GPT-6 Astra vs Grok 4.7
Compare GPT-6 Astra and Grok 4.7 side-by-side.
Compare GPT-6 Astra vs Grok 4.7 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 Grok 4.7 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 40.4%.
Overall, GPT-6 Astra averages 86.6% (#1 of 54) against 71.9% (#22 of 54) for Grok 4.7.
Grok 4.7 is cheaper ($0.012 vs $0.030 per sample), while GPT-6 Astra is faster (6.7s vs 23.6s per sample).
GPT-6 Astra vs Grok 4.7 Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | GPT-6 Astra | Grok 4.7 |
|---|---|---|
| Organization | OpenAI | SpaceXAI |
| Category | closed | closed |
| Modality | multimodal | — |
| Release Date | Sep 2026 | Sep 2026 |
| Context Window | 1.1M | 500K |
| Parameters | Undisclosed | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $10.00 | $1.60 |
| Output $/1M | $50.00 | $4.80 |
| 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% | 71.9% |
| Avg cost / sample | $0.030 | $0.012 |
| Avg speed / sample | 6.67s | 23.55s |
| By task | ||
| Object Detection (low) | 82.1% ±0.8, Mean of 3 runs, range 81.0 to 82.7 | 40.4% ±0.6, Mean of 3 runs, range 39.8 to 41.0 |
| Object Detection (high) | 83.6% ±0.8, Mean of 3 runs, range 82.8 to 84.5 | 41.2% ±1.6, Mean of 3 runs, range 39.6 to 42.8 |
| Counting (low) | 80.2% ±1.4, Mean of 3 runs, range 78.4 to 81.1 | 61.7% ±1.3, Mean of 3 runs, range 60.8 to 63.5 |
| Counting (high) | 81.1% ±1.4, Mean of 3 runs, range 79.7 to 82.4 | 60.8% ±1.3, Mean of 3 runs, range 59.5 to 62.2 |
| Identification (low) | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| Identification (high) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 |
| OCR (low) | 91.9% ±0.2, Mean of 3 runs, range 91.6 to 92.1 | 92.6% ±0.7, Mean of 3 runs, range 92.1 to 93.4 |
| OCR (high) | 91.5% ±0.2, Mean of 3 runs, range 91.3 to 91.7 | 93.5% ±0.3, Mean of 3 runs, range 93.1 to 93.8 |
| Data Extraction (low) | 88.7% ±1.0, Mean of 3 runs, range 87.6 to 89.7 | 84.9% ±2.6, Mean of 3 runs, range 82.5 to 87.6 |
| Data Extraction (high) | 91.1% ±1.0, Mean of 3 runs, range 89.7 to 91.8 | 87.6% ±1.5, Mean of 3 runs, range 86.6 to 89.7 |
| Reasoning (low) | 87.2% ±1.0, Mean of 3 runs, range 86.1 to 88.1 | 64.2% ±2.3, Mean of 3 runs, range 62.3 to 66.9 |
| Reasoning (high) | 91.2% ±0.3, Mean of 3 runs, range 90.7 to 91.4 | 66.9% ±1.3, Mean of 3 runs, range 65.6 to 68.2 |
GPT-6 Astra vs Grok 4.7: 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 4.7 is a proprietary model from SpaceXAI, released on September 21, 2026. It accepts text and images as input and returns text. It extends Grok 4.6 and is listed at the same API price.
Its Vision Evals scores are on the leaderboard. Running it in the Playground is not available yet, because the inference workflow is not ready.
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 54) against 71.9% (#22 of 54) for Grok 4.7. 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 40.4%. This is the widest gap between the two models across the benchmark's tasks.
Grok 4.7 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.012 per sample against $0.030. GPT-6 Astra is priced at $10.00 per 1M input tokens and $50.00 per 1M output; Grok 4.7 is priced at $1.60 per 1M input tokens and $4.80 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 23.6s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.