GPT-5.6 Sol vs GPT-6.1 Terra
GPT-6.1 Terra and GPT-5.6 Sol score 85.8% vs 72.4% averaged across five Vision Evals tasks (#2 and #19 of 65 models), so GPT-6.1 Terra leads overall.
- GPT-6.1 Terra wins all 5 tasks, with its widest lead on reasoning (85.4% vs 66.0%).
- GPT-6.1 Terra is about 1/6 of the cost: $0.0033 vs $0.019 overall average cost per sample.
- GPT-6.1 Terra is faster: 10.1s vs 12.2s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare GPT-5.6 Sol vs GPT-6.1 Terra live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
GPT-5.6 Sol vs GPT-6.1 Terra on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-5.6 Sol vs GPT-6.1 Terra Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | GPT-5.6 Sol | GPT-6.1 Terra |
|---|---|---|
| Organization | OpenAI | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Oct 2026 |
| Context Window | 1.5M | 1.1M |
| Parameters | Unknown | undisclosed |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $4.00 | $1.00 |
| Output $/1M | $20.00 | $5.00 |
| Vision Tasks | ||
| Captioning | Demo | Supported |
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| OCR | Demo | Supported |
| Visual Question Answering | Demo | Supported |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 72.4% | 85.8% |
| Avg cost / sample | $0.019 | $0.0033 |
| Avg speed / sample | 12.16s | 10.05s |
| By task | ||
| Object Detection (low) | 68.4% ±0.7, Mean of 3 runs, range 67.9 to 69.3 | 84.7% ±0.1, Mean of 3 runs, range 84.7 to 84.8 |
| Object Detection (high) | 68.4% ±0.8, Mean of 3 runs, range 67.7 to 69.3 | 84.7% ±0.4, Mean of 3 runs, range 84.2 to 84.9 |
| Counting (low) | 74.3% ±1.4, Mean of 3 runs, range 73.0 to 75.7 | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 |
| Counting (high) | 76.1% ±2.0, Mean of 3 runs, range 74.3 to 78.4 | 84.2% ±3.4, Mean of 3 runs, range 81.1 to 87.8 |
| Identification (low) | 89.6% ±4.7, Mean of 3 runs, range 84.4 to 93.8 | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| Identification (high) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| OCR (low) | 63.6% | 81.9% |
| by category |
|
|
| OCR (high) | 65.3% | 84.4% |
| by category |
|
|
| Reasoning (low) | 66.0% ±2.6, Mean of 3 runs, range 63.6 to 68.9 | 85.4% ±1.7, Mean of 3 runs, range 83.4 to 86.8 |
| Reasoning (high) | 71.7% ±1.3, Mean of 3 runs, range 70.2 to 72.8 | 87.0% ±1.3, Mean of 3 runs, range 85.4 to 88.1 |
GPT-5.6 Sol vs GPT-6.1 Terra: Overview
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. 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.
GPT-6.1 Terra is an OpenAI model released on October 26, 2026 for visual reasoning, computer use and writing. It accepts text and image inputs and produces text, with a 1,050,000-token context window and up to 128,000 output tokens. OpenAI lists applications including matching product photographs to catalog entries, interpreting technical drawings and operating application screens.
The API exposes six reasoning effort settings: none, low, medium, high, xhigh and max. Medium is the default. The model supports structured outputs and tool calling through the Responses API. Vision Evals reports its Low and High results for text understanding, counting, identification, reasoning and object detection, using native OpenAI API inference. Terra is a proprietary model; its weights are not released.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6.1 Terra performed better. It scores higher on all five vision tasks and averages 85.8% (#2 of 65) against 72.4% (#19 of 65) for GPT-5.6 Sol. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Reasoning benchmark at low effort, GPT-6.1 Terra leads with 85.4% against 66.0%. This is the widest gap between the two models across the benchmark's tasks.
GPT-6.1 Terra is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0033 per sample against $0.019. GPT-5.6 Sol is priced at $4.00 per 1M input tokens and $20.00 per 1M output; GPT-6.1 Terra is priced at $1.00 per 1M input tokens and $5.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
GPT-6.1 Terra is faster. Across Roboflow's Vision Evals it averaged 10.1s per inference against 12.2s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.