Gemini 3.1 Flash-Lite vs GPT-5.6 Sol
Compare Gemini 3.1 Flash-Lite and GPT-5.6 Sol side-by-side. See how these vision models stack up in Object Detection, Classification, Image Captioning, Open Prompt, and OCR.
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Gemini 3.1 Flash-Lite vs GPT-5.6 Sol: Overview
Gemini 3.1 Flash-Lite is a natively multimodal reasoning model from Google DeepMind in the Gemini 3 series, based on the Gemini 3 Pro architecture. It processes text, image, video, audio, and PDF inputs within a 1 million token context window and produces text output up to 64K tokens. The model targets high-volume, latency-sensitive workloads and supports visual question answering, image and document data extraction, content moderation, classification, translation, automated speech recognition, and agentic data pipelines. It exposes configurable thinking levels of minimal, low, medium, and high, which set the depth of internal reasoning applied per request and let developers balance response quality against cost and latency.
On benchmarks reported at launch, Gemini 3.1 Flash-Lite scores 86.9% on GPQA Diamond and 76.8% on the MMMU Pro multimodal benchmark, and reaches an Elo score of 1432 on the Arena.ai leaderboard. According to Artificial Analysis benchmarks, it produces a 2.5 times faster time to first answer token and a 45% increase in output speed relative to Gemini 2.5 Flash. It also shows improved instruction following, higher audio input quality for automated speech recognition tasks, and support for structured JSON output used in data extraction pipelines.
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.
Gemini 3.1 Flash-Lite vs GPT-5.6 Sol Comparison Table
| Property | Gemini 3.1 Flash-Lite | GPT-5.6 Sol |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Mar 2026 | Jul 2026 |
| Context Window | 1.0M | 1.5M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.250 | $5.00 |
| Output $/1M | $1.50 | $30.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Document Question Answering | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Chart Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 68.66% | 76.12% |
| Avg Response Time | 1.86s | 8.41s |
| Median input tokensincl. image tokens | 1.1K | 1.4K |
| Median output tokens | 6 | 12 |
| Est. cost / taskon this benchmark | $0.0003 | $0.0073 |
| Defect Detection | 73.3%(11/15) | 80%(12/15) |
| Document Understanding | 77.8%(7/9) | 77.8%(7/9) |
| Object Counting | 30%(3/10) | 30%(3/10) |
| Object Understanding | 64.3%(9/14) | 85.7%(12/14) |
| Spatial Understanding | 84.2%(16/19) | 89.5%(17/19) |
| OCR | ||
| Overall Score | 89.96% | 78.6% |
| Avg Response Time | 1.32s | 3.23s |
| Median input tokensincl. image tokens | 1.1K | 105 |
| Median output tokens | 10 | 22 |
| Est. cost / taskon this benchmark | $0.0003 | $0.0012 |
| Focused Scene OCR | 91.9%(91/99) | 73.7%(73/99) |
| Handwritten Math | 80%(8/10) | 60%(6/10) |
| License Plate Recognition | 100%(30/30) | 90%(27/30) |
| Text Recognition | 90%(27/30) | 90%(27/30) |
| VQA & Extraction | 83.3%(50/60) | 78.3%(47/60) |
Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology