Grok 4.6 vs Grok 4.7
Compare Grok 4.6 and Grok 4.7 side-by-side.
Compare Grok 4.6 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
Grok 4.6 vs Grok 4.7 on Vision Evals
Grok 4.7 scores higher on 4 of the six Vision Evals tasks.
The widest gap is Object Detection, where Grok 4.7 leads 40.4% to 23.8%.
Overall, Grok 4.6 averages 68.7% (#28 of 54) against 71.9% (#22 of 54) for Grok 4.7.
Grok 4.6 is both cheaper ($0.0097 vs $0.012 per sample) and faster (17.5s vs 23.6s per sample).
Grok 4.6 vs Grok 4.7 Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | Grok 4.6 | Grok 4.7 |
|---|---|---|
| Organization | SpaceXAI | SpaceXAI |
| Category | closed | closed |
| Modality | multimodal | — |
| Release Date | Aug 2026 | Sep 2026 |
| Context Window | 500K | 500K |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $1.60 |
| Output $/1M | $6.00 | $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 | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 68.7% | 71.9% |
| Avg cost / sample | $0.0097 | $0.012 |
| Avg speed / sample | 17.55s | 23.55s |
| By task | ||
| Object Detection (low) | 23.8% ±2.8, Mean of 3 runs, range 20.2 to 25.9 | 40.4% ±0.6, Mean of 3 runs, range 39.8 to 41.0 |
| Object Detection (high) | 24.0% ±1.0, Mean of 3 runs, range 23.1 to 25.1 | 41.2% ±1.6, Mean of 3 runs, range 39.6 to 42.8 |
| Counting (low) | 65.8% ±4.1, Mean of 3 runs, range 62.2 to 70.3 | 61.7% ±1.3, Mean of 3 runs, range 60.8 to 63.5 |
| Counting (high) | 56.8% ±1.4, Mean of 3 runs, range 55.4 to 58.1 | 60.8% ±1.3, Mean of 3 runs, range 59.5 to 62.2 |
| Identification (low) | 84.4% ±3.1, Mean of 3 runs, range 81.3 to 87.5 | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| Identification (high) | 85.4% ±1.6, Mean of 3 runs, range 84.4 to 87.5 | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 |
| OCR (low) | 91.8% ±0.3, Mean of 3 runs, range 91.5 to 92.1 | 92.6% ±0.7, Mean of 3 runs, range 92.1 to 93.4 |
| OCR (high) | 91.6% ±0.2, Mean of 3 runs, range 91.4 to 91.7 | 93.5% ±0.3, Mean of 3 runs, range 93.1 to 93.8 |
| Data Extraction (low) | 85.6% ±1.0, Mean of 3 runs, range 84.5 to 86.6 | 84.9% ±2.6, Mean of 3 runs, range 82.5 to 87.6 |
| Data Extraction (high) | 85.6% ±1.0, Mean of 3 runs, range 84.5 to 86.6 | 87.6% ±1.5, Mean of 3 runs, range 86.6 to 89.7 |
| Reasoning (low) | 61.1% ±1.3, Mean of 3 runs, range 59.6 to 62.3 | 64.2% ±2.3, Mean of 3 runs, range 62.3 to 66.9 |
| Reasoning (high) | 63.8% ±2.0, Mean of 3 runs, range 62.3 to 66.2 | 66.9% ±1.3, Mean of 3 runs, range 65.6 to 68.2 |
Grok 4.6 vs Grok 4.7: Overview
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.
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, Grok 4.7 performed better. It scores higher on 4 of the six vision tasks and averages 71.9% (#22 of 54) against 68.7% (#28 of 54) for Grok 4.6. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, Grok 4.7 leads with 40.4% against 23.8%. 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 $0.0097 per sample against $0.012. Grok 4.6 is priced at $2.00 per 1M input tokens and $6.00 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.
Grok 4.6 is faster. Across Roboflow's Vision Evals it averaged 17.5s per inference against 23.6s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.