Gemini 3 Flash vs GPT-5.6 Sol
Compare Gemini 3 Flash and GPT-5.6 Sol side-by-side. See how these vision models stack up in Object Detection, Classification, Open Prompt, OCR, and Image Captioning.
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Models in this comparison
Gemini 3 Flash vs GPT-5.6 Sol on Vision Evals
GPT-5.6 Sol scores higher on 4 of the six Vision Evals tasks.
The widest gap is Object Detection, where GPT-5.6 Sol leads 68.2% to 38.6%.
Overall, Gemini 3 Flash averages 74.9% (#11 of 30) against 76.9% (#10 of 30) for GPT-5.6 Sol.
Gemini 3 Flash is both cheaper ($0.0021 vs $0.013 per sample) and faster (4.1s vs 11.7s per sample).
Gemini 3 Flash vs GPT-5.6 Sol Comparison Table
Evals updated August 14, 2026Pricing updated August 18, 2026
| Property | Gemini 3 Flash | GPT-5.6 Sol |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Dec 2025 | Jul 2026 |
| Context Window | 1.0M | 1.5M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.500 | $2.50 |
| Output $/1M | $3.00 | $15.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 74.9% | 76.9% |
| Avg cost / sample | $0.0021 | $0.013 |
| Avg speed / sample | 4.10s | 11.72s |
| By task | ||
| Object Detection | 38.6% $0.0031 | 68.2% $0.022 |
| Counting | 67.6% $0.0012 | 73.0% $0.0065 |
| Identification | 93.8% $0.0009 | 81.3% $0.0035 |
| OCR | 87.6% $0.0024 | 90.7% $0.016 |
| Data Extraction | 96.9% $0.0008 | 82.5% $0.0043 |
| Reasoning (low) | 64.9% $0.0020 | 65.6% $0.0057 |
| Reasoning (high) | 74.2% $0.0040 | 72.2% $0.0079 |
Gemini 3 Flash vs GPT-5.6 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-5.6 Sol is the flagship model in OpenAI's GPT-5.6 family, which also includes Terra (a balanced everyday-work tier) and Luna (a fast, cost-efficient tier). Sol is designed for demanding reasoning, long-horizon agentic workflows, software engineering, computer use, scientific research, and cybersecurity tasks. It introduces two new capability modes: a "max" reasoning effort setting that allocates additional compute time for difficult problems, and an "ultra" mode that coordinates multiple subagents in parallel to accelerate complex, multi-step work. The model supports native multimodal input, allowing it to process screenshots, diagrams, charts, documents, and photographs alongside text. A reported context window of approximately 1.5 million tokens enables processing of large codebases, lengthy research documents, and extended agentic sessions.
GPT-5.6 Sol was announced on June 26, 2026, initially in a limited preview for trusted partners, and reached general availability on July 9, 2026. On the Agents' Last Exam benchmark, which evaluates long-running professional workflows across 55 fields, Sol scores 53.6. On Terminal-Bench 2.1, which tests command-line agentic coding workflows, Sol Ultra achieves 91.9%. The model also demonstrates gains in life sciences evaluations, including long-horizon genomics and quantitative biology analyses. OpenAI paired the release with its most extensive safety evaluation to date, combining human red teaming with large-scale automated testing, and classified Sol as High capability in both cybersecurity and biological risk under its Preparedness Framework, though it does not cross the Critical threshold in either category.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-5.6 Sol performed better. It scores higher on 4 of the six vision tasks and averages 76.9% (#10 of 30) against 74.9% (#11 of 30) 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, GPT-5.6 Sol leads with 68.2% 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 $0.0021 per sample against $0.013. Gemini 3 Flash is priced at $0.50 per 1M input tokens and $3.00 per 1M output; GPT-5.6 Sol is priced at $2.50 per 1M input tokens and $15.00 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 4.1s per inference against 11.7s. 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.