GPT-5.4 Mini vs Grok 4.7
Compare GPT-5.4 Mini and Grok 4.7 side-by-side. See how these vision models stack up in Open Prompt, Object Detection, Classification, Image Captioning, and OCR.
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Models in this comparison
GPT-5.4 Mini vs Grok 4.7 on Vision Evals
Grok 4.7 scores higher on all five Vision Evals tasks.
The widest gap is Object Detection, where Grok 4.7 leads 40.4% to 15.8%.
Overall, GPT-5.4 Mini averages 53.2% (#49 of 61) against 63.5% (#29 of 61) for Grok 4.7.
GPT-5.4 Mini is both cheaper ($0.0034 vs $0.020 per sample) and faster (5.5s vs 33.3s per sample).
GPT-5.4 Mini vs Grok 4.7 Comparison Table
Evals updated October 8, 2026Pricing updated October 10, 2026
| Property | GPT-5.4 Mini | Grok 4.7 |
|---|---|---|
| Organization | OpenAI | SpaceXAI |
| Category | closed | closed |
| Modality | multimodal | — |
| Release Date | Mar 2026 | Sep 2026 |
| Context Window | 400K | 500K |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.750 | $2.00 |
| Output $/1M | $4.50 | $6.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| LLMs with Vision Capabilities | Supported | Not listed |
| Multimodal Vision | Supported | Not listed |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 53.2% | 63.5% |
| Avg cost / sample | $0.0034 | $0.020 |
| Avg speed / sample | 5.51s | 33.31s |
| By task | ||
| Object Detection (low) | 15.8% ±0.4, Mean of 3 runs, range 15.3 to 16.1 | 40.4% ±0.6, Mean of 3 runs, range 39.8 to 41.0 |
| Object Detection (high) | 16.6% ±0.8, Mean of 3 runs, range 15.8 to 17.4 | 41.2% ±1.6, Mean of 3 runs, range 39.6 to 42.8 |
| Counting (low) | 58.6% ±2.0, Mean of 3 runs, range 56.8 to 60.8 | 61.7% ±1.3, Mean of 3 runs, range 60.8 to 63.5 |
| Counting (high) | 64.9% ±2.0, Mean of 3 runs, range 63.5 to 67.6 | 60.8% ±1.3, Mean of 3 runs, range 59.5 to 62.2 |
| Identification (low) | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| Identification (high) | 82.3% ±3.1, Mean of 3 runs, range 78.1 to 84.4 | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 |
| OCR (low) | 51.1% | 63.6% |
| by category |
|
|
| OCR (high) | 55.8% | – |
| by category |
| |
| Reasoning (low) | 57.0% ±3.3, Mean of 3 runs, range 54.3 to 60.9 | 64.2% ±2.3, Mean of 3 runs, range 62.3 to 66.9 |
| Reasoning (high) | 64.0% ±1.3, Mean of 3 runs, range 62.9 to 65.6 | 66.9% ±1.3, Mean of 3 runs, range 65.6 to 68.2 |
GPT-5.4 Mini vs Grok 4.7: Overview
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.
Grok 4.7 is a proprietary multimodal reasoning model from SpaceXAI that accepts images alongside text and returns text-only output. On visual inputs it supports image captioning, visual question answering, OCR, document and chart question answering, and image classification and tagging, with all results expressed as generated text rather than bounding boxes or masks. Its 500,000 token context window leaves room for several images, long documents, or extended conversations about visual content in a single request.
The model exposes a configurable reasoning effort setting with low, medium, high, and xhigh levels (high by default), letting callers trade latency for the amount of deliberation spent on a prompt, including multi-step questions about an image. Built on a larger base model than Grok 4.6 with extended reinforcement learning on harder tasks, it works longer on difficult problems and checks its own work more carefully at the same serving speed. SpaceXAI's launch materials focus on coding and agentic knowledge work and report no image benchmark results.
Frequently Asked Questions
On Roboflow's Vision Evals, Grok 4.7 performed better. It scores higher on all five vision tasks and averages 63.5% (#29 of 61) against 53.2% (#49 of 61) for GPT-5.4 Mini. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, Grok 4.7 leads with 40.4% 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 $0.0034 per sample against $0.020. GPT-5.4 Mini is priced at $0.75 per 1M input tokens and $4.50 per 1M output; Grok 4.7 is priced at $2.00 per 1M input tokens and $6.00 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 5.5s per inference against 33.3s. 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 open prompts and object detection in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.