GPT-5.4 Mini vs Gemini 3 Flash+ 1 other
Compare GPT-5.4 Mini, Gemini 3 Flash, and 1 other vision model side-by-side. Test these models on Open Prompt, Object Detection, Classification, Image Captioning, and OCR in the Playground.
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Models in this comparison
Model Overviews
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-5.4 Mini vs Gemini 3 Flash Comparison Table + 1 other
| Property | GPT-5.4 Mini | Gemini 3 Flash | Claude Haiku 4.5 |
|---|---|---|---|
| Organization | OpenAI | Anthropic | |
| Category | closed | closed | closed |
| Modality | multimodal | multimodal | multimodal |
| Release Date | Mar 2026 | Dec 2025 | Oct 2025 |
| Context Window | 400K | 1.0M | 200K |
| Parameters | |||
| License | Proprietary | Proprietary | Proprietary |
| Pricing per 1M tokens | |||
| Input $/1M | $0.750 | $0.500 | $1.00 |
| Output $/1M | $4.50 | $3.00 | $5.00 |
| Vision Tasks | |||
| Captioning | Demo | Demo | Demo |
| Classification | Demo | Demo | Demo |
| Object Detection | Demo | Demo | Demo |
| OCR | Demo | Demo | Demo |
| Vision Language | |||
| Visual Question Answering | Demo | Demo | Demo |
| Model Features | |||
| Foundation Vision | |||
| LLMs with Vision Capabilities | |||
| Multimodal Vision | |||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | |||
| Visual Understanding | |||
| Overall Score | 77.61% | 74.63% | 58.21% |
| Avg Response Time | 5.80s | 9.85s | 3.15s |
| Median input tokensincl. image tokens | 1.4K | 1.1K | 2.2K |
| Median output tokens | 104 | 290 | 174 |
| Est. cost / taskon this benchmark | $0.0015 | $0.0014 | $0.0030 |
| Defect Detection | 73.3%(11/15) | 73.3%(11/15) | 80%(12/15) |
| Document Understanding | 88.9%(8/9) | 88.9%(8/9) | 77.8%(7/9) |
| Object Counting | 40%(4/10) | 30%(3/10) | 0%(0/10) |
| Object Understanding | 92.9%(13/14) | 85.7%(12/14) | 71.4%(10/14) |
| Spatial Understanding | 84.2%(16/19) | 84.2%(16/19) | 52.6%(10/19) |
| OCR | |||
| Overall Score | 77.29% | 93.01% | 61.57% |
| Avg Response Time | 3.24s | 12.40s | 2.13s |
| Median input tokensincl. image tokens | 105 | 1.1K | 735 |
| Median output tokens | 126 | 160 | 101 |
| Est. cost / taskon this benchmark | $0.0006 | $0.0010 | $0.0012 |
| Focused Scene OCR | 75.8%(75/99) | 94.9%(94/99) | 61.6%(61/99) |
| Handwritten Math | 40%(4/10) | 100%(10/10) | 20%(2/10) |
| License Plate Recognition | 86.7%(26/30) | 100%(30/30) | 66.7%(20/30) |
| Text Recognition | 73.3%(22/30) | 86.7%(26/30) | 63.3%(19/30) |
| VQA & Extraction | 83.3%(50/60) | 88.3%(53/60) | 65%(39/60) |
Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology