Codestral 25.01: Mistral AI Releases Updated Coding Model with 2x Speed and Improved FIM Performance
Mistral AI has released Codestral 25.01, a significant upgrade to its Codestral coding model featuring a more efficient architecture and improved tokenizer that generates code approximately 2x faster than its predecessor. The model claims state-of-the-art performance for fill-in-the-middle (FIM) tasks across sub-100B parameter models, with a 256k context window and support for 80+ programming languages. Benchmarks show improvements over Codestral 2405 and competitive or superior results against DeepSeek Coder V2 lite and DeepSeek Coder 33B on HumanEval and FIM metrics. The model is available via Mistral's API, IDE plugins (VS Code, JetBrains via Continue), and for on-premises/VPC deployment, with cloud availability on Vertex AI and Azure AI Foundry.
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Mistral AI Releases Codestral: 22B Open-Weight Code Generation Model
Mistral AI has released Codestral, a 22B open-weight model explicitly designed for code generation, supporting 80+ programming languages with a 32k context window. The model is available under a non-production license on HuggingFace, with commercial licenses available on request, and is accessible via a dedicated API endpoint (codestral.mistral.ai) free during an 8-week beta. Codestral claims state-of-the-art performance on RepoBench, HumanEval, and fill-in-the-middle benchmarks, outperforming DeepSeek Coder 33B and matching or exceeding GPT-4-Turbo on some language-specific evals. Integrations are available with LlamaIndex, LangChain, Continue.dev, and Tabnine for IDE-based developer workflows.
Mistral Announces Codestral 25.08 and Integrated Enterprise Coding Stack
Mistral AI has released Codestral 25.08, a code generation model update claiming +30% accepted completions, 50% fewer runaway generations, and improved FIM benchmark performance. The announcement also frames a full enterprise coding stack comprising Codestral (completion), Codestral Embed (code-specific retrieval), and Devstral (agentic workflows via OpenHands), all deployable on-prem or in VPC environments. Devstral Medium is reported to achieve 61.6% on SWE-Bench Verified, while Devstral Small (24B, Apache-2.0) reaches 53.6%. The pitch targets regulated industries blocked by SaaS-only competitors through self-hostable, air-gapped deployment options.
Mistral AI Releases Devstral Medium and Devstral Small 1.1 for Agentic Coding
Mistral AI, in collaboration with All Hands AI, has released two new agentic coding models: Devstral Small 1.1 (24B parameters, Apache 2.0, 53.6% on SWE-Bench Verified) and Devstral Medium (61.6% on SWE-Bench Verified, API-only). Devstral Medium is positioned as a cost-performance leader, claiming to surpass Gemini 2.5 Pro and GPT-4.1 at roughly one-quarter the price, priced at $0.4/M input and $2/M output tokens. Devstral Small 1.1 sets a new state-of-the-art among open models for code agents without test-time scaling, and supports both Mistral function calling and XML formats for broad agentic scaffold compatibility.
Mistral AI Releases Devstral: Apache 2.0 Agentic Coding Model with SWE-Bench SOTA
Mistral AI, in collaboration with All Hands AI, releases Devstral, an agentic LLM specialized for software engineering tasks under the Apache 2.0 license. The model achieves 46.8% on SWE-Bench Verified, surpassing prior open-source state-of-the-art by over 6 percentage points and outperforming larger models like DeepSeek-V3-0324 (671B) and Qwen3 232B-A22B under the same OpenHands scaffold. Devstral is small enough to run on a single RTX 4090 or a Mac with 32GB RAM, and is available via Mistral's API at $0.1/M input tokens, as well as on HuggingFace, Ollama, and other platforms. Mistral indicates a larger agentic coding model is in development.
Mistral Large 2 (123B): New Frontier Model with 128k Context, Multilingual and Code Capabilities
Mistral AI releases Mistral Large 2, a 123-billion-parameter model with a 128k context window, supporting 80+ coding languages and over a dozen natural languages. The model claims competitive performance with GPT-4o, Claude 3 Opus, and Llama 3 405B on code generation, reasoning, and multilingual benchmarks, while targeting cost-efficient single-node inference. Weights are available under a Mistral Research License for non-commercial use, with a commercial license required for self-deployment. The model is accessible via Mistral's la Plateforme API (mistral-large-2407), HuggingFace, and Google Cloud Vertex AI.
Mistral Releases Devstral 2 (123B) and Devstral Small 2 (24B) Coding Models Plus Vibe CLI Agent
Mistral AI has released Devstral 2, a 123B-parameter open-weight coding model scoring 72.2% on SWE-bench Verified, and Devstral Small 2, a 24B model scoring 68.0% on the same benchmark and deployable on consumer hardware. Both models support a 256K context window and are permissively licensed (modified MIT and Apache 2.0 respectively). Mistral also launched Vibe CLI, an open-source terminal-based coding agent powered by Devstral that supports multi-file orchestration, natural language code editing, and IDE integration via Agent Communication Protocol. Devstral 2 is currently free via API with post-free pricing of $0.40/$2.00 per million tokens input/output.
Mistral AI Launches Mistral Code: Enterprise AI Coding Assistant with On-Prem Deployment
Mistral AI has announced Mistral Code, an enterprise-grade AI coding assistant currently in private beta for JetBrains IDEs and VSCode. The product bundles four specialized models (Codestral, Codestral Embed, Devstral, Mistral Medium) with an IDE plugin, admin controls, and deployment options ranging from serverless to air-gapped on-premises GPUs. It is built on a fork of the open-source Continue project with enterprise additions including RBAC, audit logging, and fine-tuning on private repositories. Early enterprise adopters include Abanca, SNCF (4,000 developers), and Capgemini (1,500+ developers).
Mistral Small 4: Unified Multimodal, Reasoning, and Coding MoE Model Released Under Apache 2.0
Mistral AI has released Mistral Small 4, a 119B-parameter Mixture-of-Experts model (6B active per token) that unifies capabilities previously split across Magistral (reasoning), Pixtral (multimodal), and Devstral (coding agents) into a single open-weights model. The model features a 256k context window, configurable reasoning effort via a `reasoning_effort` parameter, native text and image input support, and is released under Apache 2.0. Mistral claims 40% latency reduction and 3x throughput improvement over Mistral Small 3, with benchmark results showing competitive performance against GPT-OSS 120B and Qwen models while producing significantly shorter outputs. The release includes day-0 availability as an NVIDIA NIM and support across vLLM, llama.cpp, SGLang, and Transformers.