GPT-5.6 Terra vs GPT-6 Astra
Compare GPT-5.6 Terra and GPT-6 Astra side-by-side. See how these vision models stack up in Classification, Open Prompt, Object Detection, OCR, and Image Captioning.
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Models in this comparison
GPT-5.6 Terra vs GPT-6 Astra on Vision Evals
GPT-6 Astra scores higher on all six Vision Evals tasks.
The widest gap is Reasoning, where GPT-6 Astra leads 87.2% to 60.9%.
Overall, GPT-5.6 Terra averages 73.8% (#19 of 53) against 86.6% (#1 of 53) for GPT-6 Astra.
GPT-5.6 Terra is cheaper ($0.0088 vs $0.030 per sample), while GPT-6 Astra is faster (6.7s vs 7.7s per sample).
GPT-5.6 Terra vs GPT-6 Astra Comparison Table
Evals updated September 5, 2026Pricing updated September 5, 2026
| Property | GPT-5.6 Terra | GPT-6 Astra |
|---|---|---|
| Organization | OpenAI | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Sep 2026 |
| Context Window | 1.1M | 1.1M |
| Parameters | Undisclosed | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $10.00 |
| Output $/1M | $12.00 | $50.00 |
| 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 | 73.8% | 86.6% |
| Avg cost / sample | $0.0088 | $0.030 |
| Avg speed / sample | 7.74s | 6.67s |
| By task | ||
| Object Detection (low) | 60.6% ±0.2, Mean of 3 runs, range 60.3 to 60.7 | 82.1% ±0.8, Mean of 3 runs, range 81.0 to 82.7 |
| Object Detection (high) | 61.3% ±0.4, Mean of 3 runs, range 60.8 to 61.6 | 83.6% ±0.8, Mean of 3 runs, range 82.8 to 84.5 |
| Counting (low) | 65.8% ±2.0, Mean of 3 runs, range 63.5 to 67.6 | 80.2% ±1.4, Mean of 3 runs, range 78.4 to 81.1 |
| Counting (high) | 62.6% ±2.7, Mean of 3 runs, range 59.5 to 64.9 | 81.1% ±1.4, Mean of 3 runs, range 79.7 to 82.4 |
| Identification (low) | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | 86.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | 89.4% ±0.8, Mean of 3 runs, range 88.8 to 90.3 | 91.9% ±0.2, Mean of 3 runs, range 91.6 to 92.1 |
| OCR (high) | 89.4% ±0.6, Mean of 3 runs, range 88.8 to 90.1 | 91.5% ±0.2, Mean of 3 runs, range 91.3 to 91.7 |
| Data Extraction (low) | 79.7% ±0.5, Mean of 3 runs, range 79.4 to 80.4 | 88.7% ±1.0, Mean of 3 runs, range 87.6 to 89.7 |
| Data Extraction (high) | 80.4% ±1.0, Mean of 3 runs, range 79.4 to 81.4 | 91.1% ±1.0, Mean of 3 runs, range 89.7 to 91.8 |
| Reasoning (low) | 60.9% ±2.0, Mean of 3 runs, range 59.6 to 63.6 | 87.2% ±1.0, Mean of 3 runs, range 86.1 to 88.1 |
| Reasoning (high) | 65.3% ±1.0, Mean of 3 runs, range 64.2 to 66.2 | 91.2% ±0.3, Mean of 3 runs, range 90.7 to 91.4 |
GPT-5.6 Terra vs GPT-6 Astra: Overview
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.
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.
OpenAI reports evaluation results across agentic, scientific and security benchmarks, including 96.0% on GPQA Diamond, 64.6% on Terminal-Bench Science, 72.6% on OSWorld 2.0, and a perfect score on ExploitBench, along with near saturation on FrontierMath Tier 4 and ARC-AGI-3. 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 Astra performed better. It scores higher on all six vision tasks and averages 86.6% (#1 of 53) against 73.8% (#19 of 53) for GPT-5.6 Terra. 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 Astra leads with 87.2% against 60.9%. 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 $0.0088 per sample against $0.030. GPT-5.6 Terra is priced at $2.00 per 1M input tokens and $12.00 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 6.7s per inference against 7.7s. 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 classification and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.