Gemini 3.5 Flash-Lite vs GPT-5.6 Sol
Compare Gemini 3.5 Flash-Lite and GPT-5.6 Sol side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, Object Detection, and OCR.
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Models in this comparison
Gemini 3.5 Flash-Lite 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 Counting, where GPT-5.6 Sol leads 73.0% to 52.7%.
Overall, Gemini 3.5 Flash-Lite averages 69.6% (#14 of 25) against 76.9% (#9 of 25) for GPT-5.6 Sol.
Gemini 3.5 Flash-Lite is both cheaper ($0.0014 vs $0.025 per sample) and faster (2.7s vs 11.7s per sample).
Gemini 3.5 Flash-Lite vs GPT-5.6 Sol Comparison Table
Evals updated August 6, 2026Pricing updated August 11, 2026
| Property | Gemini 3.5 Flash-Lite | GPT-5.6 Sol |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Jul 2026 |
| Context Window | 1.0M | 1.5M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.300 | $5.00 |
| Output $/1M | $2.50 | $30.00 |
| 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 |
| 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 | 69.6% | 76.9% |
| Avg cost / sample | $0.0014 | $0.025 |
| Avg speed / sample | 2.70s | 11.72s |
| By task | ||
| Object Detection | 57.5% $0.0023 | 68.2% $0.045 |
| Counting | 52.7% $0.0007 | 73.0% $0.013 |
| Identification | 81.3% $0.0004 | 81.3% $0.0070 |
| OCR | 87.4% $0.0011 | 90.7% $0.032 |
| Data Extraction | 90.7% $0.0004 | 82.5% $0.0085 |
| Reasoning (low) | 48.3% $0.0012 | 65.6% $0.011 |
| Reasoning (high) | 68.9% $0.0042 | 72.2% $0.016 |
Gemini 3.5 Flash-Lite vs GPT-5.6 Sol: Overview
Gemini 3.5 Flash-Lite is a natively multimodal reasoning model developed by Google DeepMind, released on July 21, 2026 as part of the Gemini 3.5 model family. It is the fastest model in the 3.5 series, designed for both low-latency tasks and high-throughput production workloads such as agentic search, document processing, receipt translation, and large-scale data extraction. The model accepts text, images, audio, and video as inputs, with a context window of up to 1 million tokens, and produces text output. It supports configurable thinking levels, allowing developers to tune the balance between response quality, cost, and latency depending on workload requirements.
On agentic and coding benchmarks, Gemini 3.5 Flash-Lite significantly outperforms its predecessor, Gemini 3.1 Flash-Lite, including on Terminal-Bench 2.1 (54% vs. 31%), GDM-MRCR v2 long-context (72.2% vs. 60.1%), and real-world task execution as measured by GDPval-AA v2 (1140 vs. 642). It also surpasses Gemini 3 Flash on SWE-Bench Pro (54.2% vs. 49.6%) and OSWorld-Verified (74.0% vs. 65.1%). According to the Artificial Analysis Index, the model generates output at approximately 350 tokens per second. It is built on the Gemini 3.5 Flash foundation and is evaluated across reasoning, coding, multimodal understanding, multilingual performance, and long-context tasks. The model is developed under Google's Frontier Safety Framework.
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% (#9 of 25) against 69.6% (#14 of 25) for Gemini 3.5 Flash-Lite. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Counting benchmark, GPT-5.6 Sol leads with 73.0% against 52.7%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3.5 Flash-Lite is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0014 per sample against $0.025. Gemini 3.5 Flash-Lite is priced at $0.30 per 1M input tokens and $2.50 per 1M output; GPT-5.6 Sol is priced at $5.00 per 1M input tokens and $30.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Gemini 3.5 Flash-Lite is faster. Across Roboflow's Vision Evals it averaged 2.7s 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 image captioning and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.