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Claude Opus 5.5 vs GPT-5.6 Sol

Compare Claude Opus 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 Opus 5.5
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OpenAIGPT-5.6 Sol
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Models in this comparison

Claude Opus 5.5 vs GPT-5.6 Sol on Vision Evals

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

The widest gap is Reasoning, where Claude Opus 5.5 leads 83.0% to 66.0%.

Overall, Claude Opus 5.5 averages 82.0% (#3 of 63) against 72.4% (#18 of 63) for GPT-5.6 Sol.

Claude Opus 5.5 is both cheaper ($0.015 vs $0.019 per sample) and faster (7.6s vs 12.2s per sample).

Claude Opus 5.5GPT-5.6 Sol

Claude Opus 5.5 vs GPT-5.6 Sol Comparison Table

Evals updated October 13, 2026Pricing updated October 16, 2026

PropertyClaude Opus 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$4.00$4.00
Output $/1M$20.00$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
82.0%
72.4%
Avg cost / sample$0.015$0.019
Avg speed / sample7.63s12.16s
By task
Object Detection (low)
74.5%
±0.5, Mean of 3 runs, range 74.0 to 75.0
$0.021
68.4%
±0.7, Mean of 3 runs, range 67.9 to 69.3
$0.031
Object Detection (high)
76.0%
±1.1, Mean of 3 runs, range 74.7 to 77.0
$0.030
68.4%
±0.8, Mean of 3 runs, range 67.7 to 69.3
$0.071
Counting (low)
80.6%
±2.0, Mean of 3 runs, range 78.4 to 82.4
$0.0081
74.3%
±1.4, Mean of 3 runs, range 73.0 to 75.7
$0.0098
Counting (high)
82.0%
±2.0, Mean of 3 runs, range 79.7 to 83.8
$0.0098
76.1%
±2.0, Mean of 3 runs, range 74.3 to 78.4
$0.016
Identification (low)
93.8%
±0.0, Mean of 3 runs, range 93.8 to 93.8
$0.0058
89.6%
±4.7, Mean of 3 runs, range 84.4 to 93.8
$0.0056
Identification (high)
95.8%
±1.6, Mean of 3 runs, range 93.8 to 96.9
$0.0067
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0060
OCR (low)
78.3%
$0.016
63.6%
$0.019
by category
Single value
67.8%
Transcription
96.7%
Structured JSON
91.5%
Text localization
61.0%
Single value
43.5%
Transcription
91.7%
Structured JSON
82.0%
Text localization
52.2%
OCR (high)
79.9%
$0.023
65.3%
$0.048
by category
Single value
69.6%
Transcription
96.1%
Structured JSON
94.1%
Text localization
61.1%
Single value
46.1%
Transcription
92.3%
Structured JSON
83.9%
Text localization
51.6%
Reasoning (low)
83.0%
±1.0, Mean of 3 runs, range 82.1 to 84.1
$0.0090
66.0%
±2.6, Mean of 3 runs, range 63.6 to 68.9
$0.0086
Reasoning (high)
85.9%
±2.6, Mean of 3 runs, range 82.8 to 88.1
$0.011
71.7%
±1.3, Mean of 3 runs, range 70.2 to 72.8
$0.012

Claude Opus 5.5 vs GPT-5.6 Sol: Overview

Claude Opus 5.5

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. Published results include 89.0% on Chartography with tools and 81.8% on OSWorld 2.0 under partial credit scoring, alongside 48.7% under strict scoring reported in the system card. 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

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 Opus 5.5 performed better. It scores higher on all five vision tasks and averages 82.0% (#3 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 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.