Claude Sonnet 5.5 vs Gemini 3.1 Pro
Compare Claude Sonnet 5.5 and Gemini 3.1 Pro side-by-side. See how these vision models stack up in Image Captioning, OCR, Classification, Object Detection, and Open Prompt.
Compare Claude Sonnet 5.5 vs Gemini 3.1 Pro live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Detect and compare bounding boxes across models on the same image.
Upload an image
Drag and drop an image here, or click to browse
Models in this comparison
Claude Sonnet 5.5 vs Gemini 3.1 Pro on Vision Evals
Claude Sonnet 5.5 scores higher on 3 of the five Vision Evals tasks.
The widest gap is Identification, where Gemini 3.1 Pro leads 100.0% to 91.7%.
Overall, Claude Sonnet 5.5 averages 77.6% (#10 of 63) against 76.8% (#13 of 63) for Gemini 3.1 Pro.
Claude Sonnet 5.5 is both cheaper ($0.0073 vs $0.010 per sample) and faster (8.3s vs 8.4s per sample).
Claude Sonnet 5.5 vs Gemini 3.1 Pro Comparison Table
Evals updated October 13, 2026Pricing updated October 17, 2026
| Property | Claude Sonnet 5.5 | Gemini 3.1 Pro |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Feb 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $2.00 |
| Output $/1M | $10.00 | $12.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 77.6% | 76.8% |
| Avg cost / sample | $0.0073 | $0.010 |
| Avg speed / sample | 8.33s | 8.44s |
| By task | ||
| Object Detection (low) | 74.3% ±0.9, Mean of 3 runs, range 73.5 to 75.3 | 67.4% |
| Object Detection (high) | 76.8% ±0.4, Mean of 3 runs, range 76.5 to 77.3 | – |
| Counting (low) | 79.3% ±0.7, Mean of 3 runs, range 78.4 to 79.7 | 71.6% |
| Counting (high) | 82.9% ±1.4, Mean of 3 runs, range 81.1 to 83.8 | – |
| Identification (low) | 91.7% ±3.1, Mean of 3 runs, range 87.5 to 93.8 | 100.0% |
| Identification (high) | 90.6% ±0.0, Mean of 3 runs, range 90.6 to 90.6 | – |
| OCR (low) | 66.3% | 72.6% |
| by category |
|
|
| OCR (high) | – | 73.1% |
| by category |
| |
| Reasoning (low) | 76.4% ±0.7, Mean of 3 runs, range 75.5 to 76.8 | 72.2% |
| Reasoning (high) | 83.9% ±1.7, Mean of 3 runs, range 82.1 to 85.4 | 74.8% |
Claude Sonnet 5.5 vs Gemini 3.1 Pro: Overview
Claude Sonnet 5.5 is a proprietary multimodal language model from Anthropic and the second release in the Claude 5.5 family, following Claude Opus 5.5. It accepts interleaved text and image input and returns text, operating with a 1M token context window and a maximum output of 128K tokens per request. The model uses adaptive thinking by default, allocating variable reasoning effort per request rather than exposing a manual extended thinking toggle, and its training data cutoff is June 2026. Anthropic positions it as a faster, lower cost complement to Opus 5.5 for well scoped everyday tasks, bug fixing, and producing documents, slides, and spreadsheets.
On visual and agentic evaluations reported at launch, Sonnet 5.5 scores 61.6% on Chartography, a chart recognition test, compared with 15.6% for Claude Sonnet 5, and 80.1% on OSWorld 2.1, a computer use benchmark measuring screenshot driven control of a desktop environment, compared with 57.0% for Sonnet 5. It reports 70.6% on Terminal-Bench 4.0 for agentic coding. Anthropic describes it as the first Sonnet model able to complete Pokemon Red from screenshots alone, and it generates output more than 30% faster than Sonnet 5 while using fewer tokens for equivalent work.
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.
Frequently Asked Questions
On Roboflow's Vision Evals, Claude Sonnet 5.5 performed better. It scores higher on 3 of the five vision tasks and averages 77.6% (#10 of 63) against 76.8% (#13 of 63) for Gemini 3.1 Pro. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Identification benchmark at low effort, Gemini 3.1 Pro leads with 100.0% against 91.7%. This is the widest gap between the two models across the benchmark's tasks.
Claude Sonnet 5.5 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0073 per sample against $0.010. Claude Sonnet 5.5 is priced at $2.00 per 1M input tokens and $10.00 per 1M output; Gemini 3.1 Pro is priced at $2.00 per 1M input tokens and $12.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Claude Sonnet 5.5 is faster. Across Roboflow's Vision Evals it averaged 8.3s per inference against 8.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.