Gemini 3.6 Flash vs GPT-6 Sol
Compare Gemini 3.6 Flash and GPT-6 Sol side-by-side.
Compare Gemini 3.6 Flash vs GPT-6 Sol live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Models in this comparison
Gemini 3.6 Flash vs GPT-6 Sol on Vision Evals
Gemini 3.6 Flash scores higher on 4 of the six Vision Evals tasks.
The widest gap is Object Detection, where GPT-6 Sol leads 73.6% to 57.1%.
Overall, Gemini 3.6 Flash averages 83.0% (#8 of 57) against 80.7% (#10 of 57) for GPT-6 Sol.
Gemini 3.6 Flash is cheaper ($0.0032 vs $0.0065 per sample), while GPT-6 Sol is faster (8.1s vs 14.7s per sample).
Gemini 3.6 Flash vs GPT-6 Sol Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | Gemini 3.6 Flash | GPT-6 Sol |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Sep 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | undisclosed | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.750 | |
| Output $/1M | $3.75 | |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Video Classification | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 83.0% | 80.7% |
| Avg cost / sample | $0.0032 | $0.0065 |
| Avg speed / sample | 14.66s | 8.15s |
| By task | ||
| Object Detection (low) | 57.1% ±1.7, Mean of 3 runs, range 55.9 to 59.4 | 73.6% ±0.6, Mean of 3 runs, range 72.9 to 74.2 |
| Object Detection (high) | 70.7% ±0.4, Mean of 3 runs, range 70.3 to 71.2 | 75.2% ±0.9, Mean of 3 runs, range 74.1 to 75.9 |
| Counting (low) | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 74.8% ±2.7, Mean of 3 runs, range 71.6 to 77.0 |
| Counting (high) | 79.3% ±2.7, Mean of 3 runs, range 77.0 to 82.4 | 76.1% ±3.4, Mean of 3 runs, range 71.6 to 78.4 |
| Identification (low) | 99.0% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 91.7% ±3.1, Mean of 3 runs, range 87.5 to 93.8 |
| Identification (high) | 100.0% ±0.0, Mean of 3 runs, range 100.0 to 100.0 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | 88.2% ±0.3, Mean of 3 runs, range 87.9 to 88.4 | 91.7% ±0.4, Mean of 3 runs, range 91.3 to 92.1 |
| OCR (high) | 89.5% ±0.0, Mean of 3 runs, range 89.5 to 89.6 | 91.9% ±0.3, Mean of 3 runs, range 91.6 to 92.2 |
| Data Extraction (low) | 95.9% ±1.0, Mean of 3 runs, range 94.8 to 96.9 | 80.4% ±0.0, Mean of 3 runs, range 80.4 to 80.4 |
| Data Extraction (high) | 94.8% ±1.0, Mean of 3 runs, range 93.8 to 95.9 | 82.5% ±1.0, Mean of 3 runs, range 81.4 to 83.5 |
| Reasoning (low) | 77.7% ±2.0, Mean of 3 runs, range 76.2 to 80.1 | 72.2% ±1.7, Mean of 3 runs, range 70.9 to 74.2 |
| Reasoning (high) | 81.0% ±2.0, Mean of 3 runs, range 79.5 to 83.4 | 77.9% ±2.6, Mean of 3 runs, range 75.5 to 80.8 |
Gemini 3.6 Flash vs GPT-6 Sol: Overview
Gemini 3.6 Flash is a multimodal language model from Google DeepMind, positioned as the workhorse tier in the Gemini 3.x family. It accepts text, image, video, audio, and PDF inputs with a 1 million token context window and produces up to 64,000 output tokens. The model builds directly on Gemini 3.5 Flash, incorporating developer and customer feedback to improve token efficiency, coding quality, and knowledge work performance. According to the Artificial Analysis Index, it consumes 17% fewer output tokens than its predecessor, and on some benchmarks such as DeepSWE, token reduction reaches up to 65%. It supports function calling, structured output, search as a tool, and code execution, and includes computer use as a built-in capability in the Gemini API and Gemini Enterprise.
On coding benchmarks, Gemini 3.6 Flash scores 49% on DeepSWE versus 37% for 3.5 Flash, and 63.9% on MLE Bench versus 49.7%. Computer use performance on OSWorld-Verified improves from 78.4% to 83%, and knowledge work scores on GDPval-AA v2 rise from 1349 to 1421. The model carries a knowledge cutoff of March 2026 and ships with enhanced Frontier Safety safeguards covering chemical, biological, radiological, nuclear, and cyber offense domains, with training to minimize refusals for beneficial uses. It is a proprietary, closed-weights model available in preview through the Gemini API via Google AI Studio, Android Studio, Google Antigravity, Gemini Enterprise, and the Gemini app.
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. OpenAI reports results including 33.2% on AutomationBench at xhigh effort and 56.4% on Agents' Last Exam at max effort, while its reported DeepSWE and OSWorld 2.0 figures of 68.8% and 64.4% fall below those of the earlier GPT-5.6 Sol. 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 3.6 Flash performed better. It scores higher on 4 of the six vision tasks and averages 83.0% (#8 of 57) against 80.7% (#10 of 57) for GPT-6 Sol. 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 Sol leads with 73.6% against 57.1%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3.6 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0032 per sample against $0.0065. Actual costs depend on your image sizes, prompts, and output length.
GPT-6 Sol is faster. Across Roboflow's Vision Evals it averaged 8.1s per inference against 14.7s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.