Mistral Small 3.2 vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Mistral Small 3.2 by Mistral AI against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
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Mistral Small 3.2
Efficient UpgradeRefined 24B open-weights model upgraded with native vision capabilities, 128K context window, and enhanced coding precision.
$0.10 / 1M
$0.30 / 1M
128K tokens
34%
Claude Opus 5.5
Frontier Intelligence TitanAnthropic's most intelligent model for complex reasoning, research, and strategic analysis.
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
Key Differences at a Glance
- Mistral Small 3.2 is 49.7x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 128K tokens).
- Mistral Small 3.2 is faster at 100 tok/s vs 78 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 34.0%).
| Feature | Mistral Small 3.2 | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 43 | 94 |
Provider | Mistral AI | Anthropic |
Input Price Cost per million input tokens | $0.10 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $0.30 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.09 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 128K |
Throughput Speed Tokens generated per second | 100 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 34% | 84.6% |
GPQA Diamond Graduate-level reasoning | 48.5% | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 1M | $0.40 / 1M |
Who Wins Where?
At-a-glance decision frameworkEmpirical Head-to-Head Benchmark Scores
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.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Mistral Small 3.2 · Strengths & Key Advantages
- Expanded 128K context window with native multimodal image understanding
- Fast 100 tok/s execution speed on commodity hardware
- Permissive open weights for on-premise deployments
Considerations & Trade-offs
- Image resolution capped compared to large vision models
- Lacks deep autonomous chain-of-thought
Claude Opus 5.5 · Strengths & Key Advantages
- Unmatched autonomous multi-step agentic coding and refactoring
- Flawless multi-document reasoning over 1M token context with no long-prompt surcharge
- Highest composite Intelligence Index score measured (94/100), leading SWE-bench Verified at 84.6%
- Precise tool execution with robust error-recovery loops
Considerations & Trade-offs
- Premium output pricing ($20.00/M) plus heavy reasoning-token usage (~68K output tokens per complex task) keeps long agentic runs expensive
- Long thinking times at maximum effort can push a single hard agentic task toward ~$3.85+
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Mistral Small 3.2 | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.09 | $3.85 |
Output Tokens / Task Tokens generated per task | 25K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 42K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Mistral Small 3.2 | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | mistral-small-3-2-latest | claude-opus-5-5-20260901 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-06 | 2026-09 |
| Knowledge Cutoff | April 2025 | June 2026 |
| Architecture | Compact Vision-Language Transformer | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | 24B | 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 ($0.1/1M in) across full 128K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.05/1M tokens | $0.4/1M 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) · 15 GB VRAM (4-bit Quantized) · 52 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 Small 3.2 vs Claude Opus 5.5: Quick Answers
Which is more intelligent, Mistral Small 3.2 or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 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 78 tokens per second.
Which model is cheaper to run?
Mistral Small 3.2 is cheaper at $0.09 per 1M tokens blended, compared with $3.08 (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 1.40s.
Which model has a larger context window?
Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 128K tokens.
Verdict
Choose Mistral Small 3.2 for high-volume multimodal triage, edge image-text extraction, and responsive chat assistants, or choose Claude Opus 5.5 for large-scale software development agents, enterprise codebases, and mission-critical scientific synthesis.
Frequently asked questions
Which is cheaper: Mistral Small 3.2 or Claude Opus 5.5?+
Mistral Small 3.2 costs $0.1/1M input and $0.3/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.
Which model has a larger context window: Mistral Small 3.2 or Claude Opus 5.5?+
Mistral Small 3.2 supports 128K tokens whereas Claude Opus 5.5 supports 1M tokens.
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.
Sources: Mistral AI & Anthropic Official Documentation