Muse Spark 1.1 vs Muse Spark 1.3
Compare Muse Spark 1.1 and Muse Spark 1.3 side-by-side. See how these vision models stack up in OCR, Classification, Image Captioning, Object Detection, and Open Prompt.
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Models in this comparison
Muse Spark 1.1 vs Muse Spark 1.3 on Vision Evals
Muse Spark 1.1 scores higher on 4 of the six Vision Evals tasks.
The widest gap is Counting, where Muse Spark 1.1 leads 76.6% to 74.3%.
Overall, Muse Spark 1.1 averages 80.5% (#8 of 52) against 79.8% (#10 of 52) for Muse Spark 1.3.
Muse Spark 1.1 is both cheaper ($0.0067 vs $0.0075 per sample) and faster (7.1s vs 23.1s per sample).
Muse Spark 1.1 vs Muse Spark 1.3 Comparison Table
Evals updated September 3, 2026Pricing updated September 3, 2026
| Property | Muse Spark 1.1 | Muse Spark 1.3 |
|---|---|---|
| Organization | Meta | Meta |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Sep 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | $1.25 |
| Output $/1M | $4.25 | $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 | 80.5% | 79.8% |
| Avg cost / sample | $0.0067 | $0.0075 |
| Avg speed / sample | 7.07s | 23.14s |
| By task | ||
| Object Detection (low) | 60.6% ±1.9, Mean of 3 runs, range 58.4 to 62.1 | 58.6% ±0.7, Mean of 3 runs, range 58.0 to 59.4 |
| Object Detection (high) | 60.0% ±0.5, Mean of 3 runs, range 59.6 to 60.7 | 56.6% ±2.4, Mean of 3 runs, range 54.5 to 59.3 |
| Counting (low) | 76.6% ±1.3, Mean of 3 runs, range 75.7 to 78.4 | 74.3% ±2.0, Mean of 3 runs, range 73.0 to 77.0 |
| Counting (high) | 76.1% ±0.7, Mean of 3 runs, range 75.7 to 77.0 | 75.7% ±3.4, Mean of 3 runs, range 73.0 to 79.7 |
| Identification (low) | 91.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| Identification (high) | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 |
| OCR (low) | 92.6% ±0.5, Mean of 3 runs, range 92.1 to 93.1 | 91.3% ±0.5, Mean of 3 runs, range 90.7 to 91.6 |
| OCR (high) | 92.8% ±0.3, Mean of 3 runs, range 92.6 to 93.2 | 86.9% ±4.1, Mean of 3 runs, range 82.2 to 90.4 |
| Data Extraction (low) | 87.6% ±1.0, Mean of 3 runs, range 86.6 to 88.7 | 88.7% ±1.5, Mean of 3 runs, range 86.6 to 89.7 |
| Data Extraction (high) | 89.0% ±1.0, Mean of 3 runs, range 87.6 to 89.7 | 87.6% ±0.0, Mean of 3 runs, range 87.6 to 87.6 |
| Reasoning (low) | 74.2% ±1.7, Mean of 3 runs, range 72.2 to 75.5 | 73.3% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
| Reasoning (high) | 76.6% ±1.3, Mean of 3 runs, range 75.5 to 78.2 | 73.1% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
Muse Spark 1.1 vs Muse Spark 1.3: Overview
Muse Spark 1.1 is a natively multimodal reasoning model from Meta Superintelligence Labs, released on July 9, 2026, as a significant upgrade to the original Muse Spark. The model accepts text, image, video, PDF, and audio as input and produces text output. It operates with a 1-million-token context window (1,048,576 tokens per the Meta Model API documentation) and is designed specifically for agentic tasks that require planning, tool use, computer use, and multi-agent orchestration. The model runs in a "Thinking" mode, where adjustable reasoning effort is applied before generating a response. It can function both as a main agent gathering context, forming plans, and delegating to parallel subagents and as a subagent that adheres to assigned tasks and escalates when needed. It is trained to decide autonomously when to write automation scripts versus interact directly with a user interface.
Muse Spark 1.1 supports a range of multimodal capabilities including visual perception, image and video captioning, visual-to-code generation, and document analysis. The model was evaluated under Meta's Advanced AI Scaling Framework across frontier risk categories including chemical and biological threats, cybersecurity, and loss-of-control scenarios. Parameter count, architecture details, and training data composition are not publicly disclosed. The model is proprietary and closed-weight, accessible to consumers through the Meta AI app and to developers via the Meta Model API, which launched in public preview alongside this release.
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 1.1 performed better. It scores higher on 4 of the six vision tasks and averages 80.5% (#8 of 52) against 79.8% (#10 of 52) for Muse Spark 1.3. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Counting benchmark at low effort, Muse Spark 1.1 leads with 76.6% against 74.3%. This is the widest gap between the two models across the benchmark's tasks.
Muse Spark 1.1 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0067 per sample against $0.0075. Muse Spark 1.1 is priced at $1.25 per 1M input tokens and $4.25 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.
Muse Spark 1.1 is faster. Across Roboflow's Vision Evals it averaged 7.1s 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 OCR and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.