Gemini 2.5 Pro vs Mistral Medium 3.1
Compare Gemini 2.5 Pro 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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Gemini 2.5 Pro vs Mistral Medium 3.1: Overview
Gemini 2.5 Pro, released on June 17, 2025, is Google DeepMind’s most capable model in the Gemini 2.5 family, optimized for deep reasoning, coding, and complex multimodal tasks. It accepts text, images, audio, video, and PDFs as input and outputs text. The model supports 1 million input tokens with an output capacity of up to 65K tokens, enabling large-scale comprehension of datasets, codebases, and technical documents. Its training knowledge extends to January 2025.
Pro outperforms earlier Gemini 2.0 models across benchmarks, including agentic coding tasks where it achieved ~63.8% on SWE-Bench Verified. It supports structured outputs, function calling, code execution, search grounding, and URL context, making it well-suited for enterprise, STEM, and developer workflows. However, it does not currently support image or audio generation in its stable release, and its higher computational cost and latency make it less efficient than Flash or Flash-Lite. It is available via the Gemini API, Google AI Studio, and Vertex AI.
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.
Gemini 2.5 Pro vs Mistral Medium 3.1 Comparison Table
| Property | Gemini 2.5 Pro | Mistral Medium 3.1 |
|---|---|---|
| Organization | Mistral | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2025 | Aug 2025 |
| Context Window | 1.0M | 128K |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | $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 | |
| 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% | ||
| Overall Score | 70.15% | |
| Avg Response Time | 11.87s | |
| Median input tokensincl. image tokens | 294 | |
| Median output tokens | 565 | |
| Est. cost / taskon this benchmark | $0.0060 | |
| Defect Detection | 73.3%(11/15) | |
| Document Understanding | 88.9%(8/9) | |
| Object Counting | 20%(2/10) | |
| Object Understanding | 78.6%(11/14) | |
| Spatial Understanding | 78.9%(15/19) | |
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