Gemini 3.8 Flash vs GPT-6.1 Sol
GPT-6.1 Sol and Gemini 3.8 Flash score 81.7% vs 79.8% averaged across five Vision Evals tasks (#9 and #11 of 69 models), so GPT-6.1 Sol leads overall.
- GPT-6.1 Sol wins 3 of the 5 tasks, with its widest lead on object detection (80.8% vs 68.1%).
- Gemini 3.8 Flash is about half the cost: $0.0045 vs $0.0068 overall average cost per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 3.8 Flash vs GPT-6.1 Sol on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 3.8 Flash vs GPT-6.1 Sol Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | Gemini 3.8 Flash | GPT-6.1 Sol |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 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 | $0.825 | $2.00 |
| Output $/1M | $4.13 | $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 | 79.8% | 81.7% |
| Avg cost / sample | $0.0045 | $0.0068 |
| Avg speed / sample | 12.76s | 12.82s |
| By task | ||
| Object Detection (low) | 68.1% ±0.8, Mean of 3 runs, range 67.3 to 69.0 | 80.8% ±0.1, Mean of 3 runs, range 80.7 to 80.9 |
| Object Detection (high) | 74.8% ±1.1, Mean of 3 runs, range 73.4 to 75.6 | 81.6% ±0.4, Mean of 3 runs, range 81.1 to 82.0 |
| Counting (low) | 78.8% ±2.7, Mean of 3 runs, range 75.7 to 81.1 | 78.8% ±3.4, Mean of 3 runs, range 75.7 to 82.4 |
| Counting (high) | 79.3% ±1.4, Mean of 3 runs, range 78.4 to 81.1 | 80.2% ±3.4, Mean of 3 runs, range 77.0 to 83.8 |
| Identification (low) | 97.9% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 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) | 72.8% | 75.8% |
| by category |
|
|
| OCR (high) | 78.7% | 75.8% |
| by category |
|
|
| Reasoning (low) | 81.2% ±0.3, Mean of 3 runs, range 80.8 to 81.5 | 83.7% ±1.3, Mean of 3 runs, range 82.1 to 84.8 |
| Reasoning (high) | 84.5% ±1.0, Mean of 3 runs, range 83.4 to 85.4 | 88.7% ±2.0, Mean of 3 runs, range 87.4 to 91.4 |
Gemini 3.8 Flash vs GPT-6.1 Sol: Overview
Gemini 3.8 Flash is a natively multimodal reasoning model in Google's Gemini 3 series, positioned as the speed and cost oriented Flash tier while targeting long-horizon software engineering, autonomous agents, and enterprise workflows. It accepts text, images, video, audio, and PDF documents in a single request and returns text, with an input limit of 1,048,576 tokens and an output limit of 65,536 tokens. Thinking is configurable at low, medium, and high levels, and the model supports function calling, code execution, structured outputs, context caching, search and Maps grounding, file search, and computer use in preview. Image generation, audio generation, and the Live API are not supported.
Following Gemini API conventions, it can localize objects by emitting bounding boxes as [ymin, xmin, ymax, xmax] integers normalized to a 0 to 1000 range, which supports prompt driven detection and grounding in addition to captioning, document parsing, and visual question answering. The knowledge cutoff is March 2026, though coverage in some domains reflects the January 2025 cutoff shared across the Gemini 3 family.
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 3 of the five vision tasks and averages 81.7% (#9 of 69) against 79.8% (#11 of 69) for Gemini 3.8 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 68.1%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3.8 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0045 per sample against $0.0068. Gemini 3.8 Flash is priced at $0.83 per 1M input tokens and $4.13 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 3.8 Flash is faster. Across Roboflow's Vision Evals it averaged 12.8s 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 captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.