GPT-5.4 Mini vs GPT-6 Astra
Compare GPT-5.4 Mini and GPT-6 Astra side-by-side. See how these vision models stack up in Open Prompt, Object Detection, Classification, Image Captioning, and OCR.
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Models in this comparison
GPT-5.4 Mini vs GPT-6 Astra on Vision Evals
GPT-6 Astra scores higher on all six Vision Evals tasks.
The widest gap is Object Detection, where GPT-6 Astra leads 82.1% to 15.8%.
Overall, GPT-5.4 Mini averages 64.7% (#38 of 53) against 86.6% (#1 of 53) for GPT-6 Astra.
GPT-5.4 Mini is both cheaper ($0.0030 vs $0.030 per sample) and faster (5.3s vs 6.7s per sample).
GPT-5.4 Mini vs GPT-6 Astra Comparison Table
Evals updated September 5, 2026Pricing updated September 5, 2026
| Property | GPT-5.4 Mini | GPT-6 Astra |
|---|---|---|
| Organization | OpenAI | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Mar 2026 | Sep 2026 |
| Context Window | 400K | 1.1M |
| Parameters | Undisclosed | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.750 | $10.00 |
| Output $/1M | $4.50 | $50.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| 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, pooled at low effort | ||
| Overall | 64.7% | 86.6% |
| Avg cost / sample | $0.0030 | $0.030 |
| Avg speed / sample | 5.25s | 6.67s |
| By task | ||
| Object Detection (low) | 15.8% ±0.4, Mean of 3 runs, range 15.3 to 16.1 | 82.1% ±0.8, Mean of 3 runs, range 81.0 to 82.7 |
| Object Detection (high) | 16.6% ±0.8, Mean of 3 runs, range 15.8 to 17.4 | 83.6% ±0.8, Mean of 3 runs, range 82.8 to 84.5 |
| Counting (low) | 58.6% ±2.0, Mean of 3 runs, range 56.8 to 60.8 | 80.2% ±1.4, Mean of 3 runs, range 78.4 to 81.1 |
| Counting (high) | 64.9% ±2.0, Mean of 3 runs, range 63.5 to 67.6 | 81.1% ±1.4, Mean of 3 runs, range 79.7 to 82.4 |
| Identification (low) | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | 82.3% ±3.1, Mean of 3 runs, range 78.1 to 84.4 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | 89.5% ±1.3, Mean of 3 runs, range 88.1 to 90.6 | 91.9% ±0.2, Mean of 3 runs, range 91.6 to 92.1 |
| OCR (high) | 89.0% ±1.8, Mean of 3 runs, range 87.7 to 91.2 | 91.5% ±0.2, Mean of 3 runs, range 91.3 to 91.7 |
| Data Extraction (low) | 84.2% ±2.1, Mean of 3 runs, range 82.5 to 86.6 | 88.7% ±1.0, Mean of 3 runs, range 87.6 to 89.7 |
| Data Extraction (high) | 82.1% ±2.1, Mean of 3 runs, range 80.4 to 84.5 | 91.1% ±1.0, Mean of 3 runs, range 89.7 to 91.8 |
| Reasoning (low) | 57.0% ±3.3, Mean of 3 runs, range 54.3 to 60.9 | 87.2% ±1.0, Mean of 3 runs, range 86.1 to 88.1 |
| Reasoning (high) | 64.0% ±1.3, Mean of 3 runs, range 62.9 to 65.6 | 91.2% ±0.3, Mean of 3 runs, range 90.7 to 91.4 |
GPT-5.4 Mini vs GPT-6 Astra: Overview
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.
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.
OpenAI reports evaluation results across agentic, scientific and security benchmarks, including 96.0% on GPQA Diamond, 64.6% on Terminal-Bench Science, 72.6% on OSWorld 2.0, and a perfect score on ExploitBench, along with near saturation on FrontierMath Tier 4 and ARC-AGI-3. 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.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6 Astra performed better. It scores higher on all six vision tasks and averages 86.6% (#1 of 53) against 64.7% (#38 of 53) for GPT-5.4 Mini. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, GPT-6 Astra leads with 82.1% 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 $0.030. GPT-5.4 Mini is priced at $0.75 per 1M input tokens and $4.50 per 1M output; GPT-6 Astra is priced at $10.00 per 1M input tokens and $50.00 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 6.7s. 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 open prompts and object detection in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.