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Claude Sonnet 5 vs GPT-5.6 Sol

Compare Claude Sonnet 5 and GPT-5.6 Sol side-by-side. See how these vision models stack up in Object Detection, Open Prompt, OCR, Classification, and Image Captioning.

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AnthropicClaude Sonnet 5
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OpenAIGPT-5.6 Sol
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Models in this comparison

Claude Sonnet 5 vs GPT-5.6 Sol on Vision Evals

GPT-5.6 Sol scores higher on all five Vision Evals tasks.

The widest gap is Object Detection, where GPT-5.6 Sol leads 68.4% to 36.1%.

Overall, Claude Sonnet 5 averages 55.3% (#45 of 61) against 72.4% (#16 of 61) for GPT-5.6 Sol.

Claude Sonnet 5 is both cheaper ($0.0074 vs $0.0096 per sample) and faster (5.9s vs 12.2s per sample).

Claude Sonnet 5GPT-5.6 Sol

Claude Sonnet 5 vs GPT-5.6 Sol Comparison Table

Evals updated October 8, 2026Pricing updated October 8, 2026

PropertyClaude Sonnet 5GPT-5.6 Sol
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJun 2026Jul 2026
Context Window1.0M1.5M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$2.00$2.00
Output $/1M$10.00$10.00
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
55.3%
72.4%
Avg cost / sample$0.0074$0.0096
Avg speed / sample5.86s12.16s
By task
Object Detection (low)
36.1%
$0.011
68.4%
±0.7, Mean of 3 runs, range 67.9 to 69.3
$0.015
Object Detection (high)–
68.4%
±0.8, Mean of 3 runs, range 67.7 to 69.3
$0.035
Counting (low)
56.8%
$0.0030
74.3%
±1.4, Mean of 3 runs, range 73.0 to 75.7
$0.0049
Counting (high)–
76.1%
±2.0, Mean of 3 runs, range 74.3 to 78.4
$0.0078
Identification (low)
81.3%
$0.0027
89.6%
±4.7, Mean of 3 runs, range 84.4 to 93.8
$0.0028
Identification (high)–
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0030
OCR (low)
59.4%
$0.0078
63.6%
$0.0095
by category
Single value
52.2%
Transcription
88.6%
Structured JSON
78.3%
Text localization
13.1%
Single value
43.5%
Transcription
91.7%
Structured JSON
82.0%
Text localization
52.2%
OCR (high)
59.5%
$0.015
65.3%
$0.024
by category
Single value
54.8%
Transcription
88.3%
Structured JSON
76.6%
Text localization
11.6%
Single value
46.1%
Transcription
92.3%
Structured JSON
83.9%
Text localization
51.6%
Reasoning (low)
43.0%
$0.0032
66.0%
±2.6, Mean of 3 runs, range 63.6 to 68.9
$0.0043
Reasoning (high)
43.0%
$0.0043
71.7%
±1.3, Mean of 3 runs, range 70.2 to 72.8
$0.0061

Claude Sonnet 5 vs GPT-5.6 Sol: Overview

Claude Sonnet 5

Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models. On benchmarks, Sonnet 5 scores 63.2% on agentic coding and 81.2% on OSWorld, narrowing the gap with Opus 4.8 while remaining at Sonnet-tier pricing.

The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.

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, GPT-5.6 Sol performed better. It scores higher on all five vision tasks and averages 72.4% (#16 of 61) against 55.3% (#45 of 61) for Claude Sonnet 5. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

No. On the Vision Evals Object Detection benchmark at low effort, GPT-5.6 Sol leads with 68.4% against 36.1%. This is the widest gap between the two models across the benchmark's tasks.

Claude Sonnet 5 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0074 per sample against $0.0096. Claude Sonnet 5 is priced at $2.00 per 1M input tokens and $10.00 per 1M output; GPT-5.6 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 is faster. Across Roboflow's Vision Evals it averaged 5.9s 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 object detection and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.