Model Comparison
Gemma 4 31B vs GLM-5.2Which is better in 2026?
GLM-5.2 significantly outperforms across most benchmarks. Gemma 4 31B is 7.6x cheaper per token.
Verdict: Gemma 4 31B vs GLM-5.2 — which is better?
Gemma 4 31B (by Google) and GLM-5.2 (by Zhipu AI) 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.
Gemma 4 31B outperforms in 0 benchmarks, while GLM-5.2 is better at 3 benchmarks (AIME 2026, GPQA, Humanity's Last Exam). GLM-5.2 significantly outperforms across most benchmarks.
On price, Gemma 4 31B is roughly 7.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-5.2 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Choose Gemma 4 31B if…
- cost matters — it's about 7.6x cheaper per token
Choose GLM-5.2 if…
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Jun 2026
Performance Benchmarks
Comparative analysis across standard metrics
Gemma 4 31B outperforms in 0 benchmarks, while GLM-5.2 is better at 3 benchmarks (AIME 2026, GPQA, Humanity's Last Exam).
GLM-5.2 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, Gemma 4 31B ($0.13/1M tokens) is 7.3x cheaper than GLM-5.2 ($0.95/1M tokens).
For output processing, Gemma 4 31B ($0.38/1M tokens) is 7.9x cheaper than GLM-5.2 ($3.00/1M tokens).
In conclusion, GLM-5.2 is more expensive than Gemma 4 31B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5.2 has 722.3B more parameters than Gemma 4 31B, making it 2352.8% larger.
Context Window
Maximum input and output token capacity
GLM-5.2 accepts 1,048,576 input tokens compared to Gemma 4 31B's 262,144 tokens. Both models can generate responses up to 131,072 tokens.
Input Capabilities
Supported data types and modalities
Gemma 4 31B supports multimodal inputs, whereas GLM-5.2 does not.
Gemma 4 31B can handle both text and other forms of data like images, making it suitable for multimodal applications.
Gemma 4 31B
GLM-5.2
License
Usage and distribution terms
Gemma 4 31B is licensed under Apache 2.0, while GLM-5.2 uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Apache 2.0
Open weights
MIT
Open weights
Release Timeline
When each model was launched
Gemma 4 31B was released on 2026-04-02, while GLM-5.2 was released on 2026-06-16.
GLM-5.2 is 3 months newer than Gemma 4 31B.
Apr 2, 2026
3 months ago
Jun 16, 2026
1 months ago
2mo newerKnowledge Cutoff
When training data ends
Gemma 4 31B has a documented knowledge cutoff of 2025-01-01, while GLM-5.2'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-5.2's cutoff date.
Jan 2025
—
Provider Availability
Gemma 4 31B is available from DeepInfra, FriendliAI, Novita, Together. GLM-5.2 is available from DeepInfra, Fireworks, FriendliAI, Novita, Together, ZAI.
Gemma 4 31B
GLM-5.2
Outputs Comparison
Key Takeaways
Gemma 4 31B
View detailsGLM-5.2
View detailsZhipu AI
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against Gemma 4 31B and GLM-5.2 side-by-side, then vote on the output you prefer.
| Feature | | |
|---|
FAQ
Common questions about Gemma 4 31B vs GLM-5.2.