Gemini 3.1 Flash-Lite vs GPT-5.6 Terra
Compare Gemini 3.1 Flash-Lite and GPT-5.6 Terra 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 Terra: 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 Terra is the mid-tier reasoning model in OpenAI's GPT-5.6 family, which also includes the flagship Sol and the lightweight Luna. Introduced in a limited preview on June 26, 2026, and made broadly available on July 9, 2026, Terra accepts text and image input and produces text output, supporting vision, function calling, tool use, and agentic workflows. It is designed as a balanced option for everyday professional and production workloads — including coding assistance, document analysis, customer support, and multi-step agent tasks — where both output quality and cost efficiency matter. OpenAI positions Terra as delivering performance competitive with GPT-5.5 at approximately half the price, with a context window of around 1,050,000 tokens. On Terminal-Bench 2.1, Terra scores 84.3%, matching Claude Fable 5 on that benchmark. Under OpenAI's Preparedness Framework, Terra is rated High for cybersecurity and biological capabilities, meaning it demonstrates meaningful capability in those domains without reaching the Critical threshold.
GPT-5.6 introduces a new naming convention in which the generation number (5.6) is paired with a durable capability tier name (Sol, Terra, or Luna), allowing each tier to advance on its own schedule. Terra carries the API identifier gpt-5.6-terra and supports the same reasoning effort controls available across the family, including adjustable reasoning depth. The model includes prompt caching with explicit cache breakpoints and a 30-minute minimum cache life, with cache writes billed at 1.25x the uncached input rate and cache reads receiving a 90% discount. GPT-5.6 Terra is a proprietary, closed-weights model served through the OpenAI API, Codex, and ChatGPT.
Gemini 3.1 Flash-Lite vs GPT-5.6 Terra Comparison Table
| Property | Gemini 3.1 Flash-Lite | GPT-5.6 Terra |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Mar 2026 | Jul 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.250 | $2.50 |
| Output $/1M | $1.50 | $15.00 |
| 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 |
| 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 | 3.69s |
| Median input tokensincl. image tokens | 1.1K | 1.3K |
| Median output tokens | 6 | 53 |
| Est. cost / taskon this benchmark | $0.0003 | $0.0041 |
| Defect Detection | 73.3%(11/15) | 86.7%(13/15) |
| Document Understanding | 77.8%(7/9) | 88.9%(8/9) |
| Object Counting | 30%(3/10) | 20%(2/10) |
| Object Understanding | 64.3%(9/14) | 85.7%(12/14) |
| Spatial Understanding | 84.2%(16/19) | 84.2%(16/19) |
| OCR | ||
| Overall Score | 89.96% | 78.6% |
| Avg Response Time | 1.32s | 2.07s |
| Median input tokensincl. image tokens | 1.1K | 105 |
| Median output tokens | 10 | 33 |
| Est. cost / taskon this benchmark | $0.0003 | $0.0008 |
| Focused Scene OCR | 91.9%(91/99) | 79.8%(79/99) |
| Handwritten Math | 80%(8/10) | 50%(5/10) |
| License Plate Recognition | 100%(30/30) | 86.7%(26/30) |
| Text Recognition | 90%(27/30) | 83.3%(25/30) |
| VQA & Extraction | 83.3%(50/60) | 75%(45/60) |
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