Claude Sonnet 5 vs Mistral Medium 3.1
Compare Claude Sonnet 5 and Mistral Medium 3.1 side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.
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Claude Sonnet 5 vs Mistral Medium 3.1: Overview
Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models. On benchmarks, Sonnet 5 scores 63.2% on agentic coding and 81.2% on OSWorld, narrowing the gap with Opus 4.8 while remaining at Sonnet-tier pricing.
The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.
Mistral Medium 3.1, released in August 2025 as the mistral-medium-2508 update, is a proprietary frontier model from Mistral AI positioned between smaller open models and high-end closed LLMs. It is multimodal, handling both text and image inputs, with a context window of ~128K tokens.
Compared to Mistral Medium 3.0, the 3.1 release introduces improvements in reasoning, coding, STEM, and enterprise workflows, along with better tone control for conversational and business applications. It is designed for scalable enterprise deployments, including hybrid cloud and on-premises VPC setups. As part of Mistral’s Premier line, Medium 3.1 is a commercial-only offering: while it delivers strong accuracy and performance, trade-offs include higher costs than open-weight models, restricted fine-tuning access, and increased latency/cost for very large contexts.
Claude Sonnet 5 vs Mistral Medium 3.1 Comparison Table
| Property | Claude Sonnet 5 | Mistral Medium 3.1 |
|---|---|---|
| Organization | Anthropic | Mistral |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Aug 2025 |
| Context Window | 1.0M | 128K |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.400 |
| Output $/1M | $10.00 | $2.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Classification | Demo | |
| Document Question Answering | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| Model Features | ||
| Multimodal Vision | ||
| LLMs with Vision Capabilities | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 70.15% | |
| Avg Response Time | 3.90s | |
| Median input tokensincl. image tokens | 2.1K | |
| Median output tokens | 61 | |
| Est. cost / taskon this benchmark | $0.0048 | |
| Defect Detection | 73.3%(11/15) | |
| Document Understanding | 66.7%(6/9) | |
| Object Counting | 20%(2/10) | |
| Object Understanding | 92.9%(13/14) | |
| Spatial Understanding | 78.9%(15/19) | |
| OCR | ||
| Overall Score | 83.84% | |
| Avg Response Time | 2.77s | |
| Median input tokensincl. image tokens | 642 | |
| Median output tokens | 64 | |
| Est. cost / taskon this benchmark | $0.0019 | |
| Focused Scene OCR | 88.9%(88/99) | |
| Handwritten Math | 50%(5/10) | |
| License Plate Recognition | 90%(27/30) | |
| Text Recognition | 80%(24/30) | |
| VQA & Extraction | 80%(48/60) | |
Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology