Claude Sonnet 4.5 vs Mistral Medium 3.1
Compare Claude Sonnet 4.5 and Mistral Medium 3.1 side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Claude Sonnet 4.5 vs Mistral Medium 3.1: Overview
Claude Sonnet 4.5, released by Anthropic in September 2025, is the company’s most advanced Sonnet-series model, built for high-performance reasoning, coding, and long-horizon agentic workflows. It is a multimodal system that accepts both text and images, with a 200,000-token context window designed for handling large documents and extended interactions. Anthropic highlights its improvements in reliability, reduced sycophancy, and alignment, making it suitable for sustained enterprise use.
The model delivers strong results in coding and autonomous workflows, achieving 61.4% on the OSWorld benchmark and leading performance on SWE-bench Verified. It introduces infrastructure features such as a memory tool (beta), checkpointing for Claude Code, parallel tool use, and tighter integration with VS Code. Compared to Opus, which targets broader reasoning, Sonnet 4.5 is optimized for structured, long-duration tasks. Positioned against leading offerings from OpenAI and Google, it is aimed at enterprise automation, software engineering, and research-intensive applications.
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 4.5 vs Mistral Medium 3.1 Comparison Table
| Property | Claude Sonnet 4.5 | Mistral Medium 3.1 |
|---|---|---|
| Organization | Anthropic | Mistral |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2025 | Aug 2025 |
| Context Window | 200K | 128K |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $3.00 | $0.400 |
| Output $/1M | $15.00 | $2.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Classification | Demo | |
| Object Detection | Demo | |
| Model Features | ||
| Multimodal Vision | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 59.7% | |
| Avg Response Time | 5.67s | |
| Median input tokensincl. image tokens | 2.2K | |
| Median output tokens | 182 | |
| Est. cost / taskon this benchmark | $0.0092 | |
| Defect Detection | 73.3%(11/15) | |
| Document Understanding | 77.8%(7/9) | |
| Object Counting | 10%(1/10) | |
| Object Understanding | 64.3%(9/14) | |
| Spatial Understanding | 63.2%(12/19) | |
| OCR | ||
| Overall Score | 67.25% | |
| Avg Response Time | 3.93s | |
| Median input tokensincl. image tokens | 735 | |
| Median output tokens | 115 | |
| Est. cost / taskon this benchmark | $0.0039 | |
| Focused Scene OCR | 71.7%(71/99) | |
| Handwritten Math | 20%(2/10) | |
| License Plate Recognition | 53.3%(16/30) | |
| Text Recognition | 66.7%(20/30) | |
| VQA & Extraction | 75%(45/60) | |
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