Gemini 3 Flash vs GPT-6.1 Sol
GPT-6.1 Sol and Gemini 3 Flash score 81.7% vs 65.7% averaged across five Vision Evals tasks (#9 and #33 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 38.6%).
- Gemini 3 Flash is about a third of the cost: $0.0024 vs $0.0068 overall average cost per sample.
- Gemini 3 Flash is faster: 5.8s vs 12.8s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Gemini 3 Flash vs GPT-6.1 Sol 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.
Upload an image
Drag and drop an image here, or click to browse
Gemini 3 Flash vs GPT-6.1 Sol on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 3 Flash vs GPT-6.1 Sol Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | Gemini 3 Flash | GPT-6.1 Sol |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Dec 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 | $0.500 | $2.00 |
| Output $/1M | $3.00 | $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 | 65.7% | 81.7% |
| Avg cost / sample | $0.0024 | $0.0068 |
| Avg speed / sample | 5.83s | 12.82s |
| By task | ||
| Object Detection (low) | 38.6% | 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) | 67.6% | 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) | 63.9% | 75.8% |
| by category |
|
|
| OCR (high) | 73.1% | 75.8% |
| by category |
|
|
| Reasoning (low) | 64.9% | 83.7% ±1.3, Mean of 3 runs, range 82.1 to 84.8 |
| Reasoning (high) | 74.2% | 88.7% ±2.0, Mean of 3 runs, range 87.4 to 91.4 |
Gemini 3 Flash vs GPT-6.1 Sol: Overview
Gemini 3 Flash is a proprietary multimodal large language model developed by Google through Google DeepMind, designed to deliver fast, cost-efficient reasoning across real-time products and developer workflows. Released in December 2025, it is the Flash-tier variant of the Gemini 3 family, balancing low latency with reasoning quality approaching Pro models.
The model supports text, images, audio, and video, with an exceptionally large context window of roughly one million input tokens and outputs up to ~65k tokens. It emphasizes rapid responses for coding, summarization, analysis, and agentic tasks, and exposes configurable “thinking levels” via API to trade speed for deeper reasoning. Today, Gemini 3 Flash positions itself as a high-throughput, production-ready model, serving as the default in the Gemini app and Google Search’s AI Mode, optimized for scalable, interactive AI applications.
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 65.7% (#33 of 69) for Gemini 3 Flash. 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 38.6%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0024 per sample against $0.0068. Gemini 3 Flash is priced at $0.50 per 1M input tokens and $3.00 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 3 Flash is faster. Across Roboflow's Vision Evals it averaged 5.8s 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 image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.