Mistral Medium 3.5 vs Claude Sonnet 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Mistral Medium 3.5 by Mistral AI and Claude Sonnet 5.5 by Anthropic represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Mistral Medium 3.5 holds a notable pricing advantage for token ingestion. On output generation, Mistral Medium 3.5 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Claude Sonnet 5.5 provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, Claude Sonnet 5.5 leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Mistral Medium 3.5
Modelo de Pesos AbertosO Mistral Medium 3.5 da Mistral AI é um modelo de inteligência artificial com janela de contexto de 262K tokens e preço de API de $0.4/1M na entrada e $2/1M na saída, projetado para programação, raciocínio e automação empresarial.
$0.40 / 1M
$2.00 / 1M
262.1K tokens
54%
Claude Sonnet 5.5
Modelo de Raciocínio ProfundoO Claude Sonnet 5.5 da Anthropic é um modelo de inteligência artificial com janela de contexto de 1M tokens e preço de API de $2.0/1M na entrada e $10.0/1M na saída, projetado para programação, raciocínio e automação empresarial.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.9%
Principais Diferenças em Resumo
- 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%).
| Feature | 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 |
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 Medium 3.5 · Strengths & Key Advantages
- Arquitetura Enterprise Hybrid Reasoning MoE avançada da Mistral AI com contexto de 262K tokens
- Excelente relação custo-benefício ($0.4/1M entrada, $2/1M saída) e baixa latência
- Forte desempenho em benchmarks de código, análise lógica e tarefas multilíngues
Considerations & Trade-offs
- Cargas de trabalho com contexto máximo exigem monitoramento de custos de tokens
- Requer engenharia de prompt adequada para fluxos de trabalho altamente específicos
Claude Sonnet 5.5 · Strengths & Key Advantages
- Arquitetura Adaptive Frontier Multimodal Transformer avançada da Anthropic com contexto de 1M tokens
- Excelente relação custo-benefício ($2.0/1M entrada, $10.0/1M saída) e baixa latência
- Forte desempenho em benchmarks de código, análise lógica e tarefas multilíngues
Considerations & Trade-offs
- Cargas de trabalho com contexto máximo exigem monitoramento de custos de tokens
- Requer engenharia de prompt adequada para fluxos de trabalho altamente específicos
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 |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | 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: Respostas 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.
Veredito
Verdict & Model Selection Recommendation: Choose Mistral Medium 3.5 if your priority is Enterprise intelligence hubs, sovereign multilingual workflows, and multi-turn analytical agents. Its performance in demanding tasks justifies its operational token cost. Choose Claude Sonnet 5.5 if your workflow prioritizes Daily production development, agentic workflows, IDE integrations, and automated code review. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Perguntas Frequentes (FAQ)
How does Mistral Medium 3.5 compare to Claude Sonnet 5.5 in API token pricing and cost per token?+
Mistral Medium 3.5 costs $0.40 per 1M input tokens and $2.00 per 1M output tokens, whereas Claude Sonnet 5.5 costs $2.00 input and $10.00 output. Overall, Mistral Medium 3.5 is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Mistral Medium 3.5 or Claude Sonnet 5.5?+
Mistral Medium 3.5 supports a context window of 262K tokens (~197k words), while Claude Sonnet 5.5 supports 1,000K tokens (~750k words). Therefore, Claude Sonnet 5.5 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Mistral Medium 3.5 or Claude Sonnet 5.5?+
Mistral Medium 3.5 scores 54% on SWE-bench Verified while Claude Sonnet 5.5 scores 82.9%. For autonomous software engineering and multi-file debugging, Claude Sonnet 5.5 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Mistral Medium 3.5 and Claude Sonnet 5.5?+
Mistral Medium 3.5 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude Sonnet 5.5 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Mistral Medium 3.5 and Claude Sonnet 5.5?+
Mistral Medium 3.5 generates approximately 90 tokens per second, while Claude Sonnet 5.5 delivers 105 tokens per second. In latency-sensitive production environments, Claude Sonnet 5.5 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Mistral Medium 3.5 and Claude Sonnet 5.5 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Mistral Medium 3.5 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Claude Sonnet 5.5. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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: GetInfoUs Independent AI Benchmarks & Official API Specs