Mistral Large 2 vs Mistral Medium 3.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Mistral Large 2 de Mistral AI y Mistral Medium 3.5 de Mistral AI compiten directamente en el ecosistema actual de IA. Mistral Large 2 ofrece una ventana de contexto de 128K tokens a $2.00/$6.00 por millón de tokens (entrada/salida), mientras que Mistral Medium 3.5 ofrece 262.1K tokens de contexto a $0.40/$2.00 por millón de tokens. Analizamos sus benchmarks, latencia y costos reales.
Cambia y Compara Cualquier Modelo
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Mistral Large 2
Modelo Base de Pesos AbiertosMistral Large 2 de Mistral AI es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 128K, velocidad de 75 tokens/s, índice de inteligencia de 46/10
$2.00 / 1M
$6.00 / 1M
128K tokens
38%
Mistral Medium 3.5
Modelo Base de Pesos AbiertosMistral Medium 3.5 de Mistral AI es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 262K, velocidad de 90 tokens/s, índice de inteligencia de 55
$0.40 / 1M
$2.00 / 1M
262.1K tokens
54%
Diferencias Clave de un Vistazo
- Mistral Medium 3.5 is 3.9x cheaper for API usage (input + output tokens combined).
- Mistral Medium 3.5 has a larger context window (262K vs 128K tokens).
- Mistral Medium 3.5 is faster at 90 tok/s vs 75 tok/s.
- Mistral Medium 3.5 leads on SWE-bench coding (54.0% vs 38.0%).
| Característica | Mistral Large 2 | Mistral Medium 3.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 46 | 55 |
Provider | Mistral AI | Mistral AI |
Input Price Cost per million input tokens | $2.00 / 1M | $0.40 / 1M |
Output Price Cost per million output tokens | $6.00 / 1M | $2.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.70 / 1M | $0.35 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 8K | 33K |
Throughput Speed Tokens generated per second | 75 tok/s | 90 tok/s |
SWE-bench Verified Real-world software engineering | 38% | 54% |
GPQA Diamond Graduate-level reasoning | 54% | 68% |
Cached Input Price Prompt caching discount per 1M tokens | $1.00 / 1M | $0.10 / 1M |
¿Quién gana en cada área?
At-a-glance decision frameworkPuntuaciones de Benchmarks Verificadas
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Mistral Large 2 · Strengths & Key Advantages
- Ventana de contexto de 128K con hasta 8K tokens de salida máxima (SWE-bench Verified 38% · GPQA Diamond 54%)
- Rendimiento ágil de 75 tokens/segundo con latencia inicial (TTFT) de solo 520 ms
- Pesos abiertos (123B) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama
Considerations & Trade-offs
- Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
- En volúmenes masivos de tokens, se recomienda activar el caché de prompts y Batch API para optimizar costos
Mistral Medium 3.5 · Strengths & Key Advantages
- Ventana de contexto de 262K con hasta 33K tokens de salida máxima (SWE-bench Verified 54% · GPQA Diamond 68%)
- Rendimiento ágil de 90 tokens/segundo con latencia inicial (TTFT) de solo 450 ms
- Pesos abiertos (Confidential (~80B MoE)) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama
Considerations & Trade-offs
- Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
- En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Mistral Large 2 | Mistral Medium 3.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $0.38 |
Output Tokens / Task Tokens generated per task | 24K | 38K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 18K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Mistral Large 2 | Mistral Medium 3.5 |
|---|---|---|
| Official API Model ID (SDK) | mistral-large-2-latest | mistral-medium-3-5-latest |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-07 | 2026-04 |
| Knowledge Cutoff | July 2024 | February 2026 |
| Architecture | 123B Dense Frontier Transformer | Enterprise Hybrid Reasoning MoE |
| Parameters | 123B | Confidential (~80B MoE) |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 128K window | Flat rate ($0.4/1M in) across full 262K window |
| Prompt Cache Policy & TTL | Cached input at $1/1M tokens | Cached input at $0.1/1M tokens |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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 | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 76 GB VRAM (4-bit Quantized) · 264 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 50 GB VRAM (4-bit Quantized) · 172 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
Mistral Large 2 vs Mistral Medium 3.5: Respuestas Rápidas (FAQ)
Which is more intelligent, Mistral Large 2 or Mistral Medium 3.5?
Mistral Medium 3.5 is more intelligent. It scores 55 compared with 46 on the composite Intelligence Index.
Which model is faster?
Mistral Medium 3.5 is faster, generating 90 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Mistral Medium 3.5 is cheaper at $0.35 per 1M tokens blended, compared with $1.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Mistral Medium 3.5 has lower latency, with a time to first token of 0.45s compared with 0.52s.
Which model has a larger context window?
Mistral Medium 3.5 has a larger context window, supporting 262.1K tokens compared with 128K tokens.
Veredicto
Tanto Mistral Large 2 (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) como Mistral Medium 3.5 (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Mistral Medium 3.5 lidera en inteligencia general y programación, Mistral Medium 3.5 ofrece mayor velocidad de generación de tokens y Mistral Medium 3.5 resulta más rentable para cargas de trabajo API de alto volumen.
Preguntas frecuentes
¿Cuál es mejor para programación y razonamiento, Mistral Large 2 o Mistral Medium 3.5?+
En las pruebas de inteligencia compuesta y resolución de código, Mistral Medium 3.5 obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.
¿Cuánto cuesta la API de Mistral Large 2 frente a Mistral Medium 3.5?+
Mistral Large 2 cuesta $2.00 / $6.00 por millón de tokens de entrada/salida, mientras que Mistral Medium 3.5 cuesta $0.40 / $2.00. En costo combinado, Mistral Medium 3.5 es la opción más económica.
¿Qué modelo tiene mayor ventana de contexto y menor latencia?+
Mistral Large 2 cuenta con 128K tokens de contexto frente a 262.1K tokens de Mistral Medium 3.5. En velocidad de inferencia, Mistral Medium 3.5 ofrece un rendimiento superior.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Mistral Large 2 vs. GPT-4o: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
- Mistral Large 2 vs. Llama 3.3 70B: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
- Mistral Medium 3.5 vs. Claude Sonnet 5.5: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
- Mistral Medium 3.5 vs. DeepSeek V4.1-Flash: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
- Mistral Medium 3.5 vs. GPT-5.5: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
- Pixtral 12B vs. Mistral Large 2: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: Documentación oficial de API de Mistral AI y Mistral AI, tarjetas de sistema y benchmarks independientes (2026).