Gemini 2.5 Pro vs GPT-6.1 Sol
GPT-6.1 Sol and Gemini 2.5 Pro score 81.7% vs 57.6% averaged across five Vision Evals tasks (#9 and #46 of 69 models), so GPT-6.1 Sol leads overall.
- GPT-6.1 Sol wins 4 of the 5 tasks, with its widest lead on object detection (80.8% vs 33.7%).
- Gemini 2.5 Pro is cheaper: $0.0051 vs $0.0068 overall average cost per sample.
- Gemini 2.5 Pro is faster: 6.8s vs 12.8s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 2.5 Pro vs GPT-6.1 Sol on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 2.5 Pro vs GPT-6.1 Sol Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | Gemini 2.5 Pro | GPT-6.1 Sol |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2025 | 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 | $1.25 | $2.00 |
| Output $/1M | $10.00 | $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 | 57.6% | 81.7% |
| Avg cost / sample | $0.0051 | $0.0068 |
| Avg speed / sample | 6.83s | 12.82s |
| By task | ||
| Object Detection (low) | 33.7% | 80.8% ±0.1, Mean of 3 runs, range 80.7 to 80.9 |
| Object Detection (high) | – | 81.6% ±0.4, Mean of 3 runs, range 81.1 to 82.0 |
| Counting (low) | 52.7% | 78.8% ±3.4, Mean of 3 runs, range 75.7 to 82.4 |
| Counting (high) | – | 80.2% ±3.4, Mean of 3 runs, range 77.0 to 83.8 |
| Identification (low) | 93.8% | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | – | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | 65.2% | 75.8% |
| by category |
|
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| OCR (high) | 64.6% | 75.8% |
| by category |
|
|
| Reasoning (low) | 42.4% | 83.7% ±1.3, Mean of 3 runs, range 82.1 to 84.8 |
| Reasoning (high) | 62.3% | 88.7% ±2.0, Mean of 3 runs, range 87.4 to 91.4 |
Gemini 2.5 Pro vs GPT-6.1 Sol: Overview
Gemini 2.5 Pro, released on June 17, 2025, is Google DeepMind’s most capable model in the Gemini 2.5 family, optimized for deep reasoning, coding, and complex multimodal tasks. It accepts text, images, audio, video, and PDFs as input and outputs text. The model supports 1 million input tokens with an output capacity of up to 65K tokens, enabling large-scale comprehension of datasets, codebases, and technical documents. Its training knowledge extends to January 2025.
It supports structured outputs, function calling, code execution, search grounding, and URL context, making it well-suited for enterprise, STEM, and developer workflows. However, it does not currently support image or audio generation in its stable release, and its higher computational cost and latency make it less efficient than Flash or Flash-Lite. It is available via the Gemini API, Google AI Studio, and Vertex AI.
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, GPT-6.1 Sol performed better. It scores higher on 4 of the five vision tasks and averages 81.7% (#9 of 69) against 57.6% (#46 of 69) for Gemini 2.5 Pro. 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-6.1 Sol leads with 80.8% against 33.7%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 2.5 Pro is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0051 per sample against $0.0068. Gemini 2.5 Pro is priced at $1.25 per 1M input tokens and $10.00 per 1M output; GPT-6.1 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.
Gemini 2.5 Pro is faster. Across Roboflow's Vision Evals it averaged 6.8s 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 object detection and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.