Claude Opus 4.8 vs GPT-5.4 Mini
Compare Claude Opus 4.8 and GPT-5.4 Mini side-by-side. See how these vision models stack up in Image Captioning, Classification, OCR, Object Detection, and Open Prompt.
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Models in this comparison
Claude Opus 4.8 vs GPT-5.4 Mini on Vision Evals
Claude Opus 4.8 scores higher on 3 of the six Vision Evals tasks.
The widest gap is Reasoning, where GPT-5.4 Mini leads 78.3% to 60.9%.
Overall, Claude Opus 4.8 averages 64.8% (#15 of 16) against 65.3% (#14 of 16) for GPT-5.4 Mini.
GPT-5.4 Mini is cheaper ($0.0026 vs $0.013 per sample), while Claude Opus 4.8 is faster (4.2s vs 4.5s per sample).
Claude Opus 4.8 vs GPT-5.4 Mini Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | Claude Opus 4.8 | GPT-5.4 Mini |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | May 2026 | Mar 2026 |
| Context Window | 1.0M | 400K |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $0.750 |
| Output $/1M | $25.00 | $4.50 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks | ||
| Overall | 64.8% | 65.3% |
| Object Detection | 18.6% | 3.9% |
| Counting | 52.7% | 60.8% |
| Identification | 75.0% | 78.1% |
| OCR | 93.8% | 88.1% |
| Data Extraction | 87.6% | 82.5% |
| Reasoning | 60.9% | 78.3% |
| Avg cost / sample | $0.013 | $0.0026 |
| Avg speed / sample | 4.2s | 4.5s |
Claude Opus 4.8 vs GPT-5.4 Mini: Overview
Claude Opus 4.8 is Anthropic's most capable generally available large language model, released on May 28, 2026 as an incremental upgrade to Claude Opus 4.7. The model accepts text and image inputs and produces text outputs, with a 1 million token context window on the Claude API, Amazon Bedrock, and Google Cloud Vertex AI (200k tokens on Microsoft Foundry) and up to 128k max output tokens. It uses adaptive thinking and supports adjustable effort tiers — high by default, with extra and max tiers available for more demanding tasks. A fast mode operates at approximately 2.5x standard speed. The model is described by Anthropic as a hybrid reasoning model designed for advanced coding, agentic workflows, long-context reasoning, and professional knowledge work.
Key behavioral improvements over Opus 4.7 include substantially reduced rates of unreported code flaws, improved honesty in self-assessment, and better tool-calling reliability. On Anthropic's Super-Agent benchmark, Opus 4.8 completes every case end-to-end, and it scores 84% on Online-Mind2Web for computer-use and browser-agent tasks. It achieves 88.6% on SWE-bench Verified and 69.2% on SWE-bench Pro. Alongside the model, Anthropic launched Dynamic Workflows in Claude Code (research preview), which enables Claude to orchestrate hundreds of parallel subagents for codebase-scale tasks such as large migrations. The Messages API was also updated to accept mid-task system messages without breaking prompt caching, improving support for long-running agentic pipelines.
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, GPT-5.4 Mini performed slightly better overall. The two split the six vision tasks 3 to 3, but GPT-5.4 Mini averages 65.3% (#14 of 16) against 64.8% (#15 of 16) for Claude Opus 4.8. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Reasoning benchmark, GPT-5.4 Mini leads with 78.3% against 60.9%. 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 $0.0026 per sample against $0.013. Claude Opus 4.8 is priced at $5.00 per 1M input tokens and $25.00 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.
Claude Opus 4.8 is faster. Across Roboflow's Vision Evals it averaged 4.2s per inference against 4.5s. 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 image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.