Model Comparison
GLM-5 vs Gemma 4 31BWhich is better in 2026?
GLM-5 significantly outperforms across most benchmarks. Gemma 4 31B is 8.1x cheaper per token.
Verdict: GLM-5 vs Gemma 4 31B — which is better?
GLM-5 (by Zhipu AI) and Gemma 4 31B (by Google) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
GLM-5 outperforms in 1 benchmarks (t2-bench), while Gemma 4 31B is better at 0 benchmarks. GLM-5 significantly outperforms across most benchmarks.
On price, Gemma 4 31B is roughly 8.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Gemma 4 31B also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Choose GLM-5 if…
- you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
Choose Gemma 4 31B if…
- cost matters — it's about 8.1x cheaper per token
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Apr 2026
Performance Benchmarks
Comparative analysis across standard metrics
GLM-5 outperforms in 1 benchmarks (t2-bench), while Gemma 4 31B is better at 0 benchmarks.
GLM-5 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-5 ($1.00/1M tokens) is 7.7x more expensive than Gemma 4 31B ($0.13/1M tokens).
For output processing, GLM-5 ($3.20/1M tokens) is 8.4x more expensive than Gemma 4 31B ($0.38/1M tokens).
In conclusion, GLM-5 is more expensive than Gemma 4 31B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5 has 713.3B more parameters than Gemma 4 31B, making it 2323.5% larger.
Context Window
Maximum input and output token capacity
Gemma 4 31B accepts 262,144 input tokens compared to GLM-5's 200,000 tokens. Gemma 4 31B can generate longer responses up to 131,072 tokens, while GLM-5 is limited to 128,000 tokens.
Input Capabilities
Supported data types and modalities
Gemma 4 31B supports multimodal inputs, whereas GLM-5 does not.
Gemma 4 31B can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5
Gemma 4 31B
License
Usage and distribution terms
GLM-5 is licensed under MIT, while Gemma 4 31B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
GLM-5 was released on 2026-02-11, while Gemma 4 31B was released on 2026-04-02.
Gemma 4 31B is 2 months newer than GLM-5.
Feb 11, 2026
4 months ago
Apr 2, 2026
3 months ago
1mo newerKnowledge Cutoff
When training data ends
Gemma 4 31B has a documented knowledge cutoff of 2025-01-01, while GLM-5's cutoff date is not specified.
We can confirm Gemma 4 31B's training data extends to 2025-01-01, but cannot make a direct comparison without GLM-5's cutoff date.
—
Jan 2025
Provider Availability
GLM-5 is available from FriendliAI, ZAI. Gemma 4 31B is available from DeepInfra, FriendliAI, Novita, Together.
GLM-5
Gemma 4 31B
Outputs Comparison
Key Takeaways
GLM-5
View detailsZhipu AI
Gemma 4 31B
View detailsDetailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against GLM-5 and Gemma 4 31B side-by-side, then vote on the output you prefer.
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FAQ
Common questions about GLM-5 vs Gemma 4 31B.