Mistral Medium 3.1 vs Qwen2.5 VL 7B Instruct
Compare Mistral Medium 3.1 and Qwen2.5 VL 7B Instruct side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Mistral Medium 3.1 vs Qwen2.5 VL 7B Instruct: Overview
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.
Qwen2.5-VL-7B-Instruct is a 7-billion parameter vision-language model from Alibaba’s QwenLM team, released on January 26, 2025 under the Apache 2.0 license. It is the instruction-tuned variant of the 7B scale in the Qwen2.5-VL family, designed to process multimodal inputs such as text, images, charts, documents, and video. The model enables structured outputs—including JSON for structured content and bounding boxes for visual localization. Weights are publicly available on Hugging Face and GitHub, making it suitable for both research and applied multimodal use.
Mistral Medium 3.1 vs Qwen2.5 VL 7B Instruct Comparison Table
| Property | Mistral Medium 3.1 | Qwen2.5 VL 7B Instruct |
|---|---|---|
| Organization | Mistral | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Aug 2025 | Jan 2025 |
| Context Window | 128K | 33K |
| Parameters | 7B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.400 | |
| Output $/1M | $2.00 | |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Object Detection | ||
| Model Features | ||
| Multimodal Vision | ||
| LLMs with Vision Capabilities | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Overall Score | 52.24% | |
| Avg Response Time | 47.64s | |
| Defect Detection | 60%(9/15) | |
| Document Understanding | 77.8%(7/9) | |
| Object Counting | 0%(0/10) | |
| Object Understanding | 57.1%(8/14) | |
| Spatial Understanding | 57.9%(11/19) | |