Codestral от Mistral AI — передовая модель искусственного интеллекта с контекстным окном 256K токенов и стоимостью API $0.2/1M входящих и $0.6/1M исходящих токенов для программирования, аналитики и автоматизации.
Technical Specifications
- Provider
- Mistral AI
- Category
- coding
- Release Date
- 2024-05
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Fixed
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- codestral-latest
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Legacy Stable (Upgrade path available in Mistral AI lineup)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($0.2/1M in) across full 256K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.05/1M tokens
- Computer Use & Browser GUI Agent
- API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
- MCP & Parallel Tool Calling
- Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 14 GB VRAM (4-bit Quantized) · 47 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Codestral Cost, Context, Hardware & API Integration
Pay-as-you-go API is cheaper for this volume (saves $3.50/mo vs $20 flat plan).
Break-even point: ~606 requests/day equals $20/month.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Codestral:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 28%
- GPQA Diamond
- 42%
- MMLU-Pro
- 70%
- HumanEval
- 81.1%
- Chatbot Arena Elo
- 1240pts
Strengths
- Современная архитектура Code-Optimized Transformer with 32K Window от Mistral AI с поддержкой до 256K токенов контекста
- Оптимальная стоимость использования ($0.2/1M вход, $0.6/1M выход) и высокая скорость генерации
- Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Limitations & Trade-offs
- При работе с максимальным контекстом требуется контроль расхода токенов
- Для узкоспециализированных задач рекомендуется точная настройка системных промптов
Editorial Assessment
Codestral — надежная и эффективная модель от Mistral AI, сочетающая высокое качество ответов и доступную цену.
Best for: Корпоративной автоматизации, разработки ПО, ИИ-агентов и продуктовых решений на базе Mistral AI
Compare Codestral
Frequently Asked Questions about Codestral
How much does the Codestral API cost per 1M tokens?+
Codestral by Mistral AI is priced at $0.2 per 1M input tokens and $0.6 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.05 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Codestral pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in Codestral's context window?+
Codestral features a 256K tokens context window, which equals approximately 192,000 English words or ~512 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 8,192 tokens.
Can I run Codestral locally on my own GPU (Open Weights)?+
Yes, Codestral (22B) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does Mistral AI train on data sent to the Codestral API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Permissive Open-Weights (Apache 2.0 / MIT Commercial). Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does Codestral perform on coding and reasoning benchmarks?+
Codestral achieves a composite Intelligence Index of 40/100, scoring 28% on SWE-bench Verified, 42% on GPQA Diamond, 70% on MMLU-Pro, and 1240 pts on Chatbot Arena Elo.
What is Codestral best used for?+
Codestral is best suited for: Корпоративной автоматизации, разработки ПО, ИИ-агентов и продуктовых решений на базе Mistral AI. Assessment: Codestral — надежная и эффективная модель от Mistral AI, сочетающая высокое качество ответов и доступную цену.
Mistral AI Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to Mistral AI's flagship reasoning tier:
Mistral Medium 3.5
Mistral Small 4
Devstral 2
Devstral Medium
Devstral Small
Mistral Small 3.2
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Codestral:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
The ability of a language model to recognize when an external capability is needed and output structured JSON arguments to invoke external APIs, databases, or terminal commands. The runtime executes the tool and passes results back to the model.
An open standard created by Anthropic that allows AI applications and agents to connect seamlessly to local filesystems, development environments, and third-party APIs via standardized server interfaces.
A cost and latency optimization where inference providers store pre-computed attention states for repeated prompt prefixes. Reusing cached tokens typically slashes API input costs by 50% to 90% and cuts TTFT substantially.
Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Официальные источники: Mistral AI Official Documentation & Pricing↗