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 5 of the six Vision Evals tasks.
The widest gap is Object Detection, where Muse Spark 1.3 leads 58.6% to 20.2%.
Overall, Grok 4.6 averages 69.5% (#25 of 52) against 79.8% (#10 of 52) for Muse Spark 1.3.
Grok 4.6 is both cheaper ($0.0069 vs $0.0075 per sample) and faster (7.4s vs 23.1s per sample).
Grok 4.6 vs Muse Spark 1.3 Comparison Table
Evals updated September 3, 2026Pricing updated September 3, 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 | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $1.25 |
| Output $/1M | $6.00 | $4.25 |
| 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 | 69.5% | 79.8% |
| Avg cost / sample | $0.0069 | $0.0075 |
| Avg speed / sample | 7.39s | 23.14s |
| By task | ||
| Object Detection (low) | 20.2% | 58.6% ±0.7, Mean of 3 runs, range 58.0 to 59.4 |
| Object Detection (high) | – | 56.6% ±2.4, Mean of 3 runs, range 54.5 to 59.3 |
| Counting (low) | 70.3% | 74.3% ±2.0, Mean of 3 runs, range 73.0 to 77.0 |
| Counting (high) | – | 75.7% ±3.4, Mean of 3 runs, range 73.0 to 79.7 |
| Identification (low) | 87.5% | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| Identification (high) | – | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 |
| OCR (low) | 92.0% | 91.3% ±0.5, Mean of 3 runs, range 90.7 to 91.6 |
| OCR (high) | – | 86.9% ±4.1, Mean of 3 runs, range 82.2 to 90.4 |
| Data Extraction (low) | 85.6% | 88.7% ±1.5, Mean of 3 runs, range 86.6 to 89.7 |
| Data Extraction (high) | – | 87.6% ±0.0, Mean of 3 runs, range 87.6 to 87.6 |
| Reasoning (low) | 61.6% | 73.3% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
| Reasoning (high) | 61.6% | 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.
Frequently Asked Questions
On Roboflow's Vision Evals, Muse Spark 1.3 performed better. It scores higher on 5 of the six vision tasks and averages 79.8% (#10 of 52) against 69.5% (#25 of 52) 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, Muse Spark 1.3 leads with 58.6% against 20.2%. 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 $0.0075. Grok 4.6 is priced at $2.00 per 1M input tokens and $6.00 per 1M output; Muse Spark 1.3 is priced at $1.25 per 1M input tokens and $4.25 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 23.1s. 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.