GPT-6 Sol vs Grok 4.7
GPT-6 Sol and Grok 4.7 score 76.9% vs 63.5% averaged across five Vision Evals tasks (#13 and #33 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 40.4%).
- GPT-6 Sol is about a third of the cost: $0.0074 vs $0.020 overall average cost per sample.
- GPT-6 Sol is faster: 10.2s vs 33.3s per sample.
Roboflow Vision Evals. Full breakdown below.
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GPT-6 Sol vs Grok 4.7 on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-6 Sol vs Grok 4.7 Comparison Table
Evals updated October 26, 2026Pricing updated November 6, 2026
| Property | GPT-6 Sol | Grok 4.7 |
|---|---|---|
| Organization | OpenAI | SpaceXAI |
| Category | closed | closed |
| Modality | multimodal | — |
| Release Date | Sep 2026 | Sep 2026 |
| Context Window | 1.1M | 500K |
| Parameters | undisclosed | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $2.00 |
| Output $/1M | $10.00 | $6.00 |
| Vision Tasks | ||
| Promptable Concept Segmentation | Demo | Not listed |
| 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 | 76.9% | 63.5% |
| Avg cost / sample | $0.0074 | $0.020 |
| Avg speed / sample | 10.20s | 33.31s |
| By task | ||
| Object Detection (low) | 74.7% ±0.7, Mean of 3 runs, range 74.1 to 75.5 | 40.4% ±0.6, Mean of 3 runs, range 39.8 to 41.0 |
| Object Detection (high) | 76.4% ±1.2, Mean of 3 runs, range 75.0 to 77.4 | 41.2% ±1.6, Mean of 3 runs, range 39.6 to 42.8 |
| Counting (low) | 76.6% ±2.0, Mean of 3 runs, range 74.3 to 78.4 | 61.7% ±1.3, Mean of 3 runs, range 60.8 to 63.5 |
| Counting (high) | 77.5% ±1.3, Mean of 3 runs, range 75.7 to 78.4 | 60.8% ±1.3, Mean of 3 runs, range 59.5 to 62.2 |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| Identification (high) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 |
| OCR (low) | 66.7% | 63.6% |
| by category |
|
|
| OCR (high) | 68.8% | – |
| by category |
| |
| Reasoning (low) | 72.6% ±3.6, Mean of 3 runs, range 69.5 to 76.8 | 64.2% ±2.3, Mean of 3 runs, range 62.3 to 66.9 |
| Reasoning (high) | 77.7% ±1.0, Mean of 3 runs, range 76.8 to 78.8 | 66.9% ±1.3, Mean of 3 runs, range 65.6 to 68.2 |
GPT-6 Sol vs Grok 4.7: Overview
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.
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, GPT-6 Sol performed better. It scores higher on all five vision tasks and averages 76.9% (#13 of 65) against 63.5% (#33 of 65) for Grok 4.7. 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, GPT-6 Sol leads with 74.7% against 40.4%. This is the widest gap between the two models across the benchmark's tasks.
GPT-6 Sol is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0074 per sample against $0.020. GPT-6 Sol is priced at $2.00 per 1M input tokens and $10.00 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-6 Sol is faster. Across Roboflow's Vision Evals it averaged 10.2s 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 image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.