Model Comparison
Gemma 3 27B vs MiniMax M2Which is better in 2026?
MiniMax M2 significantly outperforms across most benchmarks. Gemma 3 27B is 4.2x cheaper per token.
Verdict: Gemma 3 27B vs MiniMax M2 — which is better?
Gemma 3 27B (by Google) and MiniMax M2 (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 3 27B outperforms in 0 benchmarks, while MiniMax M2 is better at 3 benchmarks (GPQA, LiveCodeBench, MMLU-Pro). MiniMax M2 significantly outperforms across most benchmarks.
On price, Gemma 3 27B is roughly 4.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiniMax M2 also accepts a larger context window (1,000,000 input tokens), making it the stronger choice for long documents and large codebases.
Choose Gemma 3 27B if…
- cost matters — it's about 4.2x cheaper per token
Choose MiniMax M2 if…
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Oct 2025
Performance Benchmarks
Comparative analysis across standard metrics
Gemma 3 27B outperforms in 0 benchmarks, while MiniMax M2 is better at 3 benchmarks (GPQA, LiveCodeBench, MMLU-Pro).
MiniMax M2 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, Gemma 3 27B ($0.10/1M tokens) is 3.0x cheaper than MiniMax M2 ($0.30/1M tokens).
For output processing, Gemma 3 27B ($0.20/1M tokens) is 6.0x cheaper than MiniMax M2 ($1.20/1M tokens).
In conclusion, MiniMax M2 is more expensive than Gemma 3 27B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiniMax M2 has 203.0B more parameters than Gemma 3 27B, making it 751.9% larger.
Context Window
Maximum input and output token capacity
MiniMax M2 accepts 1,000,000 input tokens compared to Gemma 3 27B's 131,072 tokens. MiniMax M2 can generate longer responses up to 1,000,000 tokens, while Gemma 3 27B is limited to 131,072 tokens.
Input Capabilities
Supported data types and modalities
Gemma 3 27B supports multimodal inputs, whereas MiniMax M2 does not.
Gemma 3 27B can handle both text and other forms of data like images, making it suitable for multimodal applications.
Gemma 3 27B
MiniMax M2
License
Usage and distribution terms
Gemma 3 27B is licensed under Gemma, while MiniMax M2 uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Gemma
Open weights
MIT
Open weights
Release Timeline
When each model was launched
Gemma 3 27B was released on 2025-03-12, while MiniMax M2 was released on 2025-10-27.
MiniMax M2 is 8 months newer than Gemma 3 27B.
Mar 12, 2025
1.4 years ago
Oct 27, 2025
9 months ago
7mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Gemma 3 27B is available from DeepInfra, Novita. MiniMax M2 is available from MiniMax, Novita.
Gemma 3 27B
MiniMax M2
Outputs Comparison
Key Takeaways
Gemma 3 27B
View details
MiniMax M2
View detailsMiniMax
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against Gemma 3 27B and MiniMax M2 side-by-side, then vote on the output you prefer.
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FAQ
Common questions about Gemma 3 27B vs MiniMax M2.