élevé-efficacité 24B ouvert modèle de fondation offrant rapide latence, faible mémoire empreinte, et d'entreprise-grade instruction adherence.
Technical Specifications
- Provider
- Mistral AI
- Category
- open-source
- Release Date
- 2025-01
- 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)
- mistral-small-3-latest
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2027+)
- 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 ($0.1/1M in) across full 32K 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
- 15 GB VRAM (4-bit Quantized) · 52 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 Small 3 Cost, Context, Hardware & API Integration
Pay-as-you-go API is cheaper for this volume (saves $11.00/mo vs $20 flat plan).
Break-even point: ~1,111 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 Small 3:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 31.5%
- GPQA Diamond
- 46.5%
- MMLU-Pro
- 70.2%
- HumanEval
- 79%
- Terminal-Bench
- 27%
- AIME
- 52%
- Long-Context Reasoning
- 64%
- Chatbot Arena Elo
- 1238pts
Strengths
- Extremely faible API tarification ($0.10/$0.30) et léger local empreinte
- Snappy inférence vitesse exceeding 100 tokens/sec
- fiable function calling et classification précision
Limitations & Trade-offs
- 32K fenêtre de contexte limits profond dépôt scanning
- texte-seulement avec no image entrée capacités
Editorial Assessment
un economical, razor-sharp 24B modèle conçu pour élevé-débit production.
Best for: élevé-volume client support, classification pipelines, et faible-latence chatbots
Compare Mistral Small 3
Frequently Asked Questions about Mistral Small 3
How much does the Mistral Small 3 API cost per 1M tokens?+
Mistral Small 3 by Mistral AI is priced at $0.1 per 1M input tokens and $0.3 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 Mistral Small 3 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 Small 3's context window?+
Mistral Small 3 features a 32K tokens context window, which equals approximately 24,000 English words or ~64 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 8,192 tokens.
Can I run Mistral Small 3 locally on my own GPU (Open Weights)?+
Yes, Mistral Small 3 (24B) 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 Small 3 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 Small 3 perform on coding and reasoning benchmarks?+
Mistral Small 3 achieves a composite Intelligence Index of 41/100, scoring 31.5% on SWE-bench Verified, 46.5% on GPQA Diamond, 70.2% on MMLU-Pro, and 1238 pts on Chatbot Arena Elo.
What is Mistral Small 3 best used for?+
Mistral Small 3 is best suited for: élevé-volume client support, classification pipelines, et faible-latence chatbots. Assessment: un economical, razor-sharp 24B modèle conçu pour élevé-débit production.
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 Small 3:
L'unité textuelle de base traitée par un modèle linguistique, représentant généralement 3 à 4 caractères ou environ 0,75 mot. Par exemple, une phrase courante est découpée en plusieurs jetons.
Une suite de tests standardisés permettant d'évaluer et de comparer objectivement les performances des modèles d'IA sur des tâches ciblées (code avec SWE-bench, mathématiques avec AIME, etc.).
Le nombre maximal de jetons qu'un modèle peut traiter simultanément (entrée et sortie comprises). Une fenêtre de 1 000 000 jetons permet d'analyser environ 750 000 mots en une seule requête.
La capacité d'un LLM à déterminer quand une action externe est requise et à générer des arguments structurés pour invoquer des API, calculatrices ou bases de données avant de poursuivre.
Un standard ouvert créé par Anthropic permettant aux agents d'IA de se connecter de façon uniforme aux systèmes de fichiers locaux, aux environnements de développement et aux API tierces.
Une optimisation tarifaire et technique conservant les états d'attention des préfixes de prompts récurrents, réduisant les coûts d'entrée de 50 à 90 % tout en abaissant la latence.
Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.