Grok 4.5 vs Muse Spark 1.3
Compare Grok 4.5 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 4.5 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 19.0%.
Overall, Grok 4.5 averages 55.3% (#46 of 63) against 73.9% (#16 of 63) for Muse Spark 1.3.
Grok 4.5 is both cheaper ($0.0078 vs $0.0084 per sample) and faster (16.4s vs 25.5s per sample).
Grok 4.5 vs Muse Spark 1.3 Comparison Table
Evals updated October 13, 2026Pricing updated October 13, 2026
| Property | Grok 4.5 | Muse Spark 1.3 |
|---|---|---|
| Organization | SpaceXAI | Meta |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 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 | 55.3% | 73.9% |
| Avg cost / sample | $0.0078 | $0.0084 |
| Avg speed / sample | 16.41s | 25.50s |
| By task | ||
| Object Detection (low) | 19.0% ±0.8, Mean of 3 runs, range 18.0 to 19.6 | 58.6% ±0.7, Mean of 3 runs, range 58.0 to 59.4 |
| Object Detection (high) | 17.8% ±0.3, Mean of 3 runs, range 17.5 to 18.0 | 56.6% ±2.4, Mean of 3 runs, range 54.5 to 59.3 |
| Counting (low) | 59.5% ±3.4, Mean of 3 runs, range 55.4 to 62.2 | 74.3% ±2.0, Mean of 3 runs, range 73.0 to 77.0 |
| Counting (high) | 57.7% ±3.4, Mean of 3 runs, range 54.0 to 60.8 | 75.7% ±3.4, Mean of 3 runs, range 73.0 to 79.7 |
| Identification (low) | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| Identification (high) | 85.4% ±3.1, Mean of 3 runs, range 81.3 to 87.5 | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 |
| OCR (low) | 57.1% | 70.4% |
| by category |
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| OCR (high) | 57.5% | 69.8% |
| by category |
|
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| Reasoning (low) | 57.6% ±1.7, Mean of 3 runs, range 55.6 to 58.9 | 73.3% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
| Reasoning (high) | 59.8% ±2.6, Mean of 3 runs, range 57.0 to 62.3 | 73.1% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
Grok 4.5 vs Muse Spark 1.3: 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.
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.