Claude Opus 5.5 vs GPT-6 Astra
GPT-6 Astra and Claude Opus 5.5 score 83.4% vs 82.0% averaged across five Vision Evals tasks (#3 and #4 of 65 models), so GPT-6 Astra leads overall.
- Claude Opus 5.5 wins 3 of the 5 tasks, but the widest gap is object detection, where GPT-6 Astra leads 82.1% vs 74.5%.
- Claude Opus 5.5 is about half the cost: $0.015 vs $0.034 overall average cost per sample.
- Claude Opus 5.5 is faster: 7.6s vs 9.9s per sample.
Roboflow Vision Evals. Full breakdown below.
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Claude Opus 5.5 vs GPT-6 Astra on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Claude Opus 5.5 vs GPT-6 Astra Comparison Table
Evals updated October 26, 2026Pricing updated November 7, 2026
| Property | Claude Opus 5.5 | GPT-6 Astra |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Sep 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | Unknown | Undisclosed |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $4.00 | $10.00 |
| Output $/1M | $20.00 | $50.00 |
| Vision Tasks | ||
| Promptable Concept Segmentation | Not listed | Demo |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 82.0% | 83.4% |
| Avg cost / sample | $0.015 | $0.034 |
| Avg speed / sample | 7.63s | 9.94s |
| By task | ||
| Object Detection (low) | 74.5% ±0.5, Mean of 3 runs, range 74.0 to 75.0 | 82.1% ±0.8, Mean of 3 runs, range 81.0 to 82.7 |
| Object Detection (high) | 76.0% ±1.1, Mean of 3 runs, range 74.7 to 77.0 | 83.6% ±0.8, Mean of 3 runs, range 82.8 to 84.5 |
| Counting (low) | 80.6% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 80.2% ±1.4, Mean of 3 runs, range 78.4 to 81.1 |
| Counting (high) | 82.0% ±2.0, Mean of 3 runs, range 79.7 to 83.8 | 81.1% ±1.4, Mean of 3 runs, range 79.7 to 82.4 |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | 95.8% ±1.6, Mean of 3 runs, range 93.8 to 96.9 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | 78.3% | 77.8% |
| by category |
|
|
| OCR (high) | 79.9% | 77.6% |
| by category |
|
|
| Reasoning (low) | 83.0% ±1.0, Mean of 3 runs, range 82.1 to 84.1 | 87.2% ±1.0, Mean of 3 runs, range 86.1 to 88.1 |
| Reasoning (high) | 85.9% ±2.6, Mean of 3 runs, range 82.8 to 88.1 | 91.2% ±0.3, Mean of 3 runs, range 90.7 to 91.4 |
Claude Opus 5.5 vs GPT-6 Astra: Overview
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-6 Astra is a proprietary multimodal reasoning model from OpenAI that accepts text and image input and produces text output. It is positioned as the company's flagship system for long-horizon end-to-end work spanning complex reasoning, software engineering, computer use, browsing, research and document creation. The model exposes a graduated reasoning effort control with low, medium, high, xhigh and max settings, and it accepts a change to that setting partway through a conversation rather than only at request time. It launches as a single tier with no smaller mini or nano variants, carries a context window of roughly 1.05 million tokens with a maximum output of 128,000 tokens, and reports a knowledge cutoff of April 30, 2026.
The model supports computer use, structured outputs, streaming, programmatic tool calling, multi-agent orchestration, prompt caching and persisted reasoning, and it keeps earlier context windows searchable so it can recover requirements or tool outputs from previous turns. OpenAI describes Astra as the first of its models to cross the Critical cybersecurity capability threshold under its Preparedness Framework.