compacto 2026 producción motor combining 256K contexto, ligero hybrid razonamiento, nativo visión, y ultra-rápido rendimiento.
Technical Specifications
- Provider
- Mistral AI
- Category
- open-source
- Release Date
- 2026-03
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Configurable (Low / Med / High)
- 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-4-latest
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2028+)
- 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 256K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.03/1M 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) · Vision (~258–765 tokens per image tile)
- 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 4 Cost, Context, Hardware & API Integration
Pay-as-you-go API is cheaper for this volume (saves $11.60/mo vs $20 flat plan).
Break-even point: ~1,190 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 4:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 44.5%
- GPQA Diamond
- 56%
- MMLU-Pro
- 78%
- HumanEval
- 88%
- Terminal-Bench
- 38%
- AIME
- 66%
- Long-Context Reasoning
- 80%
- Chatbot Arena Elo
- 1315pts
Strengths
- Colossal 256K ventana de contexto en un compacto 24B abierto framework
- Blazing 120 tokens/sec velocidad con 340ms tiempo-un-primer-token
- Integrated hybrid razonamiento effort y nativo visual comprensión
Limitations & Trade-offs
- Outperformed por billones-parámetro insignia titans en investigación-level physics
- visual resolución limitado un estándar documento tiles
Editorial Assessment
el benchmark estándar para alto-velocidad, bajo-costo abierto multimodal AI en 2026.
Best for: alto-volumen agente orchestration, bajo-costo documento parsing, y perimetral implementaciones
Compare Mistral Small 4
Frequently Asked Questions about Mistral Small 4
How much does the Mistral Small 4 API cost per 1M tokens?+
Mistral Small 4 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 $0.03 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Mistral Small 4 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 4's context window?+
Mistral Small 4 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 16,384 tokens.
Can I run Mistral Small 4 locally on my own GPU (Open Weights)?+
Yes, Mistral Small 4 (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 4 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 4 perform on coding and reasoning benchmarks?+
Mistral Small 4 achieves a composite Intelligence Index of 48/100, scoring 44.5% on SWE-bench Verified, 56% on GPQA Diamond, 78% on MMLU-Pro, and 1315 pts on Chatbot Arena Elo.
What is Mistral Small 4 best used for?+
Mistral Small 4 is best suited for: alto-volumen agente orchestration, bajo-costo documento parsing, y perimetral implementaciones. Assessment: el benchmark estándar para alto-velocidad, bajo-costo abierto multimodal AI en 2026.
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
Devstral 2
Devstral Medium
Devstral Small
Mistral Small 3.2
Mistral Medium 3
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Mistral Small 4:
La unidad básica de texto que procesa un modelo de lenguaje, que normalmente equivale a 3 o 4 caracteres o aproximadamente 0,75 palabras. Por ejemplo, una frase típica se divide en varios tokens.
Un conjunto estandarizado de pruebas para evaluar y comparar objetivamente las capacidades de distintos modelos de IA en tareas como programación (SWE-bench) o matemáticas avanzadas (AIME).
La cantidad máxima de tokens que un modelo puede considerar a la vez, incluyendo tanto la entrada que lee como la salida que genera. Una ventana de 1.000.000 de tokens puede procesar unas 750.000 palabras en una sola petición.
Un modelo de IA entrenado para invertir cómputo adicional en pensar y validar hipótesis lógicas antes de responder, diseñado para problemas complejos de ingeniería y matemáticas.
Una técnica de razonamiento paso a paso donde el modelo genera pensamientos intermedios antes de formular la respuesta final, mejorando radicalmente la resolución de problemas matemáticos y lógicos.
La capacidad de un modelo de detectar la necesidad de una función externa y emitir argumentos JSON estructurados para ejecutar API, búsquedas web o consultas a bases de datos.
Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.