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GPT-5.4 Mini vs Grok 4.7

Grok 4.7 and GPT-5.4 Mini score 63.5% vs 53.2% averaged across five Vision Evals tasks (#33 and #53 of 65 models), so Grok 4.7 leads overall.

  • Grok 4.7 wins all 5 tasks, with its widest lead on object detection (40.4% vs 15.8%).
  • GPT-5.4 Mini is about 1/6 of the cost: $0.0034 vs $0.020 overall average cost per sample.
  • GPT-5.4 Mini is faster: 5.5s vs 33.3s per sample.

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.

Detect and compare bounding boxes across models on the same image.

Open Object Detection in the full playground
OpenAIGPT-5.4 Mini
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GrokGrok 4.7
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GPT-5.4 Mini vs Grok 4.7 on Vision Evals

Score by taskGPT-5.4 MiniGrok 4.7
Object Detection
15.8
40.4
OCR
51.1
63.6
Reasoning
57.0
64.2
Identification
83.3
87.5
Counting
58.6
61.7

Roboflow Vision Evals. Full breakdown below.

GPT-5.4 Mini vs Grok 4.7 Comparison Table

Evals updated October 26, 2026Pricing updated November 7, 2026

PropertyGPT-5.4 MiniGrok 4.7
OrganizationOpenAISpaceXAI
Categoryclosedclosed
Modalitymultimodal—
Release DateMar 2026Sep 2026
Context Window400K500K
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$0.750$2.00
Output $/1M$4.50$6.00
Vision Tasks
Both support 10 more tasks
Model Features
Foundation VisionSupportedNot listed
LLMs with Vision CapabilitiesSupportedNot listed
Multimodal VisionSupportedNot 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 / sample5.51s33.31s
By task
Object Detection (low)
15.8%
±0.4, Mean of 3 runs, range 15.3 to 16.1
$0.0044
40.4%
±0.6, Mean of 3 runs, range 39.8 to 41.0
$0.021
Object Detection (high)
16.6%
±0.8, Mean of 3 runs, range 15.8 to 17.4
$0.030
41.2%
±1.6, Mean of 3 runs, range 39.6 to 42.8
$0.028
Counting (low)
58.6%
±2.0, Mean of 3 runs, range 56.8 to 60.8
$0.0019
61.7%
±1.3, Mean of 3 runs, range 60.8 to 63.5
$0.011
Counting (high)
64.9%
±2.0, Mean of 3 runs, range 63.5 to 67.6
$0.0073
60.8%
±1.3, Mean of 3 runs, range 59.5 to 62.2
$0.017
Identification (low)
83.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0013
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0063
Identification (high)
82.3%
±3.1, Mean of 3 runs, range 78.1 to 84.4
$0.0055
80.2%
±1.6, Mean of 3 runs, range 78.1 to 81.3
$0.0093
OCR (low)
51.1%
$0.0035
63.6%
$0.023
by category
Single value
41.3%
Transcription
77.3%
Structured JSON
73.6%
Text localization
4.1%
Single value
54.4%
Transcription
89.5%
Structured JSON
86.4%
Text localization
14.8%
OCR (high)
55.8%
$0.027
–
by category
Single value
47.0%
Transcription
78.5%
Structured JSON
79.0%
Text localization
6.1%
Reasoning (low)
57.0%
±3.3, Mean of 3 runs, range 54.3 to 60.9
$0.0022
64.2%
±2.3, Mean of 3 runs, range 62.3 to 66.9
$0.015
Reasoning (high)
64.0%
±1.3, Mean of 3 runs, range 62.9 to 65.6
$0.0096
66.9%
±1.3, Mean of 3 runs, range 65.6 to 68.2
$0.024

GPT-5.4 Mini vs Grok 4.7: Overview

GPT-5.4 Mini

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

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% (#33 of 65) against 53.2% (#53 of 65) 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.