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, and OCR.
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Models in this comparison
Grok 4.5 vs Grok 4.6 on Vision Evals
Grok 4.6 scores higher on 4 of the six Vision Evals tasks.
The widest gap is Counting, where Grok 4.6 leads 70.3% to 55.4%.
Overall, Grok 4.5 averages 64.3% (#24 of 28) against 67.8% (#16 of 28) for Grok 4.6.
Grok 4.6 is both cheaper ($0.0069 vs $0.0077 per sample) and faster (7.4s vs 14.3s per sample).
Grok 4.5 vs Grok 4.6 Comparison Table
Evals updated August 12, 2026Pricing updated August 13, 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 | ||
| 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 | 64.3% | 67.8% |
| Avg cost / sample | $0.0077 | $0.0069 |
| Avg speed / sample | 14.33s | 7.39s |
| By task | ||
| Object Detection | 18.0% $0.0100 | 20.2% $0.0068 |
| Counting | 55.4% $0.0065 | 70.3% $0.0074 |
| Identification | 78.1% $0.0045 | 78.1% $0.0048 |
| OCR | 92.5% $0.0065 | 92.0% $0.0086 |
| Data Extraction | 83.5% $0.0044 | 84.5% $0.0042 |
| Reasoning (low) | 58.3% $0.0076 | 61.6% $0.0087 |
| Reasoning (high) | 59.6% $0.011 | 61.6% $0.027 |
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 rather than producing pixel level outputs such as boxes or masks.
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 4 of the six vision tasks and averages 67.8% (#16 of 28) against 64.3% (#24 of 28) 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, Grok 4.6 leads with 70.3% against 55.4%. 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 $0.0077. 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 7.4s per inference against 14.3s. 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 image captioning and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.