Claude Sonnet 5.5 vs GPT-6.1 Sol
GPT-6.1 Sol and Claude Sonnet 5.5 score 81.7% vs 77.6% averaged across five Vision Evals tasks (#9 and #14 of 69 models), so GPT-6.1 Sol leads overall.
- GPT-6.1 Sol wins 3 of the 5 tasks, with its widest lead on OCR (75.8% vs 66.3%).
- GPT-6.1 Sol is cheaper: $0.0068 vs $0.0073 overall average cost per sample.
- Claude Sonnet 5.5 is faster: 8.3s vs 12.8s per sample.
Roboflow Vision Evals. Full breakdown below.
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Claude Sonnet 5.5 vs GPT-6.1 Sol on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Claude Sonnet 5.5 vs GPT-6.1 Sol Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | Claude Sonnet 5.5 | GPT-6.1 Sol |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Sep 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | Unknown | undisclosed |
| 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 | $10.00 |
| Vision Tasks | ||
| Promptable Concept Segmentation | Not listed | Demo |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 77.6% | 81.7% |
| Avg cost / sample | $0.0073 | $0.0068 |
| Avg speed / sample | 8.33s | 12.82s |
| By task | ||
| Object Detection (low) | 74.3% ±0.9, Mean of 3 runs, range 73.5 to 75.3 | 80.8% ±0.1, Mean of 3 runs, range 80.7 to 80.9 |
| Object Detection (high) | 76.8% ±0.4, Mean of 3 runs, range 76.5 to 77.3 | 81.6% ±0.4, Mean of 3 runs, range 81.1 to 82.0 |
| Counting (low) | 79.3% ±0.7, Mean of 3 runs, range 78.4 to 79.7 | 78.8% ±3.4, Mean of 3 runs, range 75.7 to 82.4 |
| Counting (high) | 82.9% ±1.4, Mean of 3 runs, range 81.1 to 83.8 | 80.2% ±3.4, Mean of 3 runs, range 77.0 to 83.8 |
| Identification (low) | 91.7% ±3.1, Mean of 3 runs, range 87.5 to 93.8 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | 90.6% ±0.0, Mean of 3 runs, range 90.6 to 90.6 | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | 66.3% | 75.8% |
| by category |
|
|
| OCR (high) | – | 75.8% |
| by category |
| |
| Reasoning (low) | 76.4% ±0.7, Mean of 3 runs, range 75.5 to 76.8 | 83.7% ±1.3, Mean of 3 runs, range 82.1 to 84.8 |
| Reasoning (high) | 83.9% ±1.7, Mean of 3 runs, range 82.1 to 85.4 | 88.7% ±2.0, Mean of 3 runs, range 87.4 to 91.4 |
Claude Sonnet 5.5 vs GPT-6.1 Sol: 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.
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.
GPT-6.1 Sol is a reasoning model in OpenAI's GPT-6 series that accepts text and image input and returns text. It is an upgrade to GPT-6 Sol positioned to approach the intelligence of the larger GPT-6 Astra model on agentic coding, computer use, and professional knowledge work. The model exposes an adjustable reasoning effort control, ranging from low settings for simple turns to maximum settings for harder tasks, and can be driven with tool use enabled or disabled. It operates over a context window of roughly one million tokens and emits up to 128,000 output tokens in a single response, which supports long-running agent loops over large codebases and multi-document collections. Audio and video inputs are not supported.
OpenAI reports that GPT-6.1 Sol performs on par with or better than GPT-6 Sol across its image input safety evaluations, and that the share of responses containing a factual error at low reasoning effort falls from 11.4 percent to 7.7 percent.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6.1 Sol performed better. It scores higher on 3 of the five vision tasks and averages 81.7% (#9 of 69) against 77.6% (#14 of 69) for Claude Sonnet 5.5. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals OCR benchmark at low effort, GPT-6.1 Sol leads with 75.8% against 66.3%. This is the widest gap between the two models across the benchmark's tasks.
GPT-6.1 Sol is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0068 per sample against $0.0073. Claude Sonnet 5.5 is priced at $2.00 per 1M input tokens and $10.00 per 1M output; GPT-6.1 Sol is priced at $2.00 per 1M input tokens and $10.00 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 12.8s. 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.