Gemini 3 Flash vs GPT-5.4 Mini
Compare Gemini 3 Flash and GPT-5.4 Mini side-by-side. See how these vision models stack up in Object Detection, Classification, Open Prompt, OCR, and Image Captioning.
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Gemini 3 Flash vs GPT-5.4 Mini: Overview
Gemini 3 Flash is a proprietary multimodal large language model developed by Google through Google DeepMind, designed to deliver fast, cost-efficient reasoning across real-time products and developer workflows. Released in December 2025, it is the Flash-tier variant of the Gemini 3 family, balancing low latency with reasoning quality approaching Pro models.
The model supports text, images, audio, and video, with an exceptionally large context window of roughly one million input tokens and outputs up to ~65k tokens. It emphasizes rapid responses for coding, summarization, analysis, and agentic tasks, and exposes configurable “thinking levels” via API to trade speed for deeper reasoning. Today, Gemini 3 Flash positions itself as a high-throughput, production-ready model, serving as the default in the Gemini app and Google Search’s AI Mode, optimized for scalable, interactive AI applications.
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.
Gemini 3 Flash vs GPT-5.4 Mini Comparison Table
| Property | Gemini 3 Flash | GPT-5.4 Mini |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Dec 2025 | Mar 2026 |
| Context Window | 1.0M | 400K |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.500 | $0.750 |
| Output $/1M | $3.00 | $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 Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 74.63% | 77.61% |
| Avg Response Time | 9.85s | 5.80s |
| Median input tokensincl. image tokens | 1.1K | 1.4K |
| Median output tokens | 290 | 104 |
| Est. cost / taskon this benchmark | $0.0014 | $0.0015 |
| Defect Detection | 73.3%(11/15) | 73.3%(11/15) |
| Document Understanding | 88.9%(8/9) | 88.9%(8/9) |
| Object Counting | 30%(3/10) | 40%(4/10) |
| Object Understanding | 85.7%(12/14) | 92.9%(13/14) |
| Spatial Understanding | 84.2%(16/19) | 84.2%(16/19) |
| OCR | ||
| Overall Score | 93.01% | 77.29% |
| Avg Response Time | 12.40s | 3.24s |
| Median input tokensincl. image tokens | 1.1K | 105 |
| Median output tokens | 160 | 126 |
| Est. cost / taskon this benchmark | $0.0010 | $0.0006 |
| Focused Scene OCR | 94.9%(94/99) | 75.8%(75/99) |
| Handwritten Math | 100%(10/10) | 40%(4/10) |
| License Plate Recognition | 100%(30/30) | 86.7%(26/30) |
| Text Recognition | 86.7%(26/30) | 73.3%(22/30) |
| VQA & Extraction | 88.3%(53/60) | 83.3%(50/60) |
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