Gemma 4 31B vs GPT-5.4 Mini
Gemma 4 31B averages 57.7% across four Vision Evals tasks and GPT-5.4 Mini 53.2% across five (#41 and #53 of 65 models), so Gemma 4 31B leads overall.
- GPT-5.4 Mini wins 3 of the 4 tasks both ran, but the widest gap is object detection, where Gemma 4 31B leads 47.5% vs 15.8%.
- Gemma 4 31B is about half the cost: $0.0015 vs $0.0034 overall average cost per sample.
- GPT-5.4 Mini is faster: 5.5s vs 34.4s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemma 4 31B vs GPT-5.4 Mini on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemma 4 31B vs GPT-5.4 Mini Comparison Table
Evals updated October 26, 2026Pricing updated November 5, 2026
| Property | Gemma 4 31B | GPT-5.4 Mini |
|---|---|---|
| Organization | OpenAI | |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Mar 2026 |
| Context Window | 256K | 400K |
| Parameters | 31B | Unknown |
| License | Apache 2.0 | Proprietary |
| Commercial use | free for commercial use | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.200 | $0.750 |
| Output $/1M | $0.400 | $4.50 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 57.7%* | 53.2% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0015 | $0.0034 |
| Avg speed / sample | 34.36s | 5.51s |
| By task | ||
| Object Detection (low) | 47.5% ±0.6, Mean of 3 runs, range 46.8 to 48.0 | 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) | 51.4% ±2.7, Mean of 3 runs, range 48.6 to 54.0 | 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) | 79.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | 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) | 52.8% ±1.3, Mean of 3 runs, range 51.7 to 54.3 | 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 |
Gemma 4 31B vs GPT-5.4 Mini: Overview
Gemma 4 31B is the largest dense model in Google's Gemma 4 family, built from the same research as Gemini 3 and released as open weights under the Apache 2.0 license. It supports a 256K token context window with text and image input, configurable thinking mode for step-by-step reasoning, and multilingual support across 140+ languages. The unquantized model fits on a single 80GB GPU.
For vision tasks, Gemma 4 31B supports image understanding with variable aspect ratios and resolutions, and can output structured bounding boxes for UI element detection, making it useful for document parsing and UI understanding. Compared to Gemma 3, it delivers stronger reasoning and multimodal performance. It is part of a four-size family alongside the 26B A4B MoE variant and two on-device models (E2B, E4B), with the 31B dense variant optimized for output quality and fine-tuning over inference speed.
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, GPT-5.4 Mini performed better. It scores higher on 3 of the 4 vision tasks and averages 53.2% (#53 of 65) against 57.7% (#41 of 65) for Gemma 4 31B. 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, Gemma 4 31B leads with 47.5% against 15.8%. This is the widest gap between the two models across the benchmark's tasks.
Gemma 4 31B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0015 per sample against $0.0034. Actual costs depend on your image sizes, prompts, and output length.
GPT-5.4 Mini is faster. Across Roboflow's Vision Evals it averaged 5.5s per inference against 34.4s. 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 OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.