Claude Opus 5.5 vs GPT-5.4 Mini
Compare Claude Opus 5.5 and GPT-5.4 Mini side-by-side.
Compare Claude Opus 5.5 vs GPT-5.4 Mini 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-5.4 Mini on Vision Evals
Claude Opus 5.5 scores higher on 5 of the six Vision Evals tasks.
The widest gap is Object Detection, where Claude Opus 5.5 leads 74.4% to 15.8%.
Overall, Claude Opus 5.5 averages 85.5% (#3 of 57) against 64.7% (#42 of 57) for GPT-5.4 Mini.
GPT-5.4 Mini is both cheaper ($0.0030 vs $0.014 per sample) and faster (5.3s vs 12.8s per sample).
Claude Opus 5.5 vs GPT-5.4 Mini Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | Claude Opus 5.5 | GPT-5.4 Mini |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Mar 2026 |
| Context Window | 1.0M | 400K |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $4.00 | $0.750 |
| Output $/1M | $20.00 | $4.50 |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| 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% | 64.7% |
| Avg cost / sample | $0.014 | $0.0030 |
| Avg speed / sample | 12.76s | 5.25s |
| By task | ||
| Object Detection (low) | 74.4% ±0.5, Mean of 3 runs, range 73.9 to 74.8 | 15.8% ±0.4, Mean of 3 runs, range 15.3 to 16.1 |
| Object Detection (high) | 76.8% ±1.2, Mean of 3 runs, range 75.4 to 77.8 | 16.6% ±0.8, Mean of 3 runs, range 15.8 to 17.4 |
| Counting (low) | 80.6% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 58.6% ±2.0, Mean of 3 runs, range 56.8 to 60.8 |
| Counting (high) | 82.0% ±2.0, Mean of 3 runs, range 79.7 to 83.8 | 64.9% ±2.0, Mean of 3 runs, range 63.5 to 67.6 |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | 95.8% ±1.6, Mean of 3 runs, range 93.8 to 96.9 | 82.3% ±3.1, Mean of 3 runs, range 78.1 to 84.4 |
| OCR (low) | 87.8% ±0.6, Mean of 3 runs, range 87.0 to 88.2 | 89.5% ±1.3, Mean of 3 runs, range 88.1 to 90.6 |
| OCR (high) | 87.2% ±0.6, Mean of 3 runs, range 86.5 to 87.8 | 89.0% ±1.8, Mean of 3 runs, range 87.7 to 91.2 |
| Data Extraction (low) | 93.5% ±0.5, Mean of 3 runs, range 92.8 to 93.8 | 84.2% ±2.1, Mean of 3 runs, range 82.5 to 86.6 |
| Data Extraction (high) | 93.5% ±0.5, Mean of 3 runs, range 92.8 to 93.8 | 82.1% ±2.1, Mean of 3 runs, range 80.4 to 84.5 |
| Reasoning (low) | 83.0% ±1.0, Mean of 3 runs, range 82.1 to 84.1 | 57.0% ±3.3, Mean of 3 runs, range 54.3 to 60.9 |
| Reasoning (high) | 85.9% ±2.6, Mean of 3 runs, range 82.8 to 88.1 | 64.0% ±1.3, Mean of 3 runs, range 62.9 to 65.6 |
Claude Opus 5.5 vs GPT-5.4 Mini: 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. 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-5.4 mini is a fast, cost-efficient model developed by OpenAI and released on March 17, 2026, optimized for high-throughput workloads and subagent orchestration. It supports text and image inputs within a 400,000-token context window, making it ideal for processing extensive visual datasets and large codebases in a single request. Designed for low-latency production environments, the model integrates with key API features including function calling, web search, and tool-based computer use, allowing it to assist in automated workflows that require navigating digital interfaces.
Compared to the previous GPT-5 mini, this version runs more than twice as fast while approaching the performance levels of the flagship GPT-5.4 on reasoning and coding benchmarks. While the larger GPT-5.4 introduces native, state-of-the-art computer-use capabilities, GPT-5.4 mini provides a scalable alternative for interpreting screenshots and reasoning over dense UI layouts. For vision tasks on Playground, it excels at extracting structured information from visual documents and assisting in agentic tasks that involve real-time interpretation of software interfaces alongside text.
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 64.7% (#42 of 57) for GPT-5.4 Mini. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Object Detection benchmark at low effort, Claude Opus 5.5 leads with 74.4% against 15.8%. This is the widest gap between the two models across the benchmark's tasks.
GPT-5.4 Mini is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0030 per sample against $0.014. Claude Opus 5.5 is priced at $4.00 per 1M input tokens and $20.00 per 1M output; GPT-5.4 Mini is priced at $0.75 per 1M input tokens and $4.50 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
GPT-5.4 Mini is faster. Across Roboflow's Vision Evals it averaged 5.3s per inference against 12.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.