Model Comparison

GLM-5 vs Gemma 4 26B-A4B

GLM-5 significantly outperforms across most benchmarks. Gemma 4 26B-A4B is 7.8x cheaper per token.

Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

GLM-5 outperforms in 1 benchmarks (t2-bench), while Gemma 4 26B-A4B 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

Gemma 4 26B-A4B costs less

For input processing, GLM-5 ($1.00/1M tokens) is 7.7x more expensive than Gemma 4 26B-A4B ($0.13/1M tokens).

For output processing, GLM-5 ($3.20/1M tokens) is 8.0x more expensive than Gemma 4 26B-A4B ($0.40/1M tokens).

In conclusion, GLM-5 is more expensive than Gemma 4 26B-A4B.*

* Using a 3:1 ratio of input to output tokens

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 26B-A4B
Input tokens$0.13
Output tokens$0.40
Best providerNovita
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

718.8B diff

GLM-5 has 718.8B more parameters than Gemma 4 26B-A4B, making it 2852.4% larger.

Zhipu AI
GLM-5
744.0Bparameters
Google
Gemma 4 26B-A4B
25.2Bparameters
744.0B
GLM-5
25.2B
Gemma 4 26B-A4B

Context Window

Maximum input and output token capacity

Gemma 4 26B-A4B accepts 262,144 input tokens compared to GLM-5's 200,000 tokens. Gemma 4 26B-A4B can generate longer responses up to 131,072 tokens, while GLM-5 is limited to 128,000 tokens.

Zhipu AI
GLM-5
Input200,000 tokens
Output128,000 tokens
Google
Gemma 4 26B-A4B
Input262,144 tokens
Output131,072 tokens
Sat Apr 18 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemma 4 26B-A4B supports multimodal inputs, whereas GLM-5 does not.

Gemma 4 26B-A4B 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 26B-A4B

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5 is licensed under MIT, while Gemma 4 26B-A4B 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 26B-A4B

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-5 was released on 2026-02-11, while Gemma 4 26B-A4B was released on 2026-04-02.

Gemma 4 26B-A4B is 2 months newer than GLM-5.

GLM-5

Feb 11, 2026

2 months ago

Gemma 4 26B-A4B

Apr 2, 2026

2 weeks ago

1mo newer

Knowledge Cutoff

When training data ends

Gemma 4 26B-A4B has a documented knowledge cutoff of 2025-01-01, while GLM-5's cutoff date is not specified.

We can confirm Gemma 4 26B-A4B's training data extends to 2025-01-01, but cannot make a direct comparison without GLM-5's cutoff date.

GLM-5

—

Gemma 4 26B-A4B

Jan 2025

Provider Availability

GLM-5 is available from ZAI. Gemma 4 26B-A4B is available from Novita.

GLM-5

z logo
Unknown Organization
Input Price:Input: $1.00/1MOutput Price:Output: $3.20/1M

Gemma 4 26B-A4B

novita logo
Novita
Input Price:Input: $0.13/1MOutput Price:Output: $0.40/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Zhipu AI
Higher t2-bench score (89.7% vs 85.5%)
Google
Larger context window (262,144 tokens)
Supports multimodal inputs
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-5
Google
Gemma 4 26B-A4B

FAQ

Common questions about GLM-5 vs Gemma 4 26B-A4B

GLM-5 significantly outperforms across most benchmarks. GLM-5 is made by Zhipu AI and Gemma 4 26B-A4B 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 26B-A4B scores AIME 2026: 88.3%, MMMLU: 86.3%, t2-bench: 85.5%, MMLU-Pro: 82.6%, MathVision: 82.4%.
Gemma 4 26B-A4B is 7.7x cheaper for input tokens. GLM-5 costs $1.00/M input and $3.20/M output via z. Gemma 4 26B-A4B costs $0.13/M input and $0.40/M output via novita.
GLM-5 supports 200K tokens and Gemma 4 26B-A4B supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (200K vs 262K), input pricing ($1.00 vs $0.13/M), 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 26B-A4B is developed by Google.