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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.

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

Claude Opus 5.5 scores higher on 5 of the six 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 85.5% (#3 of 57) against 79.0% (#14 of 57) for GPT-5.6 Sol.

GPT-5.6 Sol is both cheaper ($0.0088 vs $0.014 per sample) and faster (10.3s vs 12.8s per sample).

Claude Opus 5.5GPT-5.6 Sol

Claude Opus 5.5 vs GPT-5.6 Sol Comparison Table

Evals updated September 22, 2026Pricing updated September 22, 2026

PropertyClaude Opus 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
85.5%
79.0%
Avg cost / sample$0.014$0.0088
Avg speed / sample12.76s10.32s
By task
Object Detection (low)
74.4%
±0.5, Mean of 3 runs, range 73.9 to 74.8
$0.022
68.4%
±0.7, Mean of 3 runs, range 67.9 to 69.3
$0.015
Object Detection (high)
76.8%
±1.2, Mean of 3 runs, range 75.4 to 77.8
$0.030
68.4%
±0.8, Mean of 3 runs, range 67.7 to 69.3
$0.035
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
$0.0049
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
$0.0078
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
$0.0028
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
$0.0030
OCR (low)
87.8%
±0.6, Mean of 3 runs, range 87.0 to 88.2
$0.017
90.7%
±0.1, Mean of 3 runs, range 90.6 to 90.7
$0.011
OCR (high)
87.2%
±0.6, Mean of 3 runs, range 86.5 to 87.8
$0.024
90.2%
±0.2, Mean of 3 runs, range 90.0 to 90.4
$0.025
Data Extraction (low)
93.5%
±0.5, Mean of 3 runs, range 92.8 to 93.8
$0.0066
84.9%
±1.0, Mean of 3 runs, range 83.5 to 85.6
$0.0033
Data Extraction (high)
93.5%
±0.5, Mean of 3 runs, range 92.8 to 93.8
$0.0075
86.9%
±0.5, Mean of 3 runs, range 86.6 to 87.6
$0.0041
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
$0.0043
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
$0.0061

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 5 of the six vision tasks and averages 85.5% (#3 of 57) against 79.0% (#14 of 57) 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.

GPT-5.6 Sol is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0088 per sample against $0.014. 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 12.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.