Model Comparison

GLM-5 vs Gemma 4 E2B

GLM-5 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

GLM-5 outperforms in 1 benchmarks (t2-bench), while Gemma 4 E2B is better at 0 benchmarks.

GLM-5 significantly outperforms across most benchmarks.

Sat Apr 18 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Sat Apr 18 2026 • llm-stats.com
Zhipu AI
GLM-5
Input tokens$1.00
Output tokens$3.20
Best providerUnknown Organization
Google
Gemma 4 E2B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

738.9B diff

GLM-5 has 738.9B more parameters than Gemma 4 E2B, making it 14488.2% larger.

Zhipu AI
GLM-5
744.0Bparameters
Google
Gemma 4 E2B
5.1Bparameters
744.0B
GLM-5
5.1B
Gemma 4 E2B

Context Window

Maximum input and output token capacity

Only GLM-5 specifies input context (200,000 tokens). Only GLM-5 specifies output context (128,000 tokens).

Zhipu AI
GLM-5
Input200,000 tokens
Output128,000 tokens
Google
Gemma 4 E2B
Input- tokens
Output- tokens
Sat Apr 18 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemma 4 E2B supports multimodal inputs, whereas GLM-5 does not.

Gemma 4 E2B can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-5

Text
Images
Audio
Video

Gemma 4 E2B

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5 is licensed under MIT, while Gemma 4 E2B uses Apache 2.0.

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

GLM-5

MIT

Open weights

Gemma 4 E2B

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-5 was released on 2026-02-11, while Gemma 4 E2B was released on 2026-04-02.

Gemma 4 E2B is 2 months newer than GLM-5.

GLM-5

Feb 11, 2026

2 months ago

Gemma 4 E2B

Apr 2, 2026

2 weeks ago

1mo newer

Knowledge Cutoff

When training data ends

Gemma 4 E2B has a documented knowledge cutoff of 2025-01-01, while GLM-5'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 GLM-5's cutoff date.

GLM-5

—

Gemma 4 E2B

Jan 2025

Outputs Comparison

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Key Takeaways

Zhipu AI
Larger context window (200,000 tokens)
Higher t2-bench score (89.7% vs 29.4%)
Google
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-5
Google
Gemma 4 E2B

FAQ

Common questions about GLM-5 vs Gemma 4 E2B

GLM-5 significantly outperforms across most benchmarks. GLM-5 is made by Zhipu AI and Gemma 4 E2B is made by Google. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
GLM-5 scores t2-bench: 89.7%, SWE-Bench Verified: 77.8%, BrowseComp: 75.9%, MCP Atlas: 67.8%, Terminal-Bench 2.0: 56.2%. Gemma 4 E2B scores MMMLU: 67.4%, MMLU-Pro: 60.0%, MathVision: 52.4%, MMMU-Pro: 44.2%, LiveCodeBench v6: 44.0%.
GLM-5 supports 200K tokens and Gemma 4 E2B supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include multimodal support (no vs yes), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
GLM-5 is developed by Zhipu AI and Gemma 4 E2B is developed by Google.