GPT-6 Astra vs Qwen3.8 Max
Compare GPT-6 Astra and Qwen3.8 Max side-by-side. See how these vision models stack up in Object Detection, OCR, Open Prompt, Classification, and Image Captioning.
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Models in this comparison
GPT-6 Astra vs Qwen3.8 Max on Vision Evals
GPT-6 Astra scores higher on 4 of the six Vision Evals tasks.
The widest gap is Reasoning, where GPT-6 Astra leads 87.2% to 75.9%.
Overall, GPT-6 Astra averages 86.6% (#1 of 53) against 83.9% (#5 of 53) for Qwen3.8 Max.
Qwen3.8 Max is cheaper ($0.0074 vs $0.030 per sample), while GPT-6 Astra is faster (6.7s vs 17.3s per sample).
GPT-6 Astra vs Qwen3.8 Max Comparison Table
Evals updated September 5, 2026Pricing updated September 5, 2026
| Property | GPT-6 Astra | Qwen3.8 Max |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Aug 2026 |
| Context Window | 1.1M | 984K |
| Parameters | Undisclosed | 2.4T total, ~95B active |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $10.00 | |
| Output $/1M | $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 | 86.6% | 83.9% |
| Avg cost / sample | $0.030 | $0.0074 |
| Avg speed / sample | 6.67s | 17.25s |
| By task | ||
| Object Detection (low) | 82.1% ±0.8, Mean of 3 runs, range 81.0 to 82.7 | 76.7% ±0.3, Mean of 3 runs, range 76.5 to 77.1 |
| Object Detection (high) | 83.6% ±0.8, Mean of 3 runs, range 82.8 to 84.5 | 78.4% ±0.4, Mean of 3 runs, range 78.1 to 78.9 |
| Counting (low) | 80.2% ±1.4, Mean of 3 runs, range 78.4 to 81.1 | 81.1% ±2.0, Mean of 3 runs, range 78.4 to 82.4 |
| Counting (high) | 81.1% ±1.4, Mean of 3 runs, range 79.7 to 82.4 | 81.1% ±0.0, Mean of 3 runs, range 81.1 to 81.1 |
| Identification (low) | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | 91.9% ±0.2, Mean of 3 runs, range 91.6 to 92.1 | 93.3% ±0.5, Mean of 3 runs, range 92.8 to 93.9 |
| OCR (high) | 91.5% ±0.2, Mean of 3 runs, range 91.3 to 91.7 | 91.3% ±0.5, Mean of 3 runs, range 90.7 to 91.7 |
| Data Extraction (low) | 88.7% ±1.0, Mean of 3 runs, range 87.6 to 89.7 | 87.6% ±0.0, Mean of 3 runs, range 87.6 to 87.6 |
| Data Extraction (high) | 91.1% ±1.0, Mean of 3 runs, range 89.7 to 91.8 | 89.3% ±1.0, Mean of 3 runs, range 88.7 to 90.7 |
| Reasoning (low) | 87.2% ±1.0, Mean of 3 runs, range 86.1 to 88.1 | 75.9% ±2.0, Mean of 3 runs, range 73.5 to 77.5 |
| Reasoning (high) | 91.2% ±0.3, Mean of 3 runs, range 90.7 to 91.4 | 80.3% ±2.0, Mean of 3 runs, range 78.2 to 82.1 |
GPT-6 Astra vs Qwen3.8 Max: 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.
Qwen3.8 Max is the flagship tier of Alibaba's Qwen3.8 family, a sparse mixture-of-experts multimodal model with roughly 2.4 trillion total parameters of which about 95 billion activate per token, which keeps serving cost and latency well below what the total parameter count would imply. It builds on the architectural foundation established by Qwen3.5 and accepts text, images, video, and documents as input while producing text output. Reported context handling reaches close to one million tokens, with a maximum generation length of 131,072 tokens, so the model is aimed at long-horizon agentic work such as repository-scale coding, multi-step research, data analysis, and office document workflows.
For vision work the model performs image and video understanding, document and chart interpretation, text recognition inside images, and grounded visual question answering, and Alibaba reports gains concentrated in multimodal and agentic evaluation categories rather than general reasoning. Published figures include 86.6 on Terminal-Bench 2.1, 67.7 on SWE-bench Pro, 93.0 on PaperBench, 82.8 on IFBench, and 92.6 on GPQA Diamond. It is the first model in the Max tier of the Qwen line for which the team states weights will be released publicly, alongside a smaller Qwen3.8 27B checkpoint. No training or safety model card has been published.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6 Astra performed better. It scores higher on 4 of the six vision tasks and averages 86.6% (#1 of 53) against 83.9% (#5 of 53) for Qwen3.8 Max. 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 75.9%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.8 Max is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0074 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 17.3s. 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 object detection and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.