GPT-6 Astra vs GPT-6 Sol
Compare GPT-6 Astra and GPT-6 Sol side-by-side.
Compare GPT-6 Astra 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.
Models in this comparison
GPT-6 Astra vs GPT-6 Sol on Vision Evals
GPT-6 Astra scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where GPT-6 Astra leads 87.2% to 72.2%.
Overall, GPT-6 Astra averages 86.6% (#1 of 57) against 80.7% (#10 of 57) for GPT-6 Sol.
GPT-6 Sol is cheaper ($0.0065 vs $0.030 per sample), while GPT-6 Astra is faster (6.7s vs 8.1s per sample).
GPT-6 Astra vs GPT-6 Sol Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | GPT-6 Astra | GPT-6 Sol |
|---|---|---|
| Organization | OpenAI | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Sep 2026 |
| Context Window | 1.1M | 1.1M |
| Parameters | Undisclosed | undisclosed |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $10.00 | |
| Output $/1M | $50.00 | |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Promptable Concept Segmentation | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 86.6% | 80.7% |
| Avg cost / sample | $0.030 | $0.0065 |
| Avg speed / sample | 6.67s | 8.15s |
| By task | ||
| Object Detection (low) | 82.1% ±0.8, Mean of 3 runs, range 81.0 to 82.7 | 73.6% ±0.6, Mean of 3 runs, range 72.9 to 74.2 |
| Object Detection (high) | 83.6% ±0.8, Mean of 3 runs, range 82.8 to 84.5 | 75.2% ±0.9, Mean of 3 runs, range 74.1 to 75.9 |
| Counting (low) | 80.2% ±1.4, Mean of 3 runs, range 78.4 to 81.1 | 74.8% ±2.7, Mean of 3 runs, range 71.6 to 77.0 |
| Counting (high) | 81.1% ±1.4, Mean of 3 runs, range 79.7 to 82.4 | 76.1% ±3.4, Mean of 3 runs, range 71.6 to 78.4 |
| Identification (low) | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | 91.7% ±3.1, Mean of 3 runs, range 87.5 to 93.8 |
| Identification (high) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | 91.9% ±0.2, Mean of 3 runs, range 91.6 to 92.1 | 91.7% ±0.4, Mean of 3 runs, range 91.3 to 92.1 |
| OCR (high) | 91.5% ±0.2, Mean of 3 runs, range 91.3 to 91.7 | 91.9% ±0.3, Mean of 3 runs, range 91.6 to 92.2 |
| Data Extraction (low) | 88.7% ±1.0, Mean of 3 runs, range 87.6 to 89.7 | 80.4% ±0.0, Mean of 3 runs, range 80.4 to 80.4 |
| Data Extraction (high) | 91.1% ±1.0, Mean of 3 runs, range 89.7 to 91.8 | 82.5% ±1.0, Mean of 3 runs, range 81.4 to 83.5 |
| Reasoning (low) | 87.2% ±1.0, Mean of 3 runs, range 86.1 to 88.1 | 72.2% ±1.7, Mean of 3 runs, range 70.9 to 74.2 |
| Reasoning (high) | 91.2% ±0.3, Mean of 3 runs, range 90.7 to 91.4 | 77.9% ±2.6, Mean of 3 runs, range 75.5 to 80.8 |
GPT-6 Astra vs GPT-6 Sol: Overview
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.
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. OpenAI reports results including 33.2% on AutomationBench at xhigh effort and 56.4% on Agents' Last Exam at max effort, while its reported DeepSWE and OSWorld 2.0 figures of 68.8% and 64.4% fall below those of the earlier GPT-5.6 Sol. 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 Astra performed better. It scores higher on 5 of the six vision tasks and averages 86.6% (#1 of 57) against 80.7% (#10 of 57) 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 Reasoning benchmark at low effort, GPT-6 Astra leads with 87.2% against 72.2%. This is the widest gap between the two models across the benchmark's tasks.
GPT-6 Sol is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0065 per sample against $0.030. 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 8.1s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.