Mistral Medium 3.5 vs GPT-5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Mistral Medium 3.5 da Mistral AI e GPT-5.5 da OpenAI são dois modelos de IA de destaque. O Mistral Medium 3.5 oferece uma janela de contexto de 262.1K tokens a $0.40/$2.00 por milhão de tokens, enquanto o GPT-5.5 oferece 1.05M tokens de contexto a $5.00/$30.00 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.
Troque e Compare Qualquer Modelo
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
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%
GPT-5.5
Modelo Principal de FronteiraO GPT-5.5 da OpenAI é um modelo de IA projetado para raciocínio corporativo de fronteira, programação avançada e cargas de produção, oferecendo janela de contexto de 1.1M, velocidade de 112 tokens/s, índice de inteligência de 84/100 e tarif
$5.00 / 1M
$30.00 / 1M
1.05M tokens
81%
Principais Diferenças em Resumo
- Mistral Medium 3.5 is 13.8x cheaper for API usage (input + output tokens combined).
- GPT-5.5 has a larger context window (1M vs 262K tokens).
- GPT-5.5 is faster at 112 tok/s vs 90 tok/s.
- GPT-5.5 leads on SWE-bench coding (81.0% vs 54.0%).
| Feature | Mistral Medium 3.5 | GPT-5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 55 | 84 |
Provider | Mistral AI | OpenAI |
Input Price Cost per million input tokens | $0.40 / 1M | $5.00 / 1M |
Output Price Cost per million output tokens | $2.00 / 1M | $30.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.35 / 1M | $4.35 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 1.05M tokens~1,575 A4 pages of text |
Max Output Tokens Per single request | 33K | 128K |
Throughput Speed Tokens generated per second | 90 tok/s | 112 tok/s |
SWE-bench Verified Real-world software engineering | 54% | 81% |
GPQA Diamond Graduate-level reasoning | 68% | 80% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.50 / 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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
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 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
GPT-5.5 · Strengths & Key Advantages
- Janela de contexto de 1.1M com limite máximo de saída de 128K tokens (SWE-bench Verified 81% · GPQA Diamond 80%)
- Taxa de transferência rápida de 112 tokens/segundo com latência inicial (TTFT) de 1180 ms
- Economia de API competitiva ($5/$30 por 1M de tokens) com suporte a cache de prompts e saídas JSON estruturadas
Considerations & Trade-offs
- Modelo proprietário em nuvem; não permite download dos pesos para execução local offline
- Para alto volume de requisições, recomenda-se ativar o cache de prompts e a Batch API para reduzir custos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Mistral Medium 3.5 | GPT-5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.38 | $3.90 |
Output Tokens / Task Tokens generated per task | 38K | 64K |
Reasoning Tokens / Task Thinking tokens burned per task | 18K | 37K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | Mistral Medium 3.5 | GPT-5.5 |
|---|---|---|
| Official API Model ID (SDK) | mistral-medium-3-5-latest | gpt-5-5 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-04 | 2026-04 |
| Knowledge Cutoff | February 2026 | December 2025 |
| Architecture | Enterprise Hybrid Reasoning MoE | Default frontier unified reasoning model |
| 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 | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.4/1M in) across full 262K window | Flat rate ($5/1M in) across full 1050K window |
| Prompt Cache Policy & TTL | Cached input at $0.1/1M tokens | Automatic prefix caching at $0.5/1M (no extra cache-write fee) |
| 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 tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| 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) | OpenAI Preparedness Framework Audited & System Card Published |
| 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 GPT-5.5: Respostas Rápidas (FAQ)
Which is more intelligent, Mistral Medium 3.5 or GPT-5.5?
GPT-5.5 is more intelligent. It scores 84 compared with 55 on the composite Intelligence Index.
Which model is faster?
GPT-5.5 is faster, generating 112 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 $4.35 (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 1.18s.
Which model has a larger context window?
GPT-5.5 has a larger context window, supporting 1.05M tokens compared with 262.1K tokens.
Veredito
Tanto 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.) quanto GPT-5.5 (Indicado para raciocínio corporativo de fronteira, programação avançada e cargas de produção, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: GPT-5.5 lidera em inteligência geral e programação, GPT-5.5 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 Medium 3.5 ou GPT-5.5?+
Nas avaliações de inteligência e código, GPT-5.5 leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.
Qual modelo tem a API mais barata, Mistral Medium 3.5 ou GPT-5.5?+
O Mistral Medium 3.5 custa $0.40 / $2.00 por 1M de tokens (entrada/saída) e o GPT-5.5 custa $5.00 / $30.00. No custo combinado, Mistral Medium 3.5 é mais econômico.
Qual modelo possui maior janela de contexto e velocidade?+
O Mistral Medium 3.5 suporta 262.1K tokens de contexto contra 1.05M tokens do GPT-5.5, enquanto GPT-5.5 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
- GPT-5.4 vs. GPT-5.5: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- GPT-5.5 vs. GPT-5.6 Sol: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Mistral Large 2 vs. Mistral Medium 3.5: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Mistral Medium 3.5 vs. Claude Sonnet 5.5: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Mistral Medium 3.5 vs. DeepSeek V4.1-Flash: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Aion-3.0 vs. Aion-Mini: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
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 OpenAI e benchmarks independentes (2026).