Mistral AI が提供する Mistral 7B は、最大 32K トークンのコンテキストウィンドウを備え、入力 $0.06/100万トークン・出力 $0.06/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
Technical Specifications
- Provider
- Mistral AI
- Category
- fast
- Release Date
- 2023-09
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Fixed
- Structured Outputs
- No
- 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)
- mistral-7b-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 ($0.06/1M in) across full 33K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.015/1M tokens
- Computer Use & Browser GUI Agent
- Text-only completion (No GUI action loop)
- MCP & Parallel Tool Calling
- Prompt-based JSON extraction only
- 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
- 5 GB VRAM (4-bit Quantized) · 16 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
Mistral 7B Cost, Context, Hardware & API Integration
Pay-as-you-go API is cheaper for this volume (saves $16.85/mo vs $20 flat plan).
Break-even point: ~3,175 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 Mistral 7B:
Benchmark Scores & 6-Axis Capability Radar
- MMLU-Pro
- 56%
- HumanEval
- 40.2%
- Chatbot Arena Elo
- 1120pts
Strengths
- Mistral AI の高度な Sliding Window Attention (SWA) Transformer アーキテクチャと 32K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0.06/1M、出力 $0.06/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Limitations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Editorial Assessment
Mistral 7B は、性能・速度・API コストのバランスに優れた Mistral AI の主力モデルです。
Best for: エンタープライズ自動化、ソフトウェア開発、AI エージェント構築、Mistral AI エコシステムの統合
Compare Mistral 7B
Frequently Asked Questions about Mistral 7B
How much does the Mistral 7B API cost per 1M tokens?+
Mistral 7B by Mistral AI is priced at $0.06 per 1M input tokens and $0.06 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.015 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Mistral 7B 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 Mistral 7B's context window?+
Mistral 7B features a 33K tokens context window, which equals approximately 24,576 English words or ~66 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 4,096 tokens.
Can I run Mistral 7B locally on my own GPU (Open Weights)?+
Yes, Mistral 7B (7.3B) 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 Mistral 7B 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 Mistral 7B perform on coding and reasoning benchmarks?+
Mistral 7B achieves a composite Intelligence Index of 32/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, 56% on MMLU-Pro, and 1120 pts on Chatbot Arena Elo.
What is Mistral 7B best used for?+
Mistral 7B is best suited for: エンタープライズ自動化、ソフトウェア開発、AI エージェント構築、Mistral AI エコシステムの統合. Assessment: Mistral 7B は、性能・速度・API コストのバランスに優れた Mistral AI の主力モデルです。
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 Mistral 7B:
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.
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.
A GPU memory optimization that stores previously calculated Key and Value attention matrices so the model does not recompute attention for existing tokens at each generation step. KV cache is vital for fast streaming generation and prompt caching.
The neural network architecture introduced in 2017 that powers modern LLMs, using self-attention mechanisms to process all tokens in a sequence concurrently rather than sequentially.
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.
公的ソース: Mistral AI: Mistral 7B Research Release (September 2023)↗