Gemini 3.5 Flash vs GPT-5.4 Mini
Gemini 3.5 Flash and GPT-5.4 Mini score 81.8% vs 53.2% averaged across five Vision Evals tasks (#6 and #55 of 67 models), so Gemini 3.5 Flash leads overall.
- Gemini 3.5 Flash wins all 5 tasks, with its widest lead on object detection (70.6% vs 15.8%).
- GPT-5.4 Mini is about a quarter of the cost: $0.0034 vs $0.012 overall average cost per sample.
- GPT-5.4 Mini is faster: 5.5s vs 11.5s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 3.5 Flash vs GPT-5.4 Mini on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 3.5 Flash vs GPT-5.4 Mini Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Gemini 3.5 Flash | GPT-5.4 Mini |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | May 2026 | 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 | $1.50 | $0.750 |
| Output $/1M | $9.00 | $4.50 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 81.8% | 53.2% |
| Avg cost / sample | $0.012 | $0.0034 |
| Avg speed / sample | 11.52s | 5.51s |
| By task | ||
| Object Detection (low) | 70.6% ±2.0, Mean of 3 runs, range 68.7 to 72.6 | 15.8% ±0.4, Mean of 3 runs, range 15.3 to 16.1 |
| Object Detection (high) | 69.8% ±1.8, Mean of 3 runs, range 67.5 to 71.1 | 16.6% ±0.8, Mean of 3 runs, range 15.8 to 17.4 |
| Counting (low) | 80.6% ±0.7, Mean of 3 runs, range 79.7 to 81.1 | 58.6% ±2.0, Mean of 3 runs, range 56.8 to 60.8 |
| Counting (high) | 82.4% ±0.0, Mean of 3 runs, range 82.4 to 82.4 | 64.9% ±2.0, Mean of 3 runs, range 63.5 to 67.6 |
| Identification (low) | 99.0% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | 97.9% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 82.3% ±3.1, Mean of 3 runs, range 78.1 to 84.4 |
| OCR (low) | 76.7% | 51.1% |
| by category |
|
|
| OCR (high) | 75.5% | 55.8% |
| by category |
|
|
| Reasoning (low) | 82.1% ±2.0, Mean of 3 runs, range 80.1 to 84.1 | 57.0% ±3.3, Mean of 3 runs, range 54.3 to 60.9 |
| Reasoning (high) | 81.0% ±1.7, Mean of 3 runs, range 79.5 to 82.8 | 64.0% ±1.3, Mean of 3 runs, range 62.9 to 65.6 |
Gemini 3.5 Flash vs GPT-5.4 Mini: Overview
Gemini 3.5 Flash is a multimodal language model developed by Google DeepMind and released at Google I/O 2026. It is built on the Gemini 3 Flash reasoning foundation and introduces configurable thinking levels (minimal, low, medium, and high) that allow developers to tune the depth of internal reasoning before a response is generated. The model accepts text, image, video, audio, and PDF inputs and produces text output, with a 1 million token context window and up to 65,000 output tokens per request. It is natively multimodal, processing visual inputs alongside text to support tasks such as image captioning, classification, optical character recognition, object detection, and visual grounding, where the model references specific regions within an image or video frame.
Its vision capabilities extend to interpreting UI screenshots, diagrams, charts, and real-world scenes, as well as understanding video and live frame sequences for activity and scene recognition. The model supports combined tool use, including Google Search, URL context, code execution, and custom functions, within a single request, and it uses reasoning context from previous turns when thought signatures are present in the conversation history, enabling persistent multi-turn reasoning chains. Gemini 3.5 Flash carries a knowledge cutoff of January 2026 and is available via the Gemini API, Google AI Studio, Google Antigravity, and the Gemini Enterprise Agent Platform.
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.