Gemma 4 31B vs Muse Spark 1.2
Muse Spark 1.2 averages 73.8% across five Vision Evals tasks and Gemma 4 31B 57.7% across four (#18 and #41 of 65 models), so Muse Spark 1.2 leads overall.
- Muse Spark 1.2 wins all 4 tasks both ran, with its widest lead on counting (76.6% vs 51.4%).
- Gemma 4 31B is about 1/6 of the cost: $0.0015 vs $0.0083 overall average cost per sample.
- Muse Spark 1.2 is faster: 11.5s vs 34.4s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemma 4 31B vs Muse Spark 1.2 on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemma 4 31B vs Muse Spark 1.2 Comparison Table
Evals updated October 26, 2026Pricing updated November 5, 2026
| Property | Gemma 4 31B | Muse Spark 1.2 |
|---|---|---|
| Organization | Meta | |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Aug 2026 |
| Context Window | 256K | 1.0M |
| Parameters | 31B | Unknown |
| License | Apache 2.0 | Proprietary |
| Commercial use | free for commercial use | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.200 | $1.25 |
| Output $/1M | $0.400 | $4.25 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 57.7%* | 73.8% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0015 | $0.0083 |
| Avg speed / sample | 34.36s | 11.51s |
| By task | ||
| Object Detection (low) | 47.5% ±0.6, Mean of 3 runs, range 46.8 to 48.0 | 59.0% ±1.0, Mean of 3 runs, range 58.1 to 60.2 |
| Object Detection (high) | – | 60.5% ±0.3, Mean of 3 runs, range 60.2 to 60.7 |
| Counting (low) | 51.4% ±2.7, Mean of 3 runs, range 48.6 to 54.0 | 76.6% ±2.7, Mean of 3 runs, range 74.3 to 79.7 |
| Counting (high) | – | 75.2% ±2.0, Mean of 3 runs, range 73.0 to 77.0 |
| Identification (low) | 79.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | – | 87.5% ±0.0, Mean of 3 runs, range 87.5 to 87.5 |
| OCR (low) | – | 68.7% |
| by category |
| |
| OCR (high) | – | 68.6% |
| by category |
| |
| Reasoning (low) | 52.8% ±1.3, Mean of 3 runs, range 51.7 to 54.3 | 75.1% ±0.3, Mean of 3 runs, range 74.8 to 75.5 |
| Reasoning (high) | – | 75.7% ±0.3, Mean of 3 runs, range 75.5 to 76.2 |
Gemma 4 31B vs Muse Spark 1.2: 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 1.2 is a proprietary multimodal reasoning model from Meta Superintelligence Labs, released as a coding-focused update to Muse Spark 1.1. It accepts text, images, video, audio, and PDF documents and returns text, with a context window of roughly one million tokens that allows whole repositories, long documents, and extended agent trajectories to be held in a single request. The model thinks before answering, and the amount of reasoning effort it spends is configurable per request. Alongside its visual and document understanding, it supports structured output and parallel function calling, and it is designed to operate either as a planning agent that delegates work or as a subagent executing tasks in parallel.
Training for version 1.2 scaled up compute on coding tasks and widened the diversity of training environments, concentrating on long-horizon work such as whole-repository generation, large end-to-end projects, and automated research. Part of the training data was self-generated, with Muse Spark 1.1 producing coding environments and instruction-following templates and grading candidate solutions against them. The model was co-trained with the Muse Code terminal agent, incorporating rejection-sampled harness trajectories and that toolset. Multimodal use cases documented for the family include visual-to-code generation and detailed image and video captioning.
Frequently Asked Questions
On Roboflow's Vision Evals, Muse Spark 1.2 performed better. It scores higher on all 4 vision tasks and averages 73.8% (#18 of 65) against 57.7% (#41 of 65) 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 Counting benchmark at low effort, Muse Spark 1.2 leads with 76.6% against 51.4%. 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 $0.0015 per sample against $0.0083. Actual costs depend on your image sizes, prompts, and output length.
Muse Spark 1.2 is faster. Across Roboflow's Vision Evals it averaged 11.5s per inference against 34.4s. 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.