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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.

Compare Claude Sonnet 5.5 vs GPT-5.6 Sol 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 GPT-5.6 Sol on Vision Evals

Claude Sonnet 5.5 scores higher on 5 of the six 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 83.8% (#7 of 60) against 79.0% (#15 of 60) for GPT-5.6 Sol.

Claude Sonnet 5.5 is cheaper ($0.0065 vs $0.0088 per sample), while GPT-5.6 Sol is faster (10.3s vs 10.8s per sample).

Claude Sonnet 5.5GPT-5.6 Sol

Claude Sonnet 5.5 vs GPT-5.6 Sol Comparison Table

Evals updated September 28, 2026Pricing updated September 28, 2026

PropertyClaude Sonnet 5.5GPT-5.6 Sol
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Jul 2026
Context Window1.0M1.5M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$2.00
Output $/1M$10.00
Vision Tasks
CaptioningDemo
Chart Question Answering
ClassificationDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
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%
79.0%
Avg cost / sample$0.0065$0.0088
Avg speed / sample10.78s10.32s
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
$0.015
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
$0.035
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
$0.0049
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
$0.0078
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
$0.0028
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
$0.0030
OCR (low)
90.6%
±0.9, Mean of 3 runs, range 90.0 to 91.7
$0.0079
90.7%
±0.1, Mean of 3 runs, range 90.6 to 90.7
$0.011
OCR (high)
90.9%
±1.5, Mean of 3 runs, range 89.2 to 92.3
$0.011
90.2%
±0.2, Mean of 3 runs, range 90.0 to 90.4
$0.025
Data Extraction (low)
90.7%
±1.5, Mean of 3 runs, range 89.7 to 92.8
$0.0033
84.9%
±1.0, Mean of 3 runs, range 83.5 to 85.6
$0.0033
Data Extraction (high)
93.1%
±0.5, Mean of 3 runs, range 92.8 to 93.8
$0.0036
86.9%
±0.5, Mean of 3 runs, range 86.6 to 87.6
$0.0041
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
$0.0043
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
$0.0061

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 5 of the six vision tasks and averages 83.8% (#7 of 60) against 79.0% (#15 of 60) 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 $0.0065 per sample against $0.0088. Actual costs depend on your image sizes, prompts, and output length.

GPT-5.6 Sol is faster. Across Roboflow's Vision Evals it averaged 10.3s per inference against 10.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.