Model Comparison
Gemma 4 31B vs MiniMax M3Which is better in 2026?
MiniMax M3 significantly outperforms across most benchmarks. Gemma 4 31B is 2.7x cheaper per token.
Verdict: Gemma 4 31B vs MiniMax M3 — which is better?
Gemma 4 31B (by Google) and MiniMax M3 (by MiniMax) 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 MiniMax M3 is better at 2 benchmarks (GDPval-AA, MMMU-Pro). MiniMax M3 significantly outperforms across most benchmarks.
On price, Gemma 4 31B is roughly 2.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiniMax M3 also accepts a larger context window (512,000 input tokens), making it the stronger choice for long documents and large codebases.
Choose Gemma 4 31B if…
- cost matters — it's about 2.7x cheaper per token
Choose MiniMax M3 if…
- you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
- you process long inputs — it offers a 512,000 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 MiniMax M3 is better at 2 benchmarks (GDPval-AA, MMMU-Pro).
MiniMax M3 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 2.3x cheaper than MiniMax M3 ($0.30/1M tokens).
For output processing, Gemma 4 31B ($0.38/1M tokens) is 3.2x cheaper than MiniMax M3 ($1.20/1M tokens).
In conclusion, MiniMax M3 is more expensive than Gemma 4 31B.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
MiniMax M3 accepts 512,000 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
Both Gemma 4 31B and MiniMax M3 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Gemma 4 31B
MiniMax M3
License
Usage and distribution terms
Gemma 4 31B is licensed under Apache 2.0, while MiniMax M3 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 MiniMax M3 was released on 2026-06-01.
MiniMax M3 is 2 months newer than Gemma 4 31B.
Apr 2, 2026
3 months ago
Jun 1, 2026
1 months ago
2mo newerKnowledge Cutoff
When training data ends
Gemma 4 31B has a documented knowledge cutoff of 2025-01-01, while MiniMax M3'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 MiniMax M3's cutoff date.
Jan 2025
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Provider Availability
Gemma 4 31B is available from DeepInfra, FriendliAI, Novita, Together. MiniMax M3 is available from Fireworks, Novita, Together, MiniMax.
Gemma 4 31B
MiniMax M3
Outputs Comparison
Key Takeaways
Gemma 4 31B
View details
MiniMax M3
View detailsMiniMax
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against Gemma 4 31B and MiniMax M3 side-by-side, then vote on the output you prefer.
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FAQ
Common questions about Gemma 4 31B vs MiniMax M3.