GPT-6.1 Sol vs GPT-6.1 Terra
GPT-6.1 Terra and GPT-6.1 Sol score 85.8% vs 81.7% averaged across five Vision Evals tasks (#2 and #6 of 65 models), so GPT-6.1 Terra leads overall.
- GPT-6.1 Terra wins all 5 tasks, with its widest lead on OCR (81.9% vs 75.8%).
- GPT-6.1 Terra is about half the cost: $0.0033 vs $0.0068 overall average cost per sample.
- GPT-6.1 Terra is faster: 10.1s vs 12.8s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare GPT-6.1 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-6.1 Sol vs GPT-6.1 Terra on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-6.1 Sol vs GPT-6.1 Terra Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | GPT-6.1 Sol | GPT-6.1 Terra |
|---|---|---|
| Organization | OpenAI | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Oct 2026 |
| Context Window | 1.1M | 1.1M |
| Parameters | undisclosed | undisclosed |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $1.00 |
| Output $/1M | $10.00 | $5.00 |
| Vision Tasks | ||
| Captioning | Demo | Supported |
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| OCR | Demo | Supported |
| Visual Question Answering | Demo | Supported |
| Promptable Concept Segmentation | Demo | Not listed |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 81.7% | 85.8% |
| Avg cost / sample | $0.0068 | $0.0033 |
| Avg speed / sample | 12.82s | 10.05s |
| By task | ||
| Object Detection (low) | 80.8% ±0.1, Mean of 3 runs, range 80.7 to 80.9 | 84.7% ±0.1, Mean of 3 runs, range 84.7 to 84.8 |
| Object Detection (high) | 81.6% ±0.4, Mean of 3 runs, range 81.1 to 82.0 | 84.7% ±0.4, Mean of 3 runs, range 84.2 to 84.9 |
| Counting (low) | 78.8% ±3.4, Mean of 3 runs, range 75.7 to 82.4 | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 |
| Counting (high) | 80.2% ±3.4, Mean of 3 runs, range 77.0 to 83.8 | 84.2% ±3.4, Mean of 3 runs, range 81.1 to 87.8 |
| Identification (low) | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| Identification (high) | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| OCR (low) | 75.8% | 81.9% |
| by category |
|
|
| OCR (high) | 75.8% | 84.4% |
| by category |
|
|
| Reasoning (low) | 83.7% ±1.3, Mean of 3 runs, range 82.1 to 84.8 | 85.4% ±1.7, Mean of 3 runs, range 83.4 to 86.8 |
| Reasoning (high) | 88.7% ±2.0, Mean of 3 runs, range 87.4 to 91.4 | 87.0% ±1.3, Mean of 3 runs, range 85.4 to 88.1 |
GPT-6.1 Sol vs GPT-6.1 Terra: Overview
GPT-6.1 Sol is a reasoning model in OpenAI's GPT-6 series that accepts text and image input and returns text. It is an upgrade to GPT-6 Sol positioned to approach the intelligence of the larger GPT-6 Astra model on agentic coding, computer use, and professional knowledge work. The model exposes an adjustable reasoning effort control, ranging from low settings for simple turns to maximum settings for harder tasks, and can be driven with tool use enabled or disabled. It operates over a context window of roughly one million tokens and emits up to 128,000 output tokens in a single response, which supports long-running agent loops over large codebases and multi-document collections. Audio and video inputs are not supported.
OpenAI reports that GPT-6.1 Sol performs on par with or better than GPT-6 Sol across its image input safety evaluations, and that the share of responses containing a factual error at low reasoning effort falls from 11.4 percent to 7.7 percent.
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 81.7% (#6 of 65) for GPT-6.1 Sol. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Identification benchmark at low effort, GPT-6.1 Terra leads with 96.9% against 89.6%. 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 $0.0068. GPT-6.1 Sol is priced at $2.00 per 1M input tokens and $10.00 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 12.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.