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Claude Sonnet 5.5 vs GPT-5.6 Sol

Compare Claude Sonnet 5.5 and GPT-5.6 Sol side-by-side. See how these vision models stack up in Image Captioning, OCR, Classification, Object Detection, and Open Prompt.

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AnthropicClaude Sonnet 5.5
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OpenAIGPT-5.6 Sol
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Models in this comparison

Claude Sonnet 5.5 vs GPT-5.6 Sol on Vision Evals

Claude Sonnet 5.5 scores higher on all five Vision Evals tasks.

The widest gap is Reasoning, where Claude Sonnet 5.5 leads 76.4% to 66.0%.

Overall, Claude Sonnet 5.5 averages 77.6% (#10 of 63) against 72.4% (#18 of 63) for GPT-5.6 Sol.

Claude Sonnet 5.5 is both cheaper ($0.0073 vs $0.019 per sample) and faster (8.3s vs 12.2s per sample).

Claude Sonnet 5.5GPT-5.6 Sol

Claude Sonnet 5.5 vs GPT-5.6 Sol Comparison Table

Evals updated October 13, 2026Pricing updated October 17, 2026

PropertyClaude Sonnet 5.5GPT-5.6 Sol
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Jul 2026
Context Window1.0M1.5M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$2.00$4.00
Output $/1M$10.00$20.00
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoDemo
OCRDemoDemo
Vision LanguageSupportedSupported
Visual Question AnsweringDemoDemo
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
77.6%
72.4%
Avg cost / sample$0.0073$0.019
Avg speed / sample8.33s12.16s
By task
Object Detection (low)
74.3%
±0.9, Mean of 3 runs, range 73.5 to 75.3
$0.0098
68.4%
±0.7, Mean of 3 runs, range 67.9 to 69.3
$0.031
Object Detection (high)
76.8%
±0.4, Mean of 3 runs, range 76.5 to 77.3
$0.014
68.4%
±0.8, Mean of 3 runs, range 67.7 to 69.3
$0.071
Counting (low)
79.3%
±0.7, Mean of 3 runs, range 78.4 to 79.7
$0.0042
74.3%
±1.4, Mean of 3 runs, range 73.0 to 75.7
$0.0098
Counting (high)
82.9%
±1.4, Mean of 3 runs, range 81.1 to 83.8
$0.0053
76.1%
±2.0, Mean of 3 runs, range 74.3 to 78.4
$0.016
Identification (low)
91.7%
±3.1, Mean of 3 runs, range 87.5 to 93.8
$0.0029
89.6%
±4.7, Mean of 3 runs, range 84.4 to 93.8
$0.0056
Identification (high)
90.6%
±0.0, Mean of 3 runs, range 90.6 to 90.6
$0.0033
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0060
OCR (low)
66.3%
$0.0075
63.6%
$0.019
by category
Single value
48.7%
Transcription
76.3%
Structured JSON
83.8%
Text localization
60.6%
Single value
43.5%
Transcription
91.7%
Structured JSON
82.0%
Text localization
52.2%
OCR (high)–
65.3%
$0.048
by category
Single value
46.1%
Transcription
92.3%
Structured JSON
83.9%
Text localization
51.6%
Reasoning (low)
76.4%
±0.7, Mean of 3 runs, range 75.5 to 76.8
$0.0049
66.0%
±2.6, Mean of 3 runs, range 63.6 to 68.9
$0.0086
Reasoning (high)
83.9%
±1.7, Mean of 3 runs, range 82.1 to 85.4
$0.0061
71.7%
±1.3, Mean of 3 runs, range 70.2 to 72.8
$0.012

Claude Sonnet 5.5 vs GPT-5.6 Sol: Overview

Claude Sonnet 5.5

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.

GPT-5.6 Sol

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. On Terminal-Bench 2.1, which tests command-line agentic coding workflows, Sol Ultra achieves 91.9%. 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 Sonnet 5.5 performed better. It scores higher on all five vision tasks and averages 77.6% (#10 of 63) against 72.4% (#18 of 63) 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 Sonnet 5.5 leads with 76.4% against 66.0%. 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 $0.019. Claude Sonnet 5.5 is priced at $2.00 per 1M input tokens and $10.00 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 Sonnet 5.5 is faster. Across Roboflow's Vision Evals it averaged 8.3s 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.