Grok 4.5 vs Grok 4.6
Compare Grok 4.5 and Grok 4.6 side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, OCR, and Object Detection.
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Models in this comparison
Grok 4.5 vs Grok 4.6 on Vision Evals
Grok 4.6 scores higher on 5 of the six Vision Evals tasks.
The widest gap is Counting, where Grok 4.6 leads 65.8% to 59.5%.
Overall, Grok 4.5 averages 65.8% (#40 of 59) against 68.7% (#31 of 59) for Grok 4.6.
Grok 4.5 is cheaper ($0.0084 vs $0.0097 per sample), while Grok 4.6 is faster (17.5s vs 20.8s per sample).
Grok 4.5 vs Grok 4.6 Comparison Table
Evals updated September 27, 2026Pricing updated September 27, 2026
| Property | Grok 4.5 | Grok 4.6 |
|---|---|---|
| Organization | SpaceXAI | SpaceXAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Aug 2026 |
| Context Window | 500K | 500K |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $2.00 |
| Output $/1M | $6.00 | $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 | 65.8% | 68.7% |
| Avg cost / sample | $0.0084 | $0.0097 |
| Avg speed / sample | 20.75s | 17.55s |
| By task | ||
| Object Detection (low) | 19.0% ±0.8, Mean of 3 runs, range 18.0 to 19.6 | 23.8% ±2.8, Mean of 3 runs, range 20.2 to 25.9 |
| Object Detection (high) | 17.8% ±0.3, Mean of 3 runs, range 17.5 to 18.0 | 24.0% ±1.0, Mean of 3 runs, range 23.1 to 25.1 |
| Counting (low) | 59.5% ±3.4, Mean of 3 runs, range 55.4 to 62.2 | 65.8% ±4.1, Mean of 3 runs, range 62.2 to 70.3 |
| Counting (high) | 57.7% ±3.4, Mean of 3 runs, range 54.0 to 60.8 | 56.8% ±1.4, Mean of 3 runs, range 55.4 to 58.1 |
| Identification (low) | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 | 84.4% ±3.1, Mean of 3 runs, range 81.3 to 87.5 |
| Identification (high) | 85.4% ±3.1, Mean of 3 runs, range 81.3 to 87.5 | 85.4% ±1.6, Mean of 3 runs, range 84.4 to 87.5 |
| OCR (low) | 92.1% ±0.3, Mean of 3 runs, range 91.9 to 92.5 | 91.8% ±0.3, Mean of 3 runs, range 91.5 to 92.1 |
| OCR (high) | 92.3% ±0.5, Mean of 3 runs, range 91.9 to 92.9 | 91.6% ±0.2, Mean of 3 runs, range 91.4 to 91.7 |
| Data Extraction (low) | 83.5% ±1.6, Mean of 3 runs, range 81.4 to 84.5 | 85.6% ±1.0, Mean of 3 runs, range 84.5 to 86.6 |
| Data Extraction (high) | 81.8% ±1.6, Mean of 3 runs, range 80.4 to 83.5 | 85.6% ±1.0, Mean of 3 runs, range 84.5 to 86.6 |
| Reasoning (low) | 57.6% ±1.7, Mean of 3 runs, range 55.6 to 58.9 | 61.1% ±1.3, Mean of 3 runs, range 59.6 to 62.3 |
| Reasoning (high) | 59.8% ±2.6, Mean of 3 runs, range 57.0 to 62.3 | 63.8% ±2.0, Mean of 3 runs, range 62.3 to 66.2 |
Grok 4.5 vs Grok 4.6: Overview
Grok 4.5 is a proprietary reasoning model from SpaceXAI (xAI) that accepts interleaved text and image input and returns text, with a 500,000 token context window. xAI positions it as a model for coding, agentic software work, and knowledge tasks, and states it was trained in the company's Memphis data centers on datasets spanning science, engineering, and mathematics. Its reinforcement learning stage covers hundreds of thousands of multi step software engineering tasks scored by automated checks and model based grading, and training is reported to have run on tens of thousands of NVIDIA GB300 GPUs using an asynchronous scheme in which multi hour agentic rollouts continue while learning proceeds in parallel, targeting long horizon autonomous operation rather than single turn inference.
For vision, the model consumes JPEG and PNG images in any order relative to text prompts, covering visual question answering, description of chart and document imagery, and reading text rendered inside a scene. Reasoning effort is configurable, and the model supports function calling and structured outputs, so image inputs can be interleaved with tool calls inside agent loops. xAI has not published a technical report, architecture details, or parameter count, and reported mixture of experts sizing figures come from secondary coverage rather than official documentation.
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, Grok 4.6 performed better. It scores higher on 5 of the six vision tasks and averages 68.7% (#31 of 59) against 65.8% (#40 of 59) for Grok 4.5. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Counting benchmark at low effort, Grok 4.6 leads with 65.8% against 59.5%. This is the widest gap between the two models across the benchmark's tasks.
Grok 4.5 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0084 per sample against $0.0097. Grok 4.5 is priced at $2.00 per 1M input tokens and $6.00 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.
Grok 4.6 is faster. Across Roboflow's Vision Evals it averaged 17.5s per inference against 20.8s. 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 image captioning and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.