Claude Opus 5.5 vs GPT-5.6 Sol
Claude Opus 5.5 and GPT-5.6 Sol score 82.0% vs 72.4% averaged across five Vision Evals tasks (#4 and #19 of 65 models), so Claude Opus 5.5 leads overall.
- Claude Opus 5.5 wins all 5 tasks, with its widest lead on reasoning (83.0% vs 66.0%).
- Claude Opus 5.5 is cheaper: $0.015 vs $0.019 overall average cost per sample.
- Claude Opus 5.5 is faster: 7.6s vs 12.2s per sample.
Roboflow Vision Evals. Full breakdown below.
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Claude Opus 5.5 vs GPT-5.6 Sol on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Claude Opus 5.5 vs GPT-5.6 Sol Comparison Table
Evals updated October 26, 2026Pricing updated November 7, 2026
| Property | Claude Opus 5.5 | GPT-5.6 Sol |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Jul 2026 |
| Context Window | 1.0M | 1.5M |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $4.00 | $4.00 |
| Output $/1M | $20.00 | $20.00 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 82.0% | 72.4% |
| Avg cost / sample | $0.015 | $0.019 |
| Avg speed / sample | 7.63s | 12.16s |
| By task | ||
| Object Detection (low) | 74.5% ±0.5, Mean of 3 runs, range 74.0 to 75.0 | 68.4% ±0.7, Mean of 3 runs, range 67.9 to 69.3 |
| Object Detection (high) | 76.0% ±1.1, Mean of 3 runs, range 74.7 to 77.0 | 68.4% ±0.8, Mean of 3 runs, range 67.7 to 69.3 |
| Counting (low) | 80.6% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 74.3% ±1.4, Mean of 3 runs, range 73.0 to 75.7 |
| Counting (high) | 82.0% ±2.0, Mean of 3 runs, range 79.7 to 83.8 | 76.1% ±2.0, Mean of 3 runs, range 74.3 to 78.4 |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 89.6% ±4.7, Mean of 3 runs, range 84.4 to 93.8 |
| Identification (high) | 95.8% ±1.6, Mean of 3 runs, range 93.8 to 96.9 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | 78.3% | 63.6% |
| by category |
|
|
| OCR (high) | 79.9% | 65.3% |
| by category |
|
|
| Reasoning (low) | 83.0% ±1.0, Mean of 3 runs, range 82.1 to 84.1 | 66.0% ±2.6, Mean of 3 runs, range 63.6 to 68.9 |
| Reasoning (high) | 85.9% ±2.6, Mean of 3 runs, range 82.8 to 88.1 | 71.7% ±1.3, Mean of 3 runs, range 70.2 to 72.8 |
Claude Opus 5.5 vs GPT-5.6 Sol: Overview
Claude Opus 5.5 is a proprietary multimodal reasoning model from Anthropic and the first entry in the Claude 5.5 family. It accepts interleaved text and image input and returns text, with a one million token context window and up to 128,000 output tokens per response. Adaptive thinking is always enabled on this model and cannot be disabled; thinking depth is instead governed by an effort parameter with five levels, where medium is the default, a change from the high default used by Claude Opus 5 and earlier Opus models. Anthropic reports a knowledge cutoff of June 2026.
On the visual side, Anthropic characterizes Opus 5.5 as its strongest Opus release for vision and computer use, describing improved reading of dense documents, charts, screenshots, and diagrams for document extraction and visual analysis tasks. The accompanying system card states that Opus 5.5 scored higher than Opus 5 on every evaluation in its capability summary, with the largest gains concentrated in agentic coding, visual reasoning, computer use, and long-horizon knowledge work. The model ships with safety classifiers covering biology and cybersecurity that can route blocked requests to earlier Claude models.
GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 family, which also includes Terra (a balanced everyday-work tier) and Luna (a fast, cost-efficient tier). Sol is designed for demanding reasoning, long-horizon agentic workflows, software engineering, computer use, scientific research, and cybersecurity tasks. It introduces two new capability modes: a "max" reasoning effort setting that allocates additional compute time for difficult problems, and an "ultra" mode that coordinates multiple subagents in parallel to accelerate complex, multi-step work. The model supports native multimodal input, allowing it to process screenshots, diagrams, charts, documents, and photographs alongside text. A reported context window of approximately 1.5 million tokens enables processing of large codebases, lengthy research documents, and extended agentic sessions.
GPT-5.6 Sol was announced on June 26, 2026, initially in a limited preview for trusted partners, and reached general availability on July 9, 2026. On the Agents' Last Exam benchmark, which evaluates long-running professional workflows across 55 fields, Sol scores 53.6. The model also demonstrates gains in life sciences evaluations, including long-horizon genomics and quantitative biology analyses. OpenAI paired the release with its most extensive safety evaluation to date, combining human red teaming with large-scale automated testing, and classified Sol as High capability in both cybersecurity and biological risk under its Preparedness Framework, though it does not cross the Critical threshold in either category.
Frequently Asked Questions
On Roboflow's Vision Evals, Claude Opus 5.5 performed better. It scores higher on all five vision tasks and averages 82.0% (#4 of 65) against 72.4% (#19 of 65) for GPT-5.6 Sol. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Reasoning benchmark at low effort, Claude Opus 5.5 leads with 83.0% against 66.0%. This is the widest gap between the two models across the benchmark's tasks.
Claude Opus 5.5 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.015 per sample against $0.019. Claude Opus 5.5 is priced at $4.00 per 1M input tokens and $20.00 per 1M output; GPT-5.6 Sol is priced at $4.00 per 1M input tokens and $20.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Claude Opus 5.5 is faster. Across Roboflow's Vision Evals it averaged 7.6s per inference against 12.2s. 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.