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Claude Opus 5.5 vs GPT-5 Mini

Claude Opus 5.5: 82.0% average across five Vision Evals tasks (#4 of 65 models). We have not run GPT-5 Mini through Roboflow's Vision Evals, so there is no head-to-head score. Run both on your own image below.

Compare Claude Opus 5.5 vs GPT-5 Mini live

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AnthropicClaude Opus 5.5
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OpenAIGPT-5 Mini
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Claude Opus 5.5 vs GPT-5 Mini Comparison Table

Evals updated October 26, 2026Pricing updated November 7, 2026

PropertyClaude Opus 5.5GPT-5 Mini
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Aug 2025
Context Window1.0M400K
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$4.00$0.250
Output $/1M$20.00$2.00
Vision Tasks
Both support 10 more tasks
Model Features
Both support 3 more features
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
82.0%
Not evaluated
Avg cost / sample$0.015–
Avg speed / sample7.63s–
By task
Object Detection (low)
74.5%
±0.5, Mean of 3 runs, range 74.0 to 75.0
$0.021
–
Object Detection (high)
76.0%
±1.1, Mean of 3 runs, range 74.7 to 77.0
$0.030
–
Counting (low)
80.6%
±2.0, Mean of 3 runs, range 78.4 to 82.4
$0.0081
–
Counting (high)
82.0%
±2.0, Mean of 3 runs, range 79.7 to 83.8
$0.0098
–
Identification (low)
93.8%
±0.0, Mean of 3 runs, range 93.8 to 93.8
$0.0058
–
Identification (high)
95.8%
±1.6, Mean of 3 runs, range 93.8 to 96.9
$0.0067
–
OCR (low)
78.3%
$0.016
–
by category
Single value
67.8%
Transcription
96.7%
Structured JSON
91.5%
Text localization
61.0%
OCR (high)
79.9%
$0.023
–
by category
Single value
69.6%
Transcription
96.1%
Structured JSON
94.1%
Text localization
61.1%
Reasoning (low)
83.0%
±1.0, Mean of 3 runs, range 82.1 to 84.1
$0.0090
–
Reasoning (high)
85.9%
±2.6, Mean of 3 runs, range 82.8 to 88.1
$0.011
–

Claude Opus 5.5 vs GPT-5 Mini: 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. 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 Mini

GPT-5 Mini, released by OpenAI on August 7, 2025, is a mid-tier variant of the GPT-5 family that balances cost, speed, and capability. It is multimodal, supporting both text and image inputs, and offers a substantial input context window of ~400,000 tokens with output lengths up to ~128,000 tokens. While less powerful than the full GPT-5, it inherits its safety tuning, instruction-following improvements, and multimodal reasoning, making it a practical choice for developers who need large context handling without the expense of premium models.

GPT-5 Mini is optimized for affordability while retaining strong reasoning performance. Benchmarks show it outperforming earlier models such as GPT-4o on many multimodal and medical VQA tasks, though it lags behind GPT-5 on the most complex problems. Ideal use cases include prototyping, scalable content generation, document analysis, and mid-range reasoning tasks where efficiency and context capacity matter more than top-tier accuracy.