Gemini 3.8 Flash vs GPT-6 Sol
Gemini 3.8 Flash and GPT-6 Sol score 79.8% vs 76.9% averaged across five Vision Evals tasks (#10 and #15 of 68 models), so Gemini 3.8 Flash leads overall.
- Gemini 3.8 Flash wins 4 of the 5 tasks, with its widest lead on reasoning (81.2% vs 72.6%).
- Gemini 3.8 Flash is about half the cost: $0.0045 vs $0.0074 overall average cost per sample.
- GPT-6 Sol is faster: 10.2s vs 12.8s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 3.8 Flash vs GPT-6 Sol on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 3.8 Flash vs GPT-6 Sol Comparison Table
Evals updated November 10, 2026Pricing updated November 11, 2026
| Property | Gemini 3.8 Flash | GPT-6 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% | 76.9% |
| Avg cost / sample | $0.0045 | $0.0074 |
| Avg speed / sample | 12.76s | 10.20s |
| By task | ||
| Object Detection (low) | 68.1% ±0.8, Mean of 3 runs, range 67.3 to 69.0 | 74.7% ±0.7, Mean of 3 runs, range 74.1 to 75.5 |
| Object Detection (high) | 74.8% ±1.1, Mean of 3 runs, range 73.4 to 75.6 | 76.4% ±1.2, Mean of 3 runs, range 75.0 to 77.4 |
| Counting (low) | 78.8% ±2.7, Mean of 3 runs, range 75.7 to 81.1 | 76.6% ±2.0, Mean of 3 runs, range 74.3 to 78.4 |
| Counting (high) | 79.3% ±1.4, Mean of 3 runs, range 78.4 to 81.1 | 77.5% ±1.3, Mean of 3 runs, range 75.7 to 78.4 |
| Identification (low) | 97.9% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | 72.8% | 66.7% |
| by category |
|
|
| OCR (high) | 78.7% | 68.8% |
| by category |
|
|
| Reasoning (low) | 81.2% ±0.3, Mean of 3 runs, range 80.8 to 81.5 | 72.6% ±3.6, Mean of 3 runs, range 69.5 to 76.8 |
| Reasoning (high) | 84.5% ±1.0, Mean of 3 runs, range 83.4 to 85.4 | 77.7% ±1.0, Mean of 3 runs, range 76.8 to 78.8 |
Gemini 3.8 Flash vs GPT-6 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 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.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3.8 Flash performed better. It scores higher on 4 of the five vision tasks and averages 79.8% (#10 of 68) against 76.9% (#15 of 68) for GPT-6 Sol. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Reasoning benchmark at low effort, Gemini 3.8 Flash leads with 81.2% against 72.6%. 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 $0.0074. Gemini 3.8 Flash is priced at $0.83 per 1M input tokens and $4.13 per 1M output; GPT-6 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.
GPT-6 Sol is faster. Across Roboflow's Vision Evals it averaged 10.2s 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.