El modelo insignia europeo de frontera con ventana de contexto de 128K, sólido soporte multilingüe y rendimiento excepcional en codificación.
Technical Specifications
- Provider
- Mistral AI
- Category
- flagship
- Release Date
- 2024-07
- 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-large-2-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 ($2/1M in) across full 128K window
- Prompt Cache Write & TTL Policy
- Cached input at $1/1M 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
- 76 GB VRAM (4-bit Quantized) · 264 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 Large 2 Cost, Context, Hardware & API Integration
For single-user chat, a $20/mo subscription caps your bill below this API volume.
Break-even point: ~56 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 Large 2:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 38%
- GPQA Diamond
- 54%
- MMLU-Pro
- 76.5%
- HumanEval
- 84%
- Chatbot Arena Elo
- 1282pts
Strengths
- Directly rivals GPT-4o y Claude 3.5 Sonnet en razonamiento y generación de código
- denso 123B parámetro modelo optimizado para único-node servicio eficiencia
- primer-clase JSON schema outputs y multipaso agentic function calling
Limitations & Trade-offs
- texto-solo sin nativo imagen visión capacidades
- requiere empresarial multi-GPU server para local hosting
Editorial Assessment
El modelo comercial de frontera más potente de Europa que garantiza precisión de nivel mundial.
Best for: Cumplimiento empresarial europeo, codificación multifarchivo y procesamiento de documentos multilingües
Compare Mistral Large 2
Frequently Asked Questions about Mistral Large 2
How much does the Mistral Large 2 API cost per 1M tokens?+
Mistral Large 2 by Mistral AI is priced at $2 per 1M input tokens and $6 per 1M output tokens. With prompt caching enabled, cached input tokens cost $1 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Mistral Large 2 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 Large 2's context window?+
Mistral Large 2 features a 128K tokens context window, which equals approximately 96,000 English words or ~256 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 8,192 tokens.
Can I run Mistral Large 2 locally on my own GPU (Open Weights)?+
Yes, Mistral Large 2 (123B) 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 Large 2 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 Large 2 perform on coding and reasoning benchmarks?+
Mistral Large 2 achieves a composite Intelligence Index of 46/100, scoring 38% on SWE-bench Verified, 54% on GPQA Diamond, 76.5% on MMLU-Pro, and 1282 pts on Chatbot Arena Elo.
What is Mistral Large 2 best used for?+
Mistral Large 2 is best suited for: Cumplimiento empresarial europeo, codificación multifarchivo y procesamiento de documentos multilingües. Assessment: El modelo comercial de frontera más potente de Europa que garantiza precisión de nivel mundial.
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 Large 2:
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.
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.
Un protocolo abierto desarrollado por Anthropic que permite a los agentes de IA conectarse de forma estandarizada a sistemas de archivos locales, entornos de desarrollo y servicios externos.
Una optimización donde los proveedores almacenan estados de atención previos para prefijos de prompts frecuentes, reduciendo el coste de entrada hasta en un 90% y acelerando la respuesta.
Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.