Gemini 3.1 Pro vs Claude Opus 4.7+ 2 others
Compare Gemini 3.1 Pro, Claude Opus 4.7, and 2 other vision models side-by-side. Test these models on Image Captioning, Open Prompt, and OCR in the Playground.
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Model Overviews
Gemini 3.1 Pro is a proprietary multimodal model from Google’s Gemini 3 series, released in early 2026 and designed for advanced reasoning across large multimodal datasets. It accepts text, images, audio, video, and documents, supporting up to a 1-million-token input context with up to 64k output tokens. Compared with Gemini 3 Pro, it improves long-context synthesis and multi-step reasoning, enabling more reliable analysis of large documents, datasets, and software codebases.
The model also advances visual understanding and grounding, allowing it to interpret UI screenshots, diagrams, and real-world scenes while referencing specific regions within images or video. These capabilities make Gemini 3.1 Pro well suited for multimodal workflows involving document processing, interface analysis, robotics research, and complex visual reasoning.
Gemini 3.1 Pro vs Claude Opus 4.7 Comparison Table + 2 others
| Property | Gemini 3.1 Pro | Claude Opus 4.7 | GPT-5.5 | Qwen3.5 9b |
|---|---|---|---|---|
| Organization | Anthropic | OpenAI | Qwen | |
| Category | closed | closed | closed | open |
| Modality | multimodal | multimodal | multimodal | multimodal |
| Release Date | Feb 2026 | Apr 2026 | Apr 2026 | Mar 2026 |
| Context Window | 1.0M | 1.0M | 1.0M | 262K |
| Parameters | 9B | |||
| License | Proprietary | Proprietary | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||||
| Input $/1M | $2.00 | $5.00 | $5.00 | $0.100 |
| Output $/1M | $12.00 | $25.00 | $30.00 | $0.150 |
| Vision Tasks | ||||
| Captioning | Demo | Demo | Demo | Demo |
| Object Detection | Demo | Demo | Demo | |
| OCR | Demo | Demo | Demo | Demo |
| Vision Language | ||||
| Visual Question Answering | Demo | Demo | Demo | Demo |
| Classification | Demo | Demo | Demo | |
| Model Features | ||||
| LLMs with Vision Capabilities | ||||
| Multimodal Vision | ||||
| Foundation Vision | ||||
Vision Evalspass/fail results · 66 prompts Score key:≥75%40–74%<40% | ||||
| Visual Understanding | ||||
| Overall Score | 75.76% | 67.16% | 77.61% | 71.64% |
| Avg Response Time | 6.13s | 4.85s | 30.12s | 8.99s |
| Median input tokensincl. image tokens | 1.1K | 2.4K | 1.4K | |
| Median output tokens | 11 | 110 | 138 | |
| Est. cost / taskon this benchmark | $0.0024 | $0.015 | $0.011 | |
| Defect Detection | 73.3%(11/15) | 73.3%(11/15) | 86.7%(13/15) | 86.7%(13/15) |
| Document Understanding | 88.9%(8/9) | 77.8%(7/9) | 88.9%(8/9) | 66.7%(6/9) |
| Object Counting | 44.4%(4/9) | 20%(2/10) | 30%(3/10) | 30%(3/10) |
| Object Understanding | 92.9%(13/14) | 85.7%(12/14) | 92.9%(13/14) | 71.4%(10/14) |
| Spatial Understanding | 73.7%(14/19) | 68.4%(13/19) | 78.9%(15/19) | 84.2%(16/19) |
| OCR | ||||
| Overall Score | 89.52% | 86.9% | 81.22% | |
| Avg Response Time | 3.11s | 4.19s | 5.16s | |
| Median input tokensincl. image tokens | 1.1K | 969 | 105 | |
| Median output tokens | 12 | 81 | 83 | |
| Est. cost / taskon this benchmark | $0.0024 | $0.0069 | $0.0030 | |
| Focused Scene OCR | 94.9%(94/99) | 88.9%(88/99) | 77.8%(77/99) | |
| Handwritten Math | 90%(9/10) | 80%(8/10) | 40%(4/10) | |
| License Plate Recognition | 90%(27/30) | 93.3%(28/30) | 93.3%(28/30) | |
| Text Recognition | 86.7%(26/30) | 86.7%(26/30) | 83.3%(25/30) | |
| VQA & Extraction | 81.7%(49/60) | 81.7%(49/60) | 86.7%(52/60) | |
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