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Claude Sonnet 5 vs GPT-6.1 Sol

Compare Claude Sonnet 5 and GPT-6.1 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-6.1 Sol
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Models in this comparison

Claude Sonnet 5 vs GPT-6.1 Sol on Vision Evals

GPT-6.1 Sol scores higher on all five Vision Evals tasks.

The widest gap is Object Detection, where GPT-6.1 Sol leads 80.8% to 36.1%.

Overall, Claude Sonnet 5 averages 55.3% (#47 of 63) against 81.7% (#5 of 63) for GPT-6.1 Sol.

GPT-6.1 Sol is cheaper ($0.0068 vs $0.0074 per sample), while Claude Sonnet 5 is faster (5.9s vs 12.8s per sample).

Claude Sonnet 5GPT-6.1 Sol

Claude Sonnet 5 vs GPT-6.1 Sol Comparison Table

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

PropertyClaude Sonnet 5GPT-6.1 Sol
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJun 2026Sep 2026
Context Window1.0M1.1M
ParametersUnknownundisclosed
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
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
Promptable Concept SegmentationNot listedDemo
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%
81.7%
Avg cost / sample$0.0074$0.0068
Avg speed / sample5.86s12.82s
By task
Object Detection (low)
36.1%
$0.011
80.8%
±0.1, Mean of 3 runs, range 80.7 to 80.9
$0.010
Object Detection (high)–
81.6%
±0.4, Mean of 3 runs, range 81.1 to 82.0
$0.022
Counting (low)
56.8%
$0.0030
78.8%
±3.4, Mean of 3 runs, range 75.7 to 82.4
$0.0037
Counting (high)–
80.2%
±3.4, Mean of 3 runs, range 77.0 to 83.8
$0.0057
Identification (low)
81.3%
$0.0027
89.6%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0025
Identification (high)–
88.5%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0030
OCR (low)
59.4%
$0.0078
75.8%
$0.0069
by category
Single value
52.2%
Transcription
88.6%
Structured JSON
78.3%
Text localization
13.1%
Single value
58.3%
Transcription
95.7%
Structured JSON
89.5%
Text localization
74.4%
OCR (high)
59.5%
$0.015
75.8%
$0.016
by category
Single value
54.8%
Transcription
88.3%
Structured JSON
76.6%
Text localization
11.6%
Single value
56.5%
Transcription
96.0%
Structured JSON
90.4%
Text localization
76.7%
Reasoning (low)
43.0%
$0.0032
83.7%
±1.3, Mean of 3 runs, range 82.1 to 84.8
$0.0033
Reasoning (high)
43.0%
$0.0043
88.7%
±2.0, Mean of 3 runs, range 87.4 to 91.4
$0.0042

Claude Sonnet 5 vs GPT-6.1 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-6.1 Sol

GPT-6.1 Sol is a reasoning model in OpenAI's GPT-6 series that accepts text and image input and returns text. It is an upgrade to GPT-6 Sol positioned to approach the intelligence of the larger GPT-6 Astra model on agentic coding, computer use, and professional knowledge work. The model exposes an adjustable reasoning effort control, ranging from low settings for simple turns to maximum settings for harder tasks, and can be driven with tool use enabled or disabled. It operates over a context window of roughly one million tokens and emits up to 128,000 output tokens in a single response, which supports long-running agent loops over large codebases and multi-document collections. Audio and video inputs are not supported.

On the visual side, the model is evaluated on GDP.pdf, a benchmark that asks professional questions about complex PDF documents containing tables, charts, diagrams, and fine-print details, and on OSWorld 2.0, which measures agents operating graphical computer applications. OpenAI reports that GPT-6.1 Sol performs on par with or better than GPT-6 Sol across its image input safety evaluations, and that the share of responses containing a factual error at low reasoning effort falls from 11.4 percent to 7.7 percent.

Frequently Asked Questions

On Roboflow's Vision Evals, GPT-6.1 Sol performed better. It scores higher on all five vision tasks and averages 81.7% (#5 of 63) against 55.3% (#47 of 63) 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-6.1 Sol leads with 80.8% against 36.1%. This is the widest gap between the two models across the benchmark's tasks.

GPT-6.1 Sol is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0068 per sample against $0.0074. Claude Sonnet 5 is priced at $2.00 per 1M input tokens and $10.00 per 1M output; GPT-6.1 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 12.8s. 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.