Gemma 4 31B vs Muse Spark 1.3
Compare Gemma 4 31B and Muse Spark 1.3 side-by-side. See how these vision models stack up in Image Captioning, OCR, Open Prompt, Object Detection, and Classification.
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Models in this comparison
Gemma 4 31B vs Muse Spark 1.3 on Vision Evals
Muse Spark 1.3 scores higher on all six Vision Evals tasks.
The widest gap is Reasoning, where Muse Spark 1.3 leads 73.3% to 49.0%.
Overall, Gemma 4 31B averages 67.1% (#29 of 52) against 79.8% (#10 of 52) for Muse Spark 1.3.
Gemma 4 31B is cheaper ($0.0011 vs $0.0075 per sample), while Muse Spark 1.3 is faster (23.1s vs 29.7s per sample).
Gemma 4 31B vs Muse Spark 1.3 Comparison Table
Evals updated September 3, 2026Pricing updated September 3, 2026
| Property | Gemma 4 31B | Muse Spark 1.3 |
|---|---|---|
| Organization | Meta | |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Sep 2026 |
| Context Window | 256K | 1.0M |
| Parameters | 31B | |
| License | Apache 2.0 | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.090 | $1.25 |
| Output $/1M | $0.340 | $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 | 67.1% | 79.8% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0011 | $0.0075 |
| Avg speed / sample | 29.72s | 23.14s |
| By task | ||
| Object Detection (low) | 48.0% | 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) | 52.7% | 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) | 84.4% | 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) | 85.6% | 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) | 82.7% | 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) | 49.0% | 73.3% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
| Reasoning (high) | – | 73.1% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
Gemma 4 31B vs Muse Spark 1.3: Overview
Gemma 4 31B is the largest dense model in Google's Gemma 4 family, built from the same research as Gemini 3 and released as open weights under the Apache 2.0 license. It supports a 256K token context window with text and image input, configurable thinking mode for step-by-step reasoning, and multilingual support across 140+ languages. The unquantized model fits on a single 80GB GPU.
For vision tasks, Gemma 4 31B supports image understanding with variable aspect ratios and resolutions, and can output structured bounding boxes for UI element detection, making it useful for document parsing and UI understanding. Compared to Gemma 3, it delivers stronger reasoning and multimodal performance. It is part of a four-size family alongside the 26B A4B MoE variant and two on-device models (E2B, E4B), with the 31B dense variant optimized for output quality and fine-tuning over inference speed.
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 all six vision tasks and averages 79.8% (#10 of 52) against 67.1% (#29 of 52) for Gemma 4 31B. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Reasoning benchmark at low effort, Muse Spark 1.3 leads with 73.3% against 49.0%. This is the widest gap between the two models across the benchmark's tasks.
Gemma 4 31B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0011 per sample against $0.0075. Actual costs depend on your image sizes, prompts, and output length.
Muse Spark 1.3 is faster. Across Roboflow's Vision Evals it averaged 23.1s per inference against 29.7s. 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 OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.