Gemma 4 E4B vs Muse Spark 1.3
Muse Spark 1.3 leads the LLM Stats Score 55.1 to 13.9. Gemma 4 E4B is 3.1x cheaper per token.
Google · Meta · Updated for 2026
Which is better?
Muse Spark 1.3 leads the overall LLM Stats Score 55.1 to 13.9, 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.
On price, Gemma 4 E4B is roughly 3.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Muse Spark 1.3 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Gemma 4 E4B
- cost matters — it's about 3.1x cheaper per token
- 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 process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Sep 2026
At a glance
The differences that matter most.
Individual benchmarks
11 reported for Gemma 4 E4B · 11 for Muse Spark 1.3
Gemma 4 E4B 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.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Gemma 4 E4B ($0.02/1M tokens) is 5.0x cheaper than Muse Spark 1.3 ($0.10/1M tokens).
For output processing, Gemma 4 E4B ($0.10/1M tokens) is 2.0x cheaper than Muse Spark 1.3 ($0.20/1M tokens).
In conclusion, Muse Spark 1.3 is more expensive than Gemma 4 E4B.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Muse Spark 1.3 accepts 1,048,576 input tokens compared to Gemma 4 E4B's 131,072 tokens. Muse Spark 1.3 can generate longer responses up to 943,718 tokens, while Gemma 4 E4B is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Both Gemma 4 E4B and Muse Spark 1.3 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Gemma 4 E4B
Muse Spark 1.3
License
Usage and distribution terms
Gemma 4 E4B 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.
Apache 2.0
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
Gemma 4 E4B 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 E4B.
Apr 2, 2026
5 months ago
Sep 2, 2026
6 days ago
5mo newerKnowledge Cutoff
When training data ends
Gemma 4 E4B 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 E4B's training data extends to 2025-01-01, but cannot make a direct comparison without Muse Spark 1.3's cutoff date.
Jan 2025
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Provider Availability
Gemma 4 E4B is available from DeepInfra. Muse Spark 1.3 is available from Meta Model API.
Gemma 4 E4B
Muse Spark 1.3
Outputs Comparison
Judge for yourself.
Run your own prompts against Gemma 4 E4B and Muse Spark 1.3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Gemma 4 E4B vs Muse Spark 1.3.