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Gemma 4 E4B vs GLM-5.3

GLM-5.3 leads the LLM Stats Score 54.2 to 14.4.

Google · Zhipu AI · Updated for 2026

Which is better?

GLM-5.3 leads the overall LLM Stats Score 54.2 to 14.4, ranking #6 overall.

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

Choose Gemma 4 E4B

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

Choose GLM-5.3

  • overall performance matters — it scores 54.2 and ranks #6 on LLM Stats
  • your work emphasizes reasoning and agents — it leads those capability indexes
  • you want the most recent training data — it shipped Aug 2026

At a glance

The differences that matter most.

Core performance indexes
14.4
#218
54.2
#6
14.3
#213
53.4
#5
2.3
#147
41.2
#5
Cost, coverage & limits
Benchmark wins
Input price
— / M
$1.40 / M
Output price
— / M
$4.40 / M
Context window
1,048,576

Individual benchmarks

11 reported for Gemma 4 E4B · 16 for GLM-5.3

No common benchmarks found

Gemma 4 E4B and GLM-5.3don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

745.0B diff

GLM-5.3 has 745.0B more parameters than Gemma 4 E4B, making it 9312.5% larger.

Google
Gemma 4 E4B
8.0Bparameters
Zhipu AI
GLM-5.3
753.0Bparameters
8.0B
Gemma 4 E4B
753.0B
GLM-5.3

Context Window

Maximum input and output token capacity

Only GLM-5.3 specifies input context (1,048,576 tokens). Only GLM-5.3 specifies output context (131,072 tokens).

Google
Gemma 4 E4B
Input- tokens
Output- tokens
Zhipu AI
GLM-5.3
Input1,048,576 tokens
Output131,072 tokens
Fri Aug 28 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Gemma 4 E4B supports multimodal inputs, whereas GLM-5.3 does not.

Gemma 4 E4B can handle both text and other forms of data like images, making it suitable for multimodal applications.

Gemma 4 E4B

Text
Images
Audio
Video

GLM-5.3

Text
Images
Audio
Video

Release Timeline

When each model was launched

Gemma 4 E4B was released on 2026-04-02, while GLM-5.3 was released on 2026-08-14.

GLM-5.3 is 4 months newer than Gemma 4 E4B.

Gemma 4 E4B

Apr 2, 2026

4 months ago

GLM-5.3

Aug 14, 2026

2 weeks ago

4mo newer

Knowledge Cutoff

When training data ends

Gemma 4 E4B has a documented knowledge cutoff of 2025-01-01, while GLM-5.3'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 GLM-5.3's cutoff date.

Gemma 4 E4B

Jan 2025

GLM-5.3

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against Gemma 4 E4B and GLM-5.3 side-by-side, then vote on the output you prefer.

Gemma 4 E4B
✓ Preferred
GLM-5.3
Open in Playground

FAQ

Common questions about Gemma 4 E4B vs GLM-5.3.

Which is better, Gemma 4 E4B or GLM-5.3?

GLM-5.3 leads the LLM Stats Score 54.2 to 14.4. Gemma 4 E4B is made by Google and GLM-5.3 is made by Zhipu AI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Gemma 4 E4B compare to GLM-5.3 in benchmarks?

Gemma 4 E4B scores MMMLU: 76.6%, MMLU-Pro: 69.4%, MathVision: 59.5%, GPQA: 58.6%, t2-bench: 57.5%. GLM-5.3 scores Terminal-Bench 2.1: 88.2%, CyberGym: 84.5%, FrontierSWE: 78.1%, Toolathlon: 73.0%, DeepSWE 1.1: 66.9%.

What are the context window sizes for Gemma 4 E4B and GLM-5.3?

Gemma 4 E4B supports an unknown number of tokens and GLM-5.3 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 E4B and GLM-5.3?

Key differences include LLM Stats Score (14.4 vs 54.2), multimodal support (yes vs no), licensing (Apache 2.0 vs Unknown). See the full comparison above for benchmark-by-benchmark results.

Who makes Gemma 4 E4B and GLM-5.3?

Gemma 4 E4B is developed by Google and GLM-5.3 is developed by Zhipu AI.