GPT-6.1 Terra vs GPT-6 Sol
GPT-6.1 Terra and GPT-6 Sol score 85.8% vs 76.9% averaged across five Vision Evals tasks (#2 and #13 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 66.7%).
- GPT-6.1 Terra is about half the cost: $0.0033 vs $0.0074 overall average cost per sample.
Roboflow Vision Evals. Full breakdown below.
Compare GPT-6.1 Terra vs GPT-6 Sol 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 Terra vs GPT-6 Sol on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-6.1 Terra vs GPT-6 Sol Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | GPT-6.1 Terra | GPT-6 Sol |
|---|---|---|
| Organization | OpenAI | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Oct 2026 | Sep 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 | $1.00 | $2.00 |
| Output $/1M | $5.00 | $10.00 |
| Vision Tasks | ||
| Captioning | Supported | Demo |
| Classification | Supported | Demo |
| Object Detection | Supported | Demo |
| OCR | Supported | Demo |
| Visual Question Answering | Supported | Demo |
| Promptable Concept Segmentation | Not listed | Demo |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 85.8% | 76.9% |
| Avg cost / sample | $0.0033 | $0.0074 |
| Avg speed / sample | 10.05s | 10.20s |
| By task | ||
| Object Detection (low) | 84.7% ±0.1, Mean of 3 runs, range 84.7 to 84.8 | 74.7% ±0.7, Mean of 3 runs, range 74.1 to 75.5 |
| Object Detection (high) | 84.7% ±0.4, Mean of 3 runs, range 84.2 to 84.9 | 76.4% ±1.2, Mean of 3 runs, range 75.0 to 77.4 |
| Counting (low) | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 76.6% ±2.0, Mean of 3 runs, range 74.3 to 78.4 |
| Counting (high) | 84.2% ±3.4, Mean of 3 runs, range 81.1 to 87.8 | 77.5% ±1.3, Mean of 3 runs, range 75.7 to 78.4 |
| Identification (low) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | 81.9% | 66.7% |
| by category |
|
|
| OCR (high) | 84.4% | 68.8% |
| by category |
|
|
| Reasoning (low) | 85.4% ±1.7, Mean of 3 runs, range 83.4 to 86.8 | 72.6% ±3.6, Mean of 3 runs, range 69.5 to 76.8 |
| Reasoning (high) | 87.0% ±1.3, Mean of 3 runs, range 85.4 to 88.1 | 77.7% ±1.0, Mean of 3 runs, range 76.8 to 78.8 |
GPT-6.1 Terra vs GPT-6 Sol: Overview
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.
GPT-6 Sol is a proprietary multimodal reasoning model from OpenAI, released on September 22, 2026 alongside GPT-6 Luna as an efficiency-oriented tier of the GPT-6 family that began with GPT-6 Astra. OpenAI states that Sol and Luna are trained with methods similar to those used for Astra, carrying the same work on professional tasks, factuality, coding, computer use, and alignment into models that run faster. Sol accepts text and image input and returns text output, and OpenAI documents a context window of roughly one million tokens together with a knowledge cutoff of April 20, 2026.
The model targets complex coding and agentic workflows and exposes a configurable reasoning effort setting with levels of none, low, medium, high, xhigh, and max, which trades latency and token consumption against answer quality. Its vision behavior covers image understanding tasks such as visual question answering, captioning, document and chart interpretation, and text recognition.
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 76.9% (#13 of 65) for GPT-6 Sol. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals OCR benchmark at low effort, GPT-6.1 Terra leads with 81.9% against 66.7%. 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 $0.0074. GPT-6.1 Terra is priced at $1.00 per 1M input tokens and $5.00 per 1M output; GPT-6 Sol is priced at $2.00 per 1M input tokens and $10.00 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 10.2s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.