Mistral Large 2 vs Mistral Medium 3.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Mistral Large 2 da Mistral AI e Mistral Medium 3.5 da Mistral AI são dois modelos de IA de destaque. O Mistral Large 2 oferece uma janela de contexto de 128K tokens a $2.00/$6.00 por milhão de tokens, enquanto o Mistral Medium 3.5 oferece 262.1K tokens de contexto a $0.40/$2.00 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.
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Mistral Large 2
Modelo Base de Pesos AbertosO Mistral Large 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 75 tokens/s, índice de inteligência de 46/100
$2.00 / 1M
$6.00 / 1M
128K tokens
38%
Mistral Medium 3.5
Modelo Base de Pesos AbertosO Mistral Medium 3.5 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 262K, velocidade de 90 tokens/s, índice de inteligência de 55/
$0.40 / 1M
$2.00 / 1M
262.1K tokens
54%
Principais Diferenças em Resumo
- 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%).
| Feature | 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 |
Quem vence em cada categoria?
At-a-glance decision frameworkPontuações 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.
Latência e Tempo de Resposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interativo de Custo de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Mistral Large 2 · Strengths & Key Advantages
- Janela de contexto de 128K com limite máximo de saída de 8K tokens (SWE-bench Verified 38% · GPQA Diamond 54%)
- Taxa de transferência rápida de 75 tokens/segundo com latência inicial (TTFT) de 520 ms
- Pesos abertos (123B) 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
- Para alto volume de requisições, recomenda-se ativar o cache de prompts e a Batch API para reduzir custos
Mistral Medium 3.5 · Strengths & Key Advantages
- Janela de contexto de 262K com limite máximo de saída de 33K tokens (SWE-bench Verified 54% · GPQA Diamond 68%)
- Taxa de transferência rápida de 90 tokens/segundo com latência inicial (TTFT) de 450 ms
- Pesos abertos (Confidential (~80B MoE)) 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 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 |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | 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: Respostas 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.
Veredito
Tanto Mistral Large 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 3.5 (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 3.5 lidera em inteligência geral e programação, Mistral Medium 3.5 oferece geração mais rápida e Mistral Medium 3.5 apresenta o melhor custo-benefício em produção.
Perguntas Frequentes (FAQ)
Qual é melhor para programação e raciocínio, Mistral Large 2 ou Mistral Medium 3.5?+
Nas avaliações de inteligência e código, Mistral Medium 3.5 leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.
Qual modelo tem a API mais barata, Mistral Large 2 ou Mistral Medium 3.5?+
O Mistral Large 2 custa $2.00 / $6.00 por 1M de tokens (entrada/saída) e o Mistral Medium 3.5 custa $0.40 / $2.00. No custo combinado, Mistral Medium 3.5 é mais econômico.
Qual modelo possui maior janela de contexto e velocidade?+
O Mistral Large 2 suporta 128K tokens de contexto contra 262.1K tokens do Mistral Medium 3.5, enquanto Mistral Medium 3.5 entrega maior velocidade de geração.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Fontes oficiais: Documentação oficial de API da Mistral AI e Mistral AI e benchmarks independentes (2026).