Gemini 3.1 Pro vs GPT-5.6 Terra
Compare Gemini 3.1 Pro and GPT-5.6 Terra side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, OCR, and Object Detection.
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Models in this comparison
Gemini 3.1 Pro vs GPT-5.6 Terra on Vision Evals
Gemini 3.1 Pro scores higher on all six Vision Evals tasks.
The widest gap is Identification, where Gemini 3.1 Pro leads 100.0% to 78.1%.
Overall, Gemini 3.1 Pro averages 83.1% (#3 of 25) against 72.4% (#12 of 25) for GPT-5.6 Terra.
GPT-5.6 Terra is both cheaper ($0.0044 vs $0.0093 per sample) and faster (7.2s vs 7.8s per sample).
Gemini 3.1 Pro vs GPT-5.6 Terra Comparison Table
Evals updated August 6, 2026Pricing updated August 11, 2026
| Property | Gemini 3.1 Pro | GPT-5.6 Terra |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Jul 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $1.00 |
| Output $/1M | $12.00 | $6.00 |
| 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 | 83.1% | 72.4% |
| Avg cost / sample | $0.0093 | $0.0044 |
| Avg speed / sample | 7.81s | 7.15s |
| By task | ||
| Object Detection | 67.4% $0.010 | 60.7% $0.0070 |
| Counting | 71.6% $0.0071 | 67.6% $0.0030 |
| Identification | 100.0% $0.0070 | 78.1% $0.0020 |
| OCR | 92.6% $0.0066 | 88.8% $0.0065 |
| Data Extraction | 94.8% $0.0063 | 79.4% $0.0018 |
| Reasoning (low) | 72.2% $0.012 | 59.6% $0.0025 |
| Reasoning (high) | 74.8% $0.021 | 64.2% $0.0033 |
Gemini 3.1 Pro vs GPT-5.6 Terra: Overview
Gemini 3.1 Pro is a proprietary multimodal model from Google’s Gemini 3 series, released in early 2026 and designed for advanced reasoning across large multimodal datasets. It accepts text, images, audio, video, and documents, supporting up to a 1-million-token input context with up to 64k output tokens. Compared with Gemini 3 Pro, it improves long-context synthesis and multi-step reasoning, enabling more reliable analysis of large documents, datasets, and software codebases.
The model also advances visual understanding and grounding, allowing it to interpret UI screenshots, diagrams, and real-world scenes while referencing specific regions within images or video. These capabilities make Gemini 3.1 Pro well suited for multimodal workflows involving document processing, interface analysis, robotics research, and complex visual reasoning.
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.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3.1 Pro performed better. It scores higher on all six vision tasks and averages 83.1% (#3 of 25) against 72.4% (#12 of 25) for GPT-5.6 Terra. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Identification benchmark, Gemini 3.1 Pro leads with 100.0% against 78.1%. This is the widest gap between the two models across the benchmark's tasks.
GPT-5.6 Terra is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0044 per sample against $0.0093. Gemini 3.1 Pro is priced at $2.00 per 1M input tokens and $12.00 per 1M output; GPT-5.6 Terra is priced at $1.00 per 1M input tokens and $6.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
GPT-5.6 Terra is faster. Across Roboflow's Vision Evals it averaged 7.2s per inference against 7.8s. 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.