GPT-5.4 Mini vs Grok 4.7
Compare GPT-5.4 Mini and Grok 4.7 side-by-side.
Compare GPT-5.4 Mini vs Grok 4.7 live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Models in this comparison
GPT-5.4 Mini vs Grok 4.7 on Vision Evals
Grok 4.7 scores higher on all six 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 64.7% (#39 of 54) against 71.9% (#22 of 54) for Grok 4.7.
GPT-5.4 Mini is both cheaper ($0.0030 vs $0.012 per sample) and faster (5.3s vs 23.6s per sample).
GPT-5.4 Mini vs Grok 4.7 Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 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 | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.750 | $1.60 |
| Output $/1M | $4.50 | $4.80 |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 64.7% | 71.9% |
| Avg cost / sample | $0.0030 | $0.012 |
| Avg speed / sample | 5.25s | 23.55s |
| 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) | 89.5% ±1.3, Mean of 3 runs, range 88.1 to 90.6 | 92.6% ±0.7, Mean of 3 runs, range 92.1 to 93.4 |
| OCR (high) | 89.0% ±1.8, Mean of 3 runs, range 87.7 to 91.2 | 93.5% ±0.3, Mean of 3 runs, range 93.1 to 93.8 |
| Data Extraction (low) | 84.2% ±2.1, Mean of 3 runs, range 82.5 to 86.6 | 84.9% ±2.6, Mean of 3 runs, range 82.5 to 87.6 |
| Data Extraction (high) | 82.1% ±2.1, Mean of 3 runs, range 80.4 to 84.5 | 87.6% ±1.5, Mean of 3 runs, range 86.6 to 89.7 |
| 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 model from SpaceXAI, released on September 21, 2026. It accepts text and images as input and returns text. It extends Grok 4.6 and is listed at the same API price.
Its Vision Evals scores are on the leaderboard. Running it in the Playground is not available yet, because the inference workflow is not ready.
Frequently Asked Questions
On Roboflow's Vision Evals, Grok 4.7 performed better. It scores higher on all six vision tasks and averages 71.9% (#22 of 54) against 64.7% (#39 of 54) 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 $0.0030 per sample against $0.012. 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 $1.60 per 1M input tokens and $4.80 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 23.6s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.