GPT-5.6 Luna vs GPT-6 Astra
Compare GPT-5.6 Luna and GPT-6 Astra side-by-side. See how these vision models stack up in Classification, Image Captioning, OCR, Object Detection, and Open Prompt.
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Models in this comparison
GPT-5.6 Luna vs GPT-6 Astra on Vision Evals
GPT-6 Astra scores higher on all five Vision Evals tasks.
The widest gap is Reasoning, where GPT-6 Astra leads 87.2% to 60.5%.
Overall, GPT-5.6 Luna averages 64.7% (#29 of 63) against 83.4% (#2 of 63) for GPT-6 Astra.
GPT-5.6 Luna is both cheaper ($0.0011 vs $0.034 per sample) and faster (8.6s vs 9.9s per sample).
GPT-5.6 Luna vs GPT-6 Astra Comparison Table
Evals updated October 13, 2026Pricing updated October 20, 2026
| Property | GPT-5.6 Luna | GPT-6 Astra |
|---|---|---|
| Organization | OpenAI | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Sep 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 | $0.200 | $10.00 |
| Output $/1M | $1.20 | $50.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Promptable Concept Segmentation | Not listed | Demo |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 64.7% | 83.4% |
| Avg cost / sample | $0.0011 | $0.034 |
| Avg speed / sample | 8.60s | 9.94s |
| By task | ||
| Object Detection (low) | 61.0% ±1.2, Mean of 3 runs, range 59.9 to 62.2 | 82.1% ±0.8, Mean of 3 runs, range 81.0 to 82.7 |
| Object Detection (high) | 62.3% ±1.2, Mean of 3 runs, range 61.4 to 63.8 | 83.6% ±0.8, Mean of 3 runs, range 82.8 to 84.5 |
| Counting (low) | 67.1% ±1.4, Mean of 3 runs, range 66.2 to 68.9 | 80.2% ±1.4, Mean of 3 runs, range 78.4 to 81.1 |
| Counting (high) | 70.7% ±3.4, Mean of 3 runs, range 66.2 to 73.0 | 81.1% ±1.4, Mean of 3 runs, range 79.7 to 82.4 |
| Identification (low) | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | 84.4% ±6.3, Mean of 3 runs, range 78.1 to 90.6 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | 51.8% | 77.8% |
| by category |
|
|
| OCR (high) | 55.7% | 77.6% |
| by category |
|
|
| Reasoning (low) | 60.5% ±5.0, Mean of 3 runs, range 55.0 to 64.9 | 87.2% ±1.0, Mean of 3 runs, range 86.1 to 88.1 |
| Reasoning (high) | 65.6% ±3.6, Mean of 3 runs, range 60.9 to 68.2 | 91.2% ±0.3, Mean of 3 runs, range 90.7 to 91.4 |
GPT-5.6 Luna vs GPT-6 Astra: Overview
GPT-5.6 Luna is the fastest and most cost-efficient model in OpenAI's GPT-5.6 family, which also includes Sol (the flagship tier) and Terra (the balanced mid-tier). Introduced under a new naming convention where the generation number (5.6) and a durable capability tier name (Luna, Terra, Sol) together define each model, Luna occupies the lightweight end of the family and is designed for high-volume, latency-sensitive workloads such as summarization, drafting, autocomplete, classification, and routine automation. The GPT-5.6 family as a whole advances capabilities in software engineering, computer use, professional knowledge work, scientific research, and cybersecurity, with all three tiers rated at the "High" capability level under OpenAI's Preparedness Framework for both cybersecurity and biological/chemical risk domains.
GPT-5.6 Luna supports multimodal input and function calling, and shares the family's 1.5 million token context window. On Terminal-Bench 2.1, Luna scores 82.5%, and on the Artificial Analysis Coding Agent Index it outperforms comparable models at roughly one-quarter the estimated cost of higher-tier alternatives. Luna supports the GPT-5.6 prompt caching scheme, which introduces explicit cache breakpoints and a 30-minute minimum cache life. The model was previewed on June 26, 2026 to a limited group of trusted partners via the OpenAI API and Codex, with general availability rolling out on July 9, 2026 across ChatGPT, Codex, and the API.
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 five vision tasks and averages 83.4% (#2 of 63) against 64.7% (#29 of 63) for GPT-5.6 Luna. 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 60.5%. This is the widest gap between the two models across the benchmark's tasks.
GPT-5.6 Luna is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0011 per sample against $0.034. GPT-5.6 Luna is priced at $0.20 per 1M input tokens and $1.20 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-5.6 Luna is faster. Across Roboflow's Vision Evals it averaged 8.6s per inference against 9.9s. 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 image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.