Gemini 4 Carbon vs GPT-6.1 Sol
Gemini 4 Carbon and GPT-6.1 Sol score 89.4% vs 81.7% averaged across five Vision Evals tasks (#1 and #8 of 68 models), so Gemini 4 Carbon leads overall.
- Gemini 4 Carbon wins all 5 tasks, with its widest lead on OCR (87.2% vs 75.8%).
- Gemini 4 Carbon is cheaper: $0.0061 vs $0.0068 overall average cost per sample.
- Gemini 4 Carbon is faster: 8.7s vs 12.8s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 4 Carbon vs GPT-6.1 Sol on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 4 Carbon vs GPT-6.1 Sol Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Gemini 4 Carbon | GPT-6.1 Sol |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Nov 2026 | Sep 2026 |
| Context Window | 1.0M | 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 | No published price | $2.00 |
| Output $/1M | No published price | $10.00 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 89.4% | 81.7% |
| Avg cost / sample | $0.0061 | $0.0068 |
| Avg speed / sample | 8.71s | 12.82s |
| By task | ||
| Object Detection (low) | 84.5% ±0.3, Mean of 3 runs, range 84.2 to 84.8 | 80.8% ±0.1, Mean of 3 runs, range 80.7 to 80.9 |
| Object Detection (high) | 85.9% ±0.3, Mean of 3 runs, range 85.6 to 86.2 | 81.6% ±0.4, Mean of 3 runs, range 81.1 to 82.0 |
| Counting (low) | 90.1% ±1.4, Mean of 3 runs, range 89.2 to 91.9 | 78.8% ±3.4, Mean of 3 runs, range 75.7 to 82.4 |
| Counting (high) | 86.9% ±2.7, Mean of 3 runs, range 83.8 to 89.2 | 80.2% ±3.4, Mean of 3 runs, range 77.0 to 83.8 |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | 87.2% | 75.8% |
| by category |
|
|
| OCR (high) | 89.7% | 75.8% |
| by category |
|
|
| Reasoning (low) | 91.4% ±1.7, Mean of 3 runs, range 90.1 to 93.4 | 83.7% ±1.3, Mean of 3 runs, range 82.1 to 84.8 |
| Reasoning (high) | 92.3% ±0.3, Mean of 3 runs, range 92.0 to 92.7 | 88.7% ±2.0, Mean of 3 runs, range 87.4 to 91.4 |
Gemini 4 Carbon vs GPT-6.1 Sol: Overview
Gemini 4 Carbon is a multimodal model from Google. It builds on Gemini 4 Argon and supports reasoning over text and images. Google describes improvements in coding and long-running tasks. Model weights are not publicly released.
GPT-6.1 Sol is a reasoning model in OpenAI's GPT-6 series that accepts text and image input and returns text. It is an upgrade to GPT-6 Sol positioned to approach the intelligence of the larger GPT-6 Astra model on agentic coding, computer use, and professional knowledge work. The model exposes an adjustable reasoning effort control, ranging from low settings for simple turns to maximum settings for harder tasks, and can be driven with tool use enabled or disabled. It operates over a context window of roughly one million tokens and emits up to 128,000 output tokens in a single response, which supports long-running agent loops over large codebases and multi-document collections. Audio and video inputs are not supported.
OpenAI reports that GPT-6.1 Sol performs on par with or better than GPT-6 Sol across its image input safety evaluations, and that the share of responses containing a factual error at low reasoning effort falls from 11.4 percent to 7.7 percent.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 4 Carbon performed better. It scores higher on all five vision tasks and averages 89.4% (#1 of 68) against 81.7% (#8 of 68) for GPT-6.1 Sol. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals OCR benchmark at low effort, Gemini 4 Carbon leads with 87.2% against 75.8%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 4 Carbon is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0061 per sample against $0.0068. Actual costs depend on your image sizes, prompts, and output length.
Gemini 4 Carbon is faster. Across Roboflow's Vision Evals it averaged 8.7s per inference against 12.8s. 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 segmentation in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.