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Gemma 4 E2B vs Muse Spark 1.3

Muse Spark 1.3 leads the LLM Stats Score 55.1 to 6.8.

Google · Meta · Updated for 2026

Which is better?

Muse Spark 1.3 leads the overall LLM Stats Score 55.1 to 6.8, ranking #4 overall.

In the 1 individual benchmarks reported for both models, Muse Spark 1.3 wins 1; this is a narrower head-to-head signal than the composite indexes.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose Gemma 4 E2B

  • you need open weights you can self-host or fine-tune

Choose Muse Spark 1.3

  • overall performance matters — it scores 55.1 and ranks #4 on LLM Stats
  • your work emphasizes reasoning and agents — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results
  • you want the most recent training data — it shipped Sep 2026

At a glance

The differences that matter most.

Gemma 4 E2B
Google
Muse Spark 1.3
Meta
Core performance indexes
LLM Stats Score
6.8
#289
55.1
#4
Reasoning
5.7
#290
52.6
#7
Agents
-3.6
#177
40.6
#4
Cost, coverage & limits
Benchmark wins
0 of 1
1 of 1
Input price
— / M
$0.10 / M
Output price
— / M
$0.20 / M
Context window
—
1,048,576

Individual benchmarks

11 reported for Gemma 4 E2B · 11 for Muse Spark 1.3

1 shared

Gemma 4 E2B outperforms in 0 benchmarks, while Muse Spark 1.3 is better at 1 benchmark (MRCR v2 (8-needle)).

Muse Spark 1.3 significantly outperforms across most benchmarks.

Tue Sep 08 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Context Window

Maximum input and output token capacity

Only Muse Spark 1.3 specifies input context (1,048,576 tokens). Only Muse Spark 1.3 specifies output context (943,718 tokens).

Google
Gemma 4 E2B
Input- tokens
Output- tokens
Meta
Muse Spark 1.3
Input1,048,576 tokens
Output943,718 tokens
Tue Sep 08 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both Gemma 4 E2B and Muse Spark 1.3 support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Gemma 4 E2B

Text
Images
Audio
Video

Muse Spark 1.3

Text
Images
Audio
Video

License

Usage and distribution terms

Gemma 4 E2B is licensed under Apache 2.0, while Muse Spark 1.3 uses a proprietary license.

License differences may affect how you can use these models in commercial or open-source projects.

Gemma 4 E2B

Apache 2.0

Open weights

Muse Spark 1.3

Proprietary

Closed source

Release Timeline

When each model was launched

Gemma 4 E2B was released on 2026-04-02, while Muse Spark 1.3 was released on 2026-09-02.

Muse Spark 1.3 is 5 months newer than Gemma 4 E2B.

Gemma 4 E2B

Apr 2, 2026

5 months ago

Muse Spark 1.3

Sep 2, 2026

6 days ago

5mo newer

Knowledge Cutoff

When training data ends

Gemma 4 E2B has a documented knowledge cutoff of 2025-01-01, while Muse Spark 1.3's cutoff date is not specified.

We can confirm Gemma 4 E2B's training data extends to 2025-01-01, but cannot make a direct comparison without Muse Spark 1.3's cutoff date.

Gemma 4 E2B

Jan 2025

Muse Spark 1.3

—

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against Gemma 4 E2B and Muse Spark 1.3 side-by-side, then vote on the output you prefer.

Gemma 4 E2B
✓ Preferred
Muse Spark 1.3
Open in Playground

FAQ

Common questions about Gemma 4 E2B vs Muse Spark 1.3.

Which is better, Gemma 4 E2B or Muse Spark 1.3?

Muse Spark 1.3 leads the LLM Stats Score 55.1 to 6.8. Gemma 4 E2B is made by Google and Muse Spark 1.3 is made by Meta. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Gemma 4 E2B compare to Muse Spark 1.3 in benchmarks?

Gemma 4 E2B scores MMMLU: 67.4%, MMLU-Pro: 60.0%, MathVision: 52.4%, MMMU-Pro: 44.2%, LiveCodeBench v6: 44.0%. Muse Spark 1.3 scores MRCR v2 (8-needle): 98.5%, MRCR v2 (8-needle, 512K-1M): 98.1%, DeepSearchQA: 89.4%, Terminal-Bench 2.1: 88.8%, DeepSWE 1.1: 75.4%.

What are the context window sizes for Gemma 4 E2B and Muse Spark 1.3?

Gemma 4 E2B supports an unknown number of tokens and Muse Spark 1.3 supports 1.0M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Gemma 4 E2B and Muse Spark 1.3?

Key differences include LLM Stats Score (6.8 vs 55.1), licensing (Apache 2.0 vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes Gemma 4 E2B and Muse Spark 1.3?

Gemma 4 E2B is developed by Google and Muse Spark 1.3 is developed by Meta.

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