GPT-6.1 Terra vs GPT-6 Astra
GPT-6.1 Terra and GPT-6 Astra score 85.8% vs 83.4% averaged across five Vision Evals tasks (#2 and #3 of 65 models), so GPT-6.1 Terra leads overall.
- GPT-6.1 Terra wins 3 of the 5 tasks, with its widest lead on OCR (81.9% vs 77.8%).
- GPT-6.1 Terra is under a tenth of the cost: $0.0033 vs $0.034 overall average cost per sample.
Roboflow Vision Evals. Full breakdown below.
Compare GPT-6.1 Terra vs GPT-6 Astra 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 Astra on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-6.1 Terra vs GPT-6 Astra Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | GPT-6.1 Terra | GPT-6 Astra |
|---|---|---|
| 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 | $10.00 |
| Output $/1M | $5.00 | $50.00 |
| 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% | 83.4% |
| Avg cost / sample | $0.0033 | $0.034 |
| Avg speed / sample | 10.05s | 9.94s |
| By task | ||
| Object Detection (low) | 84.7% ±0.1, Mean of 3 runs, range 84.7 to 84.8 | 82.1% ±0.8, Mean of 3 runs, range 81.0 to 82.7 |
| Object Detection (high) | 84.7% ±0.4, Mean of 3 runs, range 84.2 to 84.9 | 83.6% ±0.8, Mean of 3 runs, range 82.8 to 84.5 |
| Counting (low) | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 80.2% ±1.4, Mean of 3 runs, range 78.4 to 81.1 |
| Counting (high) | 84.2% ±3.4, Mean of 3 runs, range 81.1 to 87.8 | 81.1% ±1.4, Mean of 3 runs, range 79.7 to 82.4 |
| Identification (low) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| 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% | 77.8% |
| by category |
|
|
| OCR (high) | 84.4% | 77.6% |
| by category |
|
|
| Reasoning (low) | 85.4% ±1.7, Mean of 3 runs, range 83.4 to 86.8 | 87.2% ±1.0, Mean of 3 runs, range 86.1 to 88.1 |
| Reasoning (high) | 87.0% ±1.3, Mean of 3 runs, range 85.4 to 88.1 | 91.2% ±0.3, Mean of 3 runs, range 90.7 to 91.4 |
GPT-6.1 Terra vs GPT-6 Astra: 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 Astra is a proprietary multimodal reasoning model from OpenAI that accepts text and image input and produces text output. It is positioned as the company's flagship system for long-horizon end-to-end work spanning complex reasoning, software engineering, computer use, browsing, research and document creation. The model exposes a graduated reasoning effort control with low, medium, high, xhigh and max settings, and it accepts a change to that setting partway through a conversation rather than only at request time. It launches as a single tier with no smaller mini or nano variants, carries a context window of roughly 1.05 million tokens with a maximum output of 128,000 tokens, and reports a knowledge cutoff of April 30, 2026.
The model supports computer use, structured outputs, streaming, programmatic tool calling, multi-agent orchestration, prompt caching and persisted reasoning, and it keeps earlier context windows searchable so it can recover requirements or tool outputs from previous turns. OpenAI describes Astra as the first of its models to cross the Critical cybersecurity capability threshold under its Preparedness Framework.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6.1 Terra performed better. It scores higher on 3 of the five vision tasks and averages 85.8% (#2 of 65) against 83.4% (#3 of 65) for GPT-6 Astra. 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 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 $0.034. GPT-6.1 Terra is priced at $1.00 per 1M input tokens and $5.00 per 1M output; GPT-6 Astra is priced at $10.00 per 1M input tokens and $50.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
GPT-6 Astra is faster. Across Roboflow's Vision Evals it averaged 9.9s per inference against 10.1s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.