Claude Sonnet 5.5 vs Gemini 3.1 Pro
Compare Claude Sonnet 5.5 and Gemini 3.1 Pro side-by-side.
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.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
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 six 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 83.8% (#7 of 60) against 83.3% (#8 of 60) for Gemini 3.1 Pro.
Claude Sonnet 5.5 is cheaper ($0.0065 vs $0.0093 per sample), while Gemini 3.1 Pro is faster (7.8s vs 10.8s per sample).
Claude Sonnet 5.5 vs Gemini 3.1 Pro Comparison Table
Evals updated September 28, 2026Pricing updated September 28, 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 | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | |
| Output $/1M | $12.00 | |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 83.8% | 83.3% |
| Avg cost / sample | $0.0065 | $0.0093 |
| Avg speed / sample | 10.78s | 7.81s |
| 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) | 90.6% ±0.9, Mean of 3 runs, range 90.0 to 91.7 | 92.6% |
| OCR (high) | 90.9% ±1.5, Mean of 3 runs, range 89.2 to 92.3 | – |
| Data Extraction (low) | 90.7% ±1.5, Mean of 3 runs, range 89.7 to 92.8 | 95.9% |
| Data Extraction (high) | 93.1% ±0.5, Mean of 3 runs, range 92.8 to 93.8 | – |
| 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 slightly better overall. The two split the six vision tasks 3 to 3, but Claude Sonnet 5.5 averages 83.8% (#7 of 60) against 83.3% (#8 of 60) 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 $0.0065 per sample against $0.0093. Actual costs depend on your image sizes, prompts, and output length.
Gemini 3.1 Pro is faster. Across Roboflow's Vision Evals it averaged 7.8s per inference against 10.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.