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Claude Opus 5 vs GPT-6.1 Sol

GPT-6.1 Sol and Claude Opus 5 score 81.7% vs 70.9% averaged across five Vision Evals tasks (#9 and #25 of 69 models), so GPT-6.1 Sol leads overall.

  • GPT-6.1 Sol wins 4 of the 5 tasks, with its widest lead on object detection (80.8% vs 54.4%).
  • GPT-6.1 Sol is about a third of the cost: $0.0068 vs $0.020 overall average cost per sample.
  • Claude Opus 5 is faster: 8.2s vs 12.8s per sample.
Anthropic

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AnthropicClaude Opus 5
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Claude Opus 5 vs GPT-6.1 Sol on Vision Evals

Score by taskClaude Opus 5GPT-6.1 Sol
Object Detection
54.4
80.8
Reasoning
71.5
83.7
Counting
70.3
78.8
OCR
68.0
75.8
Identification
90.6
89.6

Roboflow Vision Evals. Full breakdown below.

Claude Opus 5 vs GPT-6.1 Sol Comparison Table

Evals updated November 12, 2026Pricing updated November 13, 2026

PropertyClaude Opus 5GPT-6.1 Sol
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2026Sep 2026
Context Window1.0M1.1M
ParametersUnknownundisclosed
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$5.00$2.00
Output $/1M$25.00$10.00
Vision Tasks
Image TaggingNot listedSupported
Promptable Concept SegmentationNot listedDemo
Both support 9 more tasks
Model Features
Both support 3 more features
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
70.9%
81.7%
Avg cost / sample$0.020$0.0068
Avg speed / sample8.21s12.82s
By task
Object Detection (low)
54.4%
$0.027
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)
70.3%
$0.0096
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)
90.6%
$0.0072
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)
68.0%
$0.022
75.8%
$0.0069
by category
Single value
56.1%
Transcription
92.7%
Structured JSON
85.2%
Text localization
40.5%
Single value
58.3%
Transcription
95.7%
Structured JSON
89.5%
Text localization
74.4%
OCR (high)
65.8%
$0.043
75.8%
$0.016
by category
Single value
47.8%
Transcription
85.2%
Structured JSON
88.7%
Text localization
42.6%
Single value
56.5%
Transcription
96.0%
Structured JSON
90.4%
Text localization
76.7%
Reasoning (low)
71.5%
$0.010
83.7%
±1.3, Mean of 3 runs, range 82.1 to 84.8
$0.0033
Reasoning (high)
74.2%
$0.018
88.7%
±2.0, Mean of 3 runs, range 87.4 to 91.4
$0.0042

Claude Opus 5 vs GPT-6.1 Sol: Overview

Claude Opus 5

Claude Opus 5 is a large language model with multimodal vision capabilities developed by Anthropic, released on July 24, 2026 as the fourth model in the Claude 5 family. It sits in the Opus tier of Anthropic's lineup, positioned below the Mythos-class Fable 5 and Mythos 5 models, and is framed by Anthropic as the go-to model for most knowledge work and automation tasks. The model approaches Fable 5's capabilities at a lower cost. It becomes the default model on Claude Max and the strongest model available on Claude Pro. The model ships with a 1 million token context window and an adjustable "effort" parameter that allows users to trade reasoning depth for speed and token savings. Early enterprise customers reported that Opus 5 achieved comparable performance to Opus 4.8's maximum-reasoning mode while generating significantly fewer tokens on average, and demonstrated higher accuracy on financial modeling tasks with fewer tool calls and less time.

Claude Opus 5 supports multimodal inputs including images and text, and is designed for agentic workflows, coding, scientific research, and complex enterprise tasks. Anthropic reports the model scores 10.2 percentage points higher than Opus 4.8 on an internal chemistry benchmark, making it the most capable generally available model for scientific research in the Claude lineup. Cyber classifiers on Opus 5 are designed to intervene approximately 85 percent less often than those on Fable 5, with fallback to Opus 4.8 when a classifier triggers. The model does not retain user data for 30 days, unlike Fable 5. It is available across Anthropic's platforms including Claude Code and Claude Cowork, as well as cloud partners.

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.

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 4 of the five vision tasks and averages 81.7% (#9 of 69) against 70.9% (#25 of 69) for Claude Opus 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 54.4%. 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 $0.020. Claude Opus 5 is priced at $5.00 per 1M input tokens and $25.00 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 Opus 5 is faster. Across Roboflow's Vision Evals it averaged 8.2s 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 image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.