Head-to-Head Model Comparison

Mistral Small 3.2 vs Mistral Medium 3

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado pela equipe de pesquisa do GetInfoUsÚltima revisão: October 2026Política editorial

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Mistral AI

Mistral Small 3.2

Modelo Base de Pesos Abertos

O Mistral Small 3.2 da Mistral AI é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 128K, velocidade de 100 tokens/s, índice de inteligência de 43/

Intelligence Index43 / 100

Input Token:

$0.10 / 1M

Output Token:

$0.30 / 1M

Context Window:

128K tokens

SWE-bench Verified:

34%

Mistral AI

Mistral Medium 3

Modelo Base de Pesos Abertos

O Mistral Medium 3 da Mistral AI é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 131K, velocidade de 85 tokens/s, índice de inteligência de 47/10

Intelligence Index47 / 100

Input Token:

$0.40 / 1M

Output Token:

$2.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

40.5%

Principais Diferenças em Resumo

Mistral Small 3.2 is 5.2x cheaper for API usage (input + output tokens combined).
Mistral Medium 3 has a larger context window (131K vs 128K tokens).
Mistral Small 3.2 is faster at 100 tok/s vs 85 tok/s.
Mistral Medium 3 leads on SWE-bench coding (40.5% vs 34.0%).

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

Feature	Mistral Small 3.2	Mistral Medium 3
Intelligence Index Composite reasoning & knowledge score (0-100)	43	47
Provider	Mistral AI	Mistral AI
Input Price Cost per million input tokens	$0.10 / 1M	$0.40 / 1M
Output Price Cost per million output tokens	$0.30 / 1M	$2.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.09 / 1M	$0.35 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	131.1K tokens~197 A4 pages of text
Max Output Tokens Per single request	8K	16K
Throughput Speed Tokens generated per second	100 tok/s	85 tok/s
SWE-bench Verified Real-world software engineering	34%	40.5%
GPQA Diamond Graduate-level reasoning	48.5%	57.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.05 / 1M	$0.10 / 1M

Quem vence em cada categoria?

At-a-glance decision framework

Coding

Mistral Medium 3

+6.5% SWE-bench lead

Cost

Mistral Small 3.2

5.2x cheaper monthly runtime

Speed

Mistral Small 3.2

+18% faster generation

Context

Mistral Medium 3

131.1K tokens window

Pontuações de Benchmarks Verificadas

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

Mistral Small 3.2Mistral Medium 3

SWE-bench Verified (Autonomous Coding)

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

34%vs40.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

48.5%vs57.5%

MMLU-Pro (Multi-task Language Understanding)

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

72.5%vs79.5%

LMSYS Chatbot Arena Elo

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

1255ptsvs1310pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

29%vs36%

AIME (Advanced Math Competition)

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

55%vs68%

Long-Context Reasoning

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

68%vs77%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

100 tok/s vs85 tok/s

Latência e Tempo de Resposta

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 Small 3.20.40s

Mistral Medium 30.48s

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

Mistral Small 3.25.40s

Mistral Medium 36.36s

Simulador Interativo de Custo 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 Small 3.2 Monthly API Bill

$0.50 / month

$0.1/1M in · $0.3/1M out

Mistral Medium 3 Monthly API Bill

$2.55 / month

$0.4/1M in · $2/1M out

Mistral Small 3.2 saves approximately $2.06 / month (81%) under this workload.5.2x Savings

Mistral Small 3.2 · Strengths & Key Advantages

Janela de contexto de 128K com limite máximo de saída de 8K tokens (SWE-bench Verified 34% · GPQA Diamond 48.5%)
Taxa de transferência rápida de 100 tokens/segundo com latência inicial (TTFT) de 400 ms
Pesos abertos (24B) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama

Considerations & Trade-offs

Requer servidores GPU com memória VRAM adequada para execução local em precisão total
Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

Mistral Medium 3 · Strengths & Key Advantages

Janela de contexto de 131K com limite máximo de saída de 16K tokens (SWE-bench Verified 40.5% · GPQA Diamond 57.5%)
Taxa de transferência rápida de 85 tokens/segundo com latência inicial (TTFT) de 480 ms
Pesos abertos (Confidential (~70B)) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama

Considerations & Trade-offs

Requer servidores GPU com memória VRAM adequada para execução local em precisão total
Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

Token Use & Task Economics

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

Metric	Mistral Small 3.2	Mistral Medium 3
Cost per Task Estimated USD to finish one task	$0.09	$0.35
Output Tokens / Task Tokens generated per task	25K	35K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Especificações Técnicas

Architecture, release timeline, capabilities, and modalities.

Feature	Mistral Small 3.2	Mistral Medium 3
Official API Model ID (SDK)	mistral-small-3-2-latest	mistral-medium-3-latest
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-06	2025-05
Knowledge Cutoff	April 2025	March 2025
Architecture	Compact Vision-Language Transformer	Enterprise Multimodal Transformer
Parameters	24B	Confidential (~70B)
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 ($0.1/1M in) across full 128K window	Flat rate ($0.4/1M in) across full 131K window
Prompt Cache Policy & TTL	Cached input at $0.05/1M 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) · 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) · 15 GB VRAM (4-bit Quantized) · 52 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 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, vision	text, vision
Output Modalities	text	text

Mistral Small 3.2 vs Mistral Medium 3: Respostas Rápidas (FAQ)

Which is more intelligent, Mistral Small 3.2 or Mistral Medium 3?

Mistral Medium 3 is more intelligent. It scores 47 compared with 43 on the composite Intelligence Index.

Which model is faster?

Mistral Small 3.2 is faster, generating 100 tokens per second compared with 85 tokens per second.

Which model is cheaper to run?

Mistral Small 3.2 is cheaper at $0.09 per 1M tokens blended, compared with $0.35 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Mistral Small 3.2 has lower latency, with a time to first token of 0.40s compared with 0.48s.

Which model has a larger context window?

Mistral Medium 3 has a larger context window, supporting 131.1K tokens compared with 128K tokens.

Veredito

Tanto Mistral Small 3.2 (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) quanto Mistral Medium 3 (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Mistral Medium 3 lidera em inteligência geral e programação, Mistral Small 3.2 oferece geração mais rápida e Mistral Small 3.2 apresenta o melhor custo-benefício em produção.

Perguntas Frequentes (FAQ)

Qual é melhor para programação e raciocínio, Mistral Small 3.2 ou Mistral Medium 3?+

Nas avaliações de inteligência e código, Mistral Medium 3 leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.

Qual modelo tem a API mais barata, Mistral Small 3.2 ou Mistral Medium 3?+

O Mistral Small 3.2 custa $0.10 / $0.30 por 1M de tokens (entrada/saída) e o Mistral Medium 3 custa $0.40 / $2.00. No custo combinado, Mistral Small 3.2 é mais econômico.

Qual modelo possui maior janela de contexto e velocidade?+

O Mistral Small 3.2 suporta 128K tokens de contexto contra 131.1K tokens do Mistral Medium 3, enquanto Mistral Small 3.2 entrega maior velocidade de geração.

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 Small 3.2 vs Mistral Medium 3

Troque e Compare Qualquer Modelo

Mistral Small 3.2

Mistral Medium 3

Quem vence em cada categoria?

Pontuações de Benchmarks Verificadas

Latência e Tempo de Resposta

Simulador Interativo de Custo de Tokens

Mistral Small 3.2 · Strengths & Key Advantages

Considerations & Trade-offs

Mistral Medium 3 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificações Técnicas

Mistral Small 3.2 vs Mistral Medium 3: Respostas Rápidas (FAQ)

Veredito

Perguntas Frequentes (FAQ)

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