Grok 4.6 vs Muse Spark 1.3
Compare Grok 4.6 and Muse Spark 1.3 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 4.6 vs Muse Spark 1.3 on Vision Evals
Muse Spark 1.3 scores higher on all five Vision Evals tasks.
The widest gap is Object Detection, where Muse Spark 1.3 leads 58.6% to 23.8%.
Overall, Grok 4.6 averages 59.1% (#36 of 63) against 73.9% (#16 of 63) for Muse Spark 1.3.
Muse Spark 1.3 is cheaper ($0.0084 vs $0.011 per sample), while Grok 4.6 is faster (19.3s vs 25.5s per sample).
Grok 4.6 vs Muse Spark 1.3 Comparison Table
Evals updated October 13, 2026Pricing updated October 16, 2026
| Property | Grok 4.6 | Muse Spark 1.3 |
|---|---|---|
| Organization | SpaceXAI | Meta |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Aug 2026 | Sep 2026 |
| Context Window | 500K | 1.0M |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $1.25 |
| Output $/1M | $6.00 | $4.25 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 59.1% | 73.9% |
| Avg cost / sample | $0.011 | $0.0084 |
| Avg speed / sample | 19.32s | 25.50s |
| By task | ||
| Object Detection (low) | 23.8% ±2.8, Mean of 3 runs, range 20.2 to 25.9 | 58.6% ±0.7, Mean of 3 runs, range 58.0 to 59.4 |
| Object Detection (high) | 24.0% ±1.0, Mean of 3 runs, range 23.1 to 25.1 | 56.6% ±2.4, Mean of 3 runs, range 54.5 to 59.3 |
| Counting (low) | 65.8% ±4.1, Mean of 3 runs, range 62.2 to 70.3 | 74.3% ±2.0, Mean of 3 runs, range 73.0 to 77.0 |
| Counting (high) | 56.8% ±1.4, Mean of 3 runs, range 55.4 to 58.1 | 75.7% ±3.4, Mean of 3 runs, range 73.0 to 79.7 |
| Identification (low) | 84.4% ±3.1, Mean of 3 runs, range 81.3 to 87.5 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| Identification (high) | 85.4% ±1.6, Mean of 3 runs, range 84.4 to 87.5 | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 |
| OCR (low) | 60.6% | 70.4% |
| by category |
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| OCR (high) | 61.2% | 69.8% |
| by category |
|
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| Reasoning (low) | 61.1% ±1.3, Mean of 3 runs, range 59.6 to 62.3 | 73.3% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
| Reasoning (high) | 63.8% ±2.0, Mean of 3 runs, range 62.3 to 66.2 | 73.1% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
Grok 4.6 vs Muse Spark 1.3: 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.
Muse Spark 1.3 is a proprietary multimodal reasoning model from Meta Superintelligence Labs and the fourth Muse Spark release in five months, arriving on September 2, 2026. It takes text, images, video, and document files as input and returns text, and it operates over a context window of 1,048,576 tokens. Meta trains the model for long-horizon agentic work, so it carries accumulated context and prior tool results forward across many turns, reconciles messy or conflicting inputs, and asks for clarification when a task is underspecified. Visual inputs such as screenshots and video clips feed a reasoning loop that runs against a real execution environment rather than a scripted sequence of steps.
The model exposes graded reasoning effort settings. An xhigh configuration is generally available at launch, while a max reasoning configuration aimed at harder reasoning and agentic problems arrives after further safety testing. Artificial Analysis measures Muse Spark 1.3 (max) at 62 on its Intelligence Index and the xhigh configuration at 61, with agentic tool-use evaluations driving most of the gain over Muse Spark 1.2; max reaches 52% on Tau3-Bench Banking by spending more turns and reasoning tokens than xhigh. Prior Muse Spark versions emit bounding box coordinates, transcriptions, and structured field extractions from images on Roboflow Vision Evals.