GPT-5.4 Mini vs GPT-6 Sol
GPT-6 Sol and GPT-5.4 Mini score 76.9% vs 53.2% averaged across five Vision Evals tasks (#13 and #53 of 65 models), so GPT-6 Sol leads overall.
- GPT-6 Sol wins all 5 tasks, with its widest lead on object detection (74.7% vs 15.8%).
- GPT-5.4 Mini is about half the cost: $0.0034 vs $0.0074 overall average cost per sample.
- GPT-5.4 Mini is faster: 5.5s vs 10.2s per sample.
Roboflow Vision Evals. Full breakdown below.
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GPT-5.4 Mini vs GPT-6 Sol on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-5.4 Mini vs GPT-6 Sol Comparison Table
Evals updated October 26, 2026Pricing updated November 7, 2026
| Property | GPT-5.4 Mini | GPT-6 Sol |
|---|---|---|
| Organization | OpenAI | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Mar 2026 | Sep 2026 |
| Context Window | 400K | 1.1M |
| Parameters | Unknown | undisclosed |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.750 | $2.00 |
| Output $/1M | $4.50 | $10.00 |
| Vision Tasks | ||
| Promptable Concept Segmentation | Not listed | Demo |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 53.2% | 76.9% |
| Avg cost / sample | $0.0034 | $0.0074 |
| Avg speed / sample | 5.51s | 10.20s |
| By task | ||
| Object Detection (low) | 15.8% ±0.4, Mean of 3 runs, range 15.3 to 16.1 | 74.7% ±0.7, Mean of 3 runs, range 74.1 to 75.5 |
| Object Detection (high) | 16.6% ±0.8, Mean of 3 runs, range 15.8 to 17.4 | 76.4% ±1.2, Mean of 3 runs, range 75.0 to 77.4 |
| Counting (low) | 58.6% ±2.0, Mean of 3 runs, range 56.8 to 60.8 | 76.6% ±2.0, Mean of 3 runs, range 74.3 to 78.4 |
| Counting (high) | 64.9% ±2.0, Mean of 3 runs, range 63.5 to 67.6 | 77.5% ±1.3, Mean of 3 runs, range 75.7 to 78.4 |
| Identification (low) | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 |
| Identification (high) | 82.3% ±3.1, Mean of 3 runs, range 78.1 to 84.4 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | 51.1% | 66.7% |
| by category |
|
|
| OCR (high) | 55.8% | 68.8% |
| by category |
|
|
| Reasoning (low) | 57.0% ±3.3, Mean of 3 runs, range 54.3 to 60.9 | 72.6% ±3.6, Mean of 3 runs, range 69.5 to 76.8 |
| Reasoning (high) | 64.0% ±1.3, Mean of 3 runs, range 62.9 to 65.6 | 77.7% ±1.0, Mean of 3 runs, range 76.8 to 78.8 |
GPT-5.4 Mini vs GPT-6 Sol: 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.
GPT-6 Sol is a proprietary multimodal reasoning model from OpenAI, released on September 22, 2026 alongside GPT-6 Luna as an efficiency-oriented tier of the GPT-6 family that began with GPT-6 Astra. OpenAI states that Sol and Luna are trained with methods similar to those used for Astra, carrying the same work on professional tasks, factuality, coding, computer use, and alignment into models that run faster. Sol accepts text and image input and returns text output, and OpenAI documents a context window of roughly one million tokens together with a knowledge cutoff of April 20, 2026.
The model targets complex coding and agentic workflows and exposes a configurable reasoning effort setting with levels of none, low, medium, high, xhigh, and max, which trades latency and token consumption against answer quality. Its vision behavior covers image understanding tasks such as visual question answering, captioning, document and chart interpretation, and text recognition.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6 Sol performed better. It scores higher on all five vision tasks and averages 76.9% (#13 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, GPT-6 Sol leads with 74.7% 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 $0.0074. GPT-5.4 Mini is priced at $0.75 per 1M input tokens and $4.50 per 1M output; GPT-6 Sol is priced at $2.00 per 1M input tokens and $10.00 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 10.2s. 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.