Gemini 3.7 Flash vs GPT-5.4 Mini
Compare Gemini 3.7 Flash and GPT-5.4 Mini side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, OCR, and Object Detection.
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Models in this comparison
Gemini 3.7 Flash vs GPT-5.4 Mini on Vision Evals
Gemini 3.7 Flash scores higher on 5 of the six Vision Evals tasks.
The widest gap is Object Detection, where Gemini 3.7 Flash leads 70.5% to 15.8%.
Overall, Gemini 3.7 Flash averages 85.2% (#3 of 53) against 64.7% (#38 of 53) for GPT-5.4 Mini.
GPT-5.4 Mini is both cheaper ($0.0030 vs $0.0031 per sample) and faster (5.3s vs 16.5s per sample).
Gemini 3.7 Flash vs GPT-5.4 Mini Comparison Table
Evals updated September 5, 2026Pricing updated September 13, 2026
| Property | Gemini 3.7 Flash | GPT-5.4 Mini |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Aug 2026 | Mar 2026 |
| Context Window | 1.0M | 400K |
| Parameters | Undisclosed | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.750 | $0.750 |
| Output $/1M | $3.75 | $4.50 |
| 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 | 85.2% | 64.7% |
| Avg cost / sample | $0.0031 | $0.0030 |
| Avg speed / sample | 16.50s | 5.25s |
| By task | ||
| Object Detection (low) | 70.5% ±1.1, Mean of 3 runs, range 69.4 to 71.5 | 15.8% ±0.4, Mean of 3 runs, range 15.3 to 16.1 |
| Object Detection (high) | 74.3% ±0.8, Mean of 3 runs, range 73.3 to 75.0 | 16.6% ±0.8, Mean of 3 runs, range 15.8 to 17.4 |
| Counting (low) | 78.4% ±1.4, Mean of 3 runs, range 77.0 to 79.7 | 58.6% ±2.0, Mean of 3 runs, range 56.8 to 60.8 |
| Counting (high) | 79.3% ±2.0, Mean of 3 runs, range 77.0 to 81.1 | 64.9% ±2.0, Mean of 3 runs, range 63.5 to 67.6 |
| Identification (low) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 82.3% ±3.1, Mean of 3 runs, range 78.1 to 84.4 |
| OCR (low) | 88.2% ±1.6, Mean of 3 runs, range 86.9 to 90.0 | 89.5% ±1.3, Mean of 3 runs, range 88.1 to 90.6 |
| OCR (high) | 89.0% ±0.8, Mean of 3 runs, range 88.3 to 89.9 | 89.0% ±1.8, Mean of 3 runs, range 87.7 to 91.2 |
| Data Extraction (low) | 96.2% ±0.5, Mean of 3 runs, range 95.9 to 96.9 | 84.2% ±2.1, Mean of 3 runs, range 82.5 to 86.6 |
| Data Extraction (high) | 95.9% ±0.0, Mean of 3 runs, range 95.9 to 95.9 | 82.1% ±2.1, Mean of 3 runs, range 80.4 to 84.5 |
| Reasoning (low) | 80.8% ±2.0, Mean of 3 runs, range 78.8 to 82.8 | 57.0% ±3.3, Mean of 3 runs, range 54.3 to 60.9 |
| Reasoning (high) | 81.9% ±1.3, Mean of 3 runs, range 80.1 to 82.8 | 64.0% ±1.3, Mean of 3 runs, range 62.9 to 65.6 |
Gemini 3.7 Flash vs GPT-5.4 Mini: Overview
Gemini 3.7 Flash is a proprietary multimodal model from Google, positioned in the Flash branch of the Gemini 3 series that trades some of the capacity of the larger Pro models for lower latency and lower cost per token. It accepts interleaved text and image input alongside other modalities handled by the Gemini family and returns text, and it continues the series pattern of exposing a configurable thinking budget so that reasoning effort can be scaled up for harder problems or reduced for high throughput extraction, routing and classification work. The model is announced roughly three weeks after Gemini 3.6 Flash, part of an unusually fast iteration cadence within the Flash line.
Google reports gains concentrated in agentic coding and front end generation, citing a WebDev Arena Elo of 1588 for this release compared with 1538 for the preceding Flash model, and describes it as producing more functional layouts and more feature complete applications in fewer prompts. Weights are not published and the architecture, parameter count and training corpus are undisclosed, consistent with prior Gemini releases. Visual capability follows the Flash lineage, covering image and document understanding, chart and diagram interpretation, text recognition in images, and general visual question answering.
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.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3.7 Flash performed better. It scores higher on 5 of the six vision tasks and averages 85.2% (#3 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.
Yes. On the Vision Evals Object Detection benchmark at low effort, Gemini 3.7 Flash leads with 70.5% 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 $0.0031. Gemini 3.7 Flash is priced at $0.75 per 1M input tokens and $3.75 per 1M output; GPT-5.4 Mini is priced at $0.75 per 1M input tokens and $4.50 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 16.5s. 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 image captioning and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.