Head-to-Head Model Comparison

Mistral Medium 3.5 vs Claude Sonnet 5.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial

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Mistral Medium 3.5Profile

Claude Sonnet 5.5Profile

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Mistral AI

Mistral Medium 3.5

Modelo Base de Pesos Abiertos

Mistral 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

Intelligence Index55 / 100

Input Token:

$0.40 / 1M

Output Token:

$2.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

54%

Anthropic

Claude Sonnet 5.5

Modelo Insignia de Frontera

Claude Sonnet 5.5 de Anthropic es un modelo de IA diseñado para razonamiento empresarial de frontera, programación avanzada y flujos de producción, con ventana de contexto de 1M, velocidad de 105 tokens/s, índice de inteligencia de 78/100 y

Intelligence Index78 / 100

Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

82.9%

Diferencias Clave de un Vistazo

Mistral Medium 3.5 is 4.8x cheaper for API usage (input + output tokens combined).
Claude Sonnet 5.5 has a larger context window (1M vs 262K tokens).
Claude Sonnet 5.5 is faster at 105 tok/s vs 90 tok/s.
Claude Sonnet 5.5 leads on SWE-bench coding (82.9% vs 54.0%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Característica	Mistral Medium 3.5	Claude Sonnet 5.5
Intelligence Index Composite reasoning & knowledge score (0-100)	55	78
Provider	Mistral AI	Anthropic
Input Price Cost per million input tokens	$0.40 / 1M	$2.00 / 1M
Output Price Cost per million output tokens	$2.00 / 1M	$10.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.35 / 1M	$1.54 / 1M
Context Window Maximum input + output tokens	262.1K tokens~393 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	33K	128K
Throughput Speed Tokens generated per second	90 tok/s	105 tok/s
SWE-bench Verified Real-world software engineering	54%	82.9%
GPQA Diamond Graduate-level reasoning	68%	79.4%
Cached Input Price Prompt caching discount per 1M tokens	$0.10 / 1M	$0.20 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

Claude Sonnet 5.5

+28.9% SWE-bench lead

Costo

Mistral Medium 3.5

4.8x cheaper monthly runtime

Velocidad

Claude Sonnet 5.5

+17% faster generation

Contexto

Claude Sonnet 5.5

1M tokens window

Puntuaciones de Benchmarks Verificadas

Official evaluation metrics verified by independent third-party research labs.

Mistral Medium 3.5Claude Sonnet 5.5

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

54%vs82.9%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

68%vs79.4%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

84.5%vs90.1%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1370ptsvs1490pts

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

46%vs58.9%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

78%vsN/A

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

85%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

90 tok/s vs105 tok/s

Latencia y Tiempo de Respuesta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Mistral Medium 3.50.45s

Claude Sonnet 5.50.55s

Est. End-to-End Response (500 tokens)TTFT + generation time

Mistral Medium 3.56.01s

Claude Sonnet 5.55.31s

Simulador Interactivo de Costos de Tokens

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Mistral Medium 3.5 Monthly API Bill

$2.55 / month

$0.4/1M in · $2/1M out

Claude Sonnet 5.5 Monthly API Bill

$12.30 / month

$2/1M in · $10/1M out

Mistral Medium 3.5 saves approximately $9.75 / month (79%) under this workload.4.8x Savings

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

Claude Sonnet 5.5 · Strengths & Key Advantages

Ventana de contexto de 1M con hasta 128K tokens de salida máxima (SWE-bench Verified 82.9% · GPQA Diamond 79.4%)
Rendimiento ágil de 105 tokens/segundo con latencia inicial (TTFT) de solo 550 ms
Economía de API competitiva ($2/$10 por 1M de tokens) con soporte para caché de prompts y salidas JSON estructuradas

Considerations & Trade-offs

Modelo propietario en la nube; no permite descargar los pesos para ejecución local sin conexión
En volúmenes masivos de tokens, se recomienda activar el caché de prompts y Batch API para optimizar costos

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	Mistral Medium 3.5	Claude Sonnet 5.5
Cost per Task Estimated USD to finish one task	$0.38	$3.29
Output Tokens / Task Tokens generated per task	38K	72K
Reasoning Tokens / Task Thinking tokens burned per task	18K	37K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	Mistral Medium 3.5	Claude Sonnet 5.5
Official API Model ID (SDK)	mistral-medium-3-5-latest	claude-sonnet-5-5-20260901
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	Anthropic Messages API + AWS Bedrock + Vertex AI
Release Date	2026-04	2026-09
Knowledge Cutoff	February 2026	June 2026
Architecture	Enterprise Hybrid Reasoning MoE	Adaptive Frontier Multimodal Transformer
Parameters	Confidential (~80B MoE)	Confidential
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($0.4/1M in) across full 262K window	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL	Cached input at $0.1/1M tokens	$0.2/1M cached read (90% off); 5-min default & 1-hour extended TTL
Computer Use / GUI Agent	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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	Native Web Search Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Custom Model Tuning via Amazon Bedrock & Prompt Distillation
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)	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) · 50 GB VRAM (4-bit Quantized) · 172 GB VRAM (Native BF16)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text, vision	text, vision
Output Modalities	text	text

Mistral Medium 3.5 vs Claude Sonnet 5.5: Respuestas Rápidas (FAQ)

Which is more intelligent, Mistral Medium 3.5 or Claude Sonnet 5.5?

Claude Sonnet 5.5 is more intelligent. It scores 78 compared with 55 on the composite Intelligence Index.

Which model is faster?

Claude Sonnet 5.5 is faster, generating 105 tokens per second compared with 90 tokens per second.

Which model is cheaper to run?

Mistral Medium 3.5 is cheaper at $0.35 per 1M tokens blended, compared with $1.54 (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 0.55s.

Which model has a larger context window?

Claude Sonnet 5.5 has a larger context window, supporting 1M tokens compared with 262.1K tokens.

Veredicto

Tanto 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.) como Claude Sonnet 5.5 (Ideal para razonamiento empresarial de frontera, programación avanzada y flujos de producción, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Claude Sonnet 5.5 lidera en inteligencia general y programación, Claude Sonnet 5.5 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 Medium 3.5 o Claude Sonnet 5.5?+

En las pruebas de inteligencia compuesta y resolución de código, Claude Sonnet 5.5 obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Mistral Medium 3.5 frente a Claude Sonnet 5.5?+

Mistral Medium 3.5 cuesta $0.40 / $2.00 por millón de tokens de entrada/salida, mientras que Claude Sonnet 5.5 cuesta $2.00 / $10.00. En costo combinado, Mistral Medium 3.5 es la opción más económica.

¿Qué modelo tiene mayor ventana de contexto y menor latencia?+

Mistral Medium 3.5 cuenta con 262.1K tokens de contexto frente a 1M tokens de Claude Sonnet 5.5. En velocidad de inferencia, Claude Sonnet 5.5 ofrece un rendimiento superior.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Mistral Medium 3.5 vs Claude Sonnet 5.5

Cambia y Compara Cualquier Modelo

Mistral Medium 3.5

Claude Sonnet 5.5

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Mistral Medium 3.5 · Strengths & Key Advantages

Considerations & Trade-offs

Claude Sonnet 5.5 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Mistral Medium 3.5 vs Claude Sonnet 5.5: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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