Claude Sonnet 4.6 vs Gemini 3.1 Pro+ 1 other
Compare Claude Sonnet 4.6, Gemini 3.1 Pro, and 1 other vision model side-by-side. Test these models on Image Captioning, Classification, Open Prompt, Object Detection, and OCR in the Playground.
Compare these vision models 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
Model Overviews
Claude Sonnet 4.6 is Anthropic's mid-tier large language model, released February 17, 2026, designed to balance performance, cost, and versatility for professional and developer use. It supports text and vision-based tasks with advanced reasoning, agentic capabilities, and Adaptive Thinking — a mode where the model dynamically scales its internal reasoning depth. A beta context window of up to 1,000,000 tokens (200K standard) enables processing of entire codebases or document collections in a single request. Parameters are undisclosed.
Optimized for coding, computer use, long-context reasoning, agent planning, and knowledge work, Sonnet 4.6 delivers a full generational upgrade over Sonnet 4.5 and approaches Opus 4.5-level performance across many benchmarks at a fraction of the cost. It is the default model on Claude.ai, Claude Cowork, and is available via API and major cloud platforms — making it well suited for production workloads requiring strong reasoning without flagship pricing.
Claude Sonnet 4.6 vs Gemini 3.1 Pro Comparison Table + 1 other
| Property | Claude Sonnet 4.6 | Gemini 3.1 Pro | GPT-5.5 |
|---|---|---|---|
| Organization | Anthropic | OpenAI | |
| Category | closed | closed | closed |
| Modality | multimodal | multimodal | multimodal |
| Release Date | Feb 2026 | Feb 2026 | Apr 2026 |
| Context Window | 1.0M | 1.0M | 1.0M |
| Parameters | |||
| License | Proprietary | Proprietary | Proprietary |
| Pricing per 1M tokens | |||
| Input $/1M | $3.00 | $2.00 | $5.00 |
| Output $/1M | $15.00 | $12.00 | $30.00 |
| Vision Tasks | |||
| Captioning | Demo | Demo | Demo |
| Classification | Demo | Demo | Demo |
| Object Detection | Demo | Demo | Demo |
| OCR | Demo | Demo | Demo |
| Vision Language | |||
| Visual Question Answering | Demo | Demo | Demo |
| Model Features | |||
| LLMs with Vision Capabilities | |||
| Multimodal Vision | |||
| Foundation Vision | |||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | |||
| Visual Understanding | |||
| Overall Score | 70.15% | 75.76% | 77.61% |
| Avg Response Time | 4.24s | 6.13s | 30.12s |
| Median input tokensincl. image tokens | 2.2K | 1.1K | 1.4K |
| Median output tokens | 105 | 11 | 138 |
| Est. cost / taskon this benchmark | $0.0080 | $0.0024 | $0.011 |
| Defect Detection | 80%(12/15) | 73.3%(11/15) | 86.7%(13/15) |
| Document Understanding | 77.8%(7/9) | 88.9%(8/9) | 88.9%(8/9) |
| Object Counting | 30%(3/10) | 44.4%(4/9) | 30%(3/10) |
| Object Understanding | 71.4%(10/14) | 92.9%(13/14) | 92.9%(13/14) |
| Spatial Understanding | 78.9%(15/19) | 73.7%(14/19) | 78.9%(15/19) |
| OCR | |||
| Overall Score | 81.66% | 89.52% | 81.22% |
| Avg Response Time | 3.42s | 3.11s | 5.16s |
| Median input tokensincl. image tokens | 736 | 1.1K | 105 |
| Median output tokens | 85 | 12 | 83 |
| Est. cost / taskon this benchmark | $0.0035 | $0.0024 | $0.0030 |
| Focused Scene OCR | 85.9%(85/99) | 94.9%(94/99) | 77.8%(77/99) |
| Handwritten Math | 50%(5/10) | 90%(9/10) | 40%(4/10) |
| License Plate Recognition | 90%(27/30) | 90%(27/30) | 93.3%(28/30) |
| Text Recognition | 86.7%(26/30) | 86.7%(26/30) | 83.3%(25/30) |
| VQA & Extraction | 73.3%(44/60) | 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