Gemini 2.5 Flash vs GPT-5.4 Mini
GPT-5.4 Mini: 53.2% average across five Vision Evals tasks (#53 of 65 models). We have not run Gemini 2.5 Flash through Roboflow's Vision Evals, so there is no head-to-head score. Run both on your own image below.
Compare Gemini 2.5 Flash vs GPT-5.4 Mini live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Detect and compare bounding boxes across models on the same image.
Upload an image
Drag and drop an image here, or click to browse
Gemini 2.5 Flash vs GPT-5.4 Mini Comparison Table
Evals updated October 26, 2026Pricing updated November 4, 2026
| Property | Gemini 2.5 Flash | GPT-5.4 Mini |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2025 | Mar 2026 |
| Context Window | 1.0M | 400K |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.300 | $0.750 |
| Output $/1M | $2.50 | $4.50 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 53.2% |
| Avg cost / sample | – | $0.0034 |
| Avg speed / sample | – | 5.51s |
| By task | ||
| Object Detection (low) | – | 15.8% ±0.4, Mean of 3 runs, range 15.3 to 16.1 |
| Object Detection (high) | – | 16.6% ±0.8, Mean of 3 runs, range 15.8 to 17.4 |
| Counting (low) | – | 58.6% ±2.0, Mean of 3 runs, range 56.8 to 60.8 |
| Counting (high) | – | 64.9% ±2.0, Mean of 3 runs, range 63.5 to 67.6 |
| Identification (low) | – | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | – | 82.3% ±3.1, Mean of 3 runs, range 78.1 to 84.4 |
| OCR (low) | – | 51.1% |
| by category |
| |
| OCR (high) | – | 55.8% |
| by category |
| |
| Reasoning (low) | – | 57.0% ±3.3, Mean of 3 runs, range 54.3 to 60.9 |
| Reasoning (high) | – | 64.0% ±1.3, Mean of 3 runs, range 62.9 to 65.6 |
Gemini 2.5 Flash vs GPT-5.4 Mini: Overview
Gemini 2.5 Flash, released on June 17, 2025, is Google DeepMind’s production-ready, efficiency-focused model in the Gemini 2.5 family. It is multimodal, accepting text, images, video, and audio as inputs, with text as the primary output format. The model supports 1 million input tokens and up to 65K output tokens, enabling it to process very large contexts such as books, long video transcripts, or extensive datasets. Its training knowledge extends to January 2025.
Designed as a price-performance leader, Gemini 2.5 Flash balances speed and reasoning power, making it suitable for everyday enterprise and developer use cases without the higher latency and cost of Pro models. It supports advanced workflows like function calling, code execution, search grounding, URL context ingestion, and structured outputs. While efficient and scalable, output length is still limited compared to its input capacity, and multimodal outputs (e.g. image or audio generation) remain restricted to specialized or preview variants.
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.