts as an instant reference that helps streamline the development of responsive web templates and components, drastically reducing my time-to-market. ### 11. Excellent value for money, intuitive UI, and useful IDE integrations **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Enterprise (> 1000 emp.) **Reviewed Date:** August 04, 2026 **What do you like best about Codestral?** An excellent European-style language model that provides good answers to the questions asked. The UI is typical of other models, intuitive and allows for file uploads. There are enough integrations with everyday IDEs to integrate it directly, for example, with Visual Studio Code. Regarding performance, it is good, but not as outstanding as the more renowned models and the latest ones that have been released. For this reason, in terms of price, for the performance it offers, it is excellent. The support is on par with other models, with the possibility of writing tickets to ask questions. Obviously, being an artificial intelligence model, it goes without saying that the AI is absolutely integrated. **What do you dislike about Codestral?** I liked the performances less compared to other more well-known extra-European models, it provides more approximate and lower quality responses. **What problems is Codestral solving and how is that benefiting you?** It helped me with programming and coding, allowing both to auto-complete parts of code and to write questions to resolve my doubts. ### 12. Fast, Accurate Code Generation and Autocomplete Across Many Languages **Rating:** 4.5/5.0 stars **Reviewed by:** Sukanya N. | PowerBI developer , Enterprise (> 1000 emp.) **Reviewed Date:** July 23, 2026 **What do you like best about Codestral?** Codestral provides fast, accurate code generation and autocomplete, supports many programming languages, and helps speed up development with high-quality code suggestions. **What do you dislike about Codestral?** Sometimes Codestral generates code that requires manual verification, especially for complex business logic. It may also lack sufficient context for large projects, so developers still need to review and test the generated code before using it in production. **What problems is Codestral solving and how is that benefiting you?** Codestral reduces repetitive coding, accelerates code generation, and assists with debugging and code completion. It helps me develop SQL and PySpark solutions faster, improves productivity, minimizes syntax errors, and lets me focus more on solving business problems instead of writing boilerplate code. ### 13. Fast and accurate coding that helps me everyday. **Rating:** 4.5/5.0 stars **Reviewed by:** Yukthi M. | Student, Small-Business (50 or fewer emp.) **Reviewed Date:** August 04, 2026 **What do you like best about Codestral?** I liked a Fast and accurate coding help. **What do you dislike about Codestral?** Sometimes gives inaccurate code often... **What problems is Codestral solving and how is that benefiting you?** Codestral helps generate code quickly, explains complex logic, and speeds up debugging. It reduces repetitive coding tasks, improves productivity, and lets me focus more on solving problems and building features instead of writing boilerplate code. ### 14. Clean, Easy-to-Use UI with Spaces for Chatting, Work, and Coding **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Veterinary | Small-Business (50 or fewer emp.) **Reviewed Date:** August 12, 2026 **What do you like best about Codestral?** It’s easy to use, with a clean UI and different environments for chatting, working, or coding. **What do you dislike about Codestral?** For an AI assistant, depending on how you use it, the monthly price can feel expensive. **What problems is Codestral solving and how is that benefiting you?** With it, I can create personalized agents for specific tasks. Thanks to its integrations (connectors), it also helps me as a first line of support. ### 15. Fast, Affordable API with Smooth Code Completion **Rating:** 4.5/5.0 stars **Reviewed by:** Gabriel L. | Software engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** August 07, 2026 **What do you like best about Codestral?** Fastest I tried and chap, also easy to use api, liked how I used it for code completion **What do you dislike about Codestral?** Some of the code completion were inacurate and sometimes I got rate limits or other api issues **What problems is Codestral solving and how is that benefiting you?** Used for code completion and sometimes as ai brain on open code for code building as small sub-agent - [View Codestral pricing details and edition comparison](https://www.g2.com/products/codestral/reviews?section=pricing&secure%5Bexpires_at%5D=2026-08-15+04%3A41%3A48+-0500&secure%5Bsession_id%5D=436363f7-e440-4359-aac1-e46da5bd65dc&secure%5Btoken%5D=e8656bb3d2e67326e8b376c877aeea2fc0351c7b6e38f161b9a8cb262ee2cbaa&format=llm_user) ## Codestral Integrations - [GitHub](https://www.g2.com/products/github/reviews) - [Google Cloud Translation API](https://www.g2.com/products/google-cloud-translation-api/reviews) - [IntelliJ IDEA](https://www.g2.com/products/intellij-idea/reviews) - [Mistral AI](https://www.g2.com/products/mistral-ai/reviews) - [opencode.ai](https://www.g2.com/products/opencode-ai/reviews) - [Twilio](https://www.g2.com/products/twilio/reviews) - [Visual Studio Code](https://www.g2.com/products/visual-studio-code/reviews) ## Codestral Features **Additional Functionality** - Tagging - Natural Language Processing - Data Extraction - Multi-Language - Predictive Analytics - Drag & Drop - Speech Recognition - Reporting/Analytics - Data Storage Management - Virtual Personal Assistant (VPA) - AI Copilot - Customer Segmentation - Collaboration Tools - Data Import/Export - Generative AI - For eCommerce - Role-Based Permissions - Customizable Branding - Search/Filter - Monitoring - Document Management - API - Data Visualization - Trend Analysis - Machine Learning - Access Controls/Permissions - Alerts/Escalation - Performance Metrics - Real-Time Data - Third-Party Integrations - Mobile App - Multiple Data Sources - For Sales Teams/Organizations - Sentiment Analysis - Activity Dashboard - Chatbot - Workflow Automation **Additional Functionality** - Code Generation - Text to Image - Generative AI - API - Natural Language Processing - Virtual Characters and Avatars - Content Generation - Personalization and Recommendation - Conditional Generation - Transformer Model - Automated Image & Video Editing - Interactive and Co-Creative Systems - Text Summarization - Data Augmentation - Variation Autoencoder Models - Adversarial Training - Transfer Learning and Fine-tuning - Simulation and Scenario Generation - Creative Design - AI Copilot - Prompt Engineering - Foundation Model **Real-Time AI Coding Assistance** - Real-Time AI Coding Assistance **Proactive Error Detection** - Proactive Error Detection **Workflow Integration of AI** - Workflow Integration of AI **Code Generation** - Code Generation **Intuitive Interface** - Intuitive Interface **Knowledge Management** - Knowledge Management **Context-Aware Coding Assistance** - Context-Aware Coding Assistance **Code Refactoring** - Code Refactoring **Documentation Management** - Documentation Management **Code Explanation** - Code Explanation **Multi-File Context** - Multi-File Context **Functionality - AI Coding Assistants** - Contextual Relevance - Code Optimization - Proactive Error Detection **Usability - AI Coding Assistants** - Collaboration - Integration - Speed - Interface ## Top Codestral Alternatives - [Gemini](https://www.g2.com/products/google-gemini/reviews) - 4.4/5.0 (370 reviews) - [Claude](https://www.g2.com/products/claude-2025-12-11/reviews) - 4.6/5.0 (426 reviews) - [Replit](https://www.g2.com/products/replit/reviews) - 4.5/5.0 (401 reviews)