GPT-6 Astra vs Grok 4.6
Compare GPT-6 Astra and Grok 4.6 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 Grok 4.6 on Vision Evals
GPT-6 Astra scores higher on 5 of the six Vision Evals tasks.
The widest gap is Object Detection, where GPT-6 Astra leads 82.1% to 20.2%.
Overall, GPT-6 Astra averages 86.6% (#1 of 53) against 69.5% (#25 of 53) for Grok 4.6.
Grok 4.6 is cheaper ($0.0069 vs $0.030 per sample), while GPT-6 Astra is faster (6.7s vs 7.4s per sample).
GPT-6 Astra vs Grok 4.6 Comparison Table
Evals updated September 5, 2026Pricing updated September 5, 2026
| Property | GPT-6 Astra | Grok 4.6 |
|---|---|---|
| Organization | OpenAI | SpaceXAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Aug 2026 |
| Context Window | 1.1M | 500K |
| Parameters | Undisclosed | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $10.00 | $2.00 |
| Output $/1M | $50.00 | $6.00 |
| 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% | 69.5% |
| Avg cost / sample | $0.030 | $0.0069 |
| Avg speed / sample | 6.67s | 7.39s |
| By task | ||
| Object Detection (low) | 82.1% ±0.8, Mean of 3 runs, range 81.0 to 82.7 | 20.2% |
| Object Detection (high) | 83.6% ±0.8, Mean of 3 runs, range 82.8 to 84.5 | – |
| Counting (low) | 80.2% ±1.4, Mean of 3 runs, range 78.4 to 81.1 | 70.3% |
| Counting (high) | 81.1% ±1.4, Mean of 3 runs, range 79.7 to 82.4 | – |
| Identification (low) | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | 87.5% |
| Identification (high) | 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 | 92.0% |
| OCR (high) | 91.5% ±0.2, Mean of 3 runs, range 91.3 to 91.7 | – |
| Data Extraction (low) | 88.7% ±1.0, Mean of 3 runs, range 87.6 to 89.7 | 85.6% |
| Data Extraction (high) | 91.1% ±1.0, Mean of 3 runs, range 89.7 to 91.8 | – |
| Reasoning (low) | 87.2% ±1.0, Mean of 3 runs, range 86.1 to 88.1 | 61.6% |
| Reasoning (high) | 91.2% ±0.3, Mean of 3 runs, range 90.7 to 91.4 | 61.6% |
GPT-6 Astra vs Grok 4.6: 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.
Grok 4.6 is a proprietary reasoning model from xAI aimed at long-running agentic workflows, coding, and knowledge work. It accepts text and image input and returns text, with a 500,000 token context window and a knowledge cutoff of February 1, 2026. The model exposes an adjustable reasoning budget with low, medium, high, and xhigh settings, where high is the default, and it supports function calling, structured outputs, web and X search, and code execution as documented tool behaviors. Its visual capability covers interpreting images supplied alongside text prompts, which places it in the visual question answering and document understanding family, and it can also return object detection boxes as text coordinates when prompted.
xAI characterizes Grok 4.6 as the result of an extended post-training run over the Grok 4.5 lineage rather than a new pretrained base. The described recipe combines curated model-generated reasoning and technical data, engineering data, a revised optimizer, regenerated supervised fine-tuning trajectories, and reinforcement learning across agent environments spanning knowledge work, coding, kernel optimization, web development, and computer-aided design. Parameter count and architecture specifics are not disclosed. Independent measurement from Artificial Analysis places the model at 61 on its Intelligence Index, five points above Grok 4.5.
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 53) against 69.5% (#25 of 53) for Grok 4.6. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Object Detection benchmark at low effort, GPT-6 Astra leads with 82.1% against 20.2%. This is the widest gap between the two models across the benchmark's tasks.
Grok 4.6 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0069 per sample against $0.030. GPT-6 Astra is priced at $10.00 per 1M input tokens and $50.00 per 1M output; Grok 4.6 is priced at $2.00 per 1M input tokens and $6.00 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 7.4s. 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.