Model Comparison
Gemma 3 4B vs MiniMax M1 80KWhich is better in 2026?
MiniMax M1 80K significantly outperforms across most benchmarks. Gemma 3 4B is 38.5x cheaper per token.
Verdict: Gemma 3 4B vs MiniMax M1 80K — which is better?
Gemma 3 4B (by Google) and MiniMax M1 80K (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 4B outperforms in 0 benchmarks, while MiniMax M1 80K is better at 4 benchmarks (GPQA, LiveCodeBench, MMLU-Pro, SimpleQA). MiniMax M1 80K significantly outperforms across most benchmarks.
On price, Gemma 3 4B is roughly 38.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiniMax M1 80K 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 4B if…
- cost matters — it's about 38.5x cheaper per token
Choose MiniMax M1 80K if…
- you want the strongest raw capability — it leads on 4 of 4 shared benchmarks
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Jun 2025
Performance Benchmarks
Comparative analysis across standard metrics
Gemma 3 4B outperforms in 0 benchmarks, while MiniMax M1 80K is better at 4 benchmarks (GPQA, LiveCodeBench, MMLU-Pro, SimpleQA).
MiniMax M1 80K significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, Gemma 3 4B ($0.02/1M tokens) is 27.5x cheaper than MiniMax M1 80K ($0.55/1M tokens).
For output processing, Gemma 3 4B ($0.04/1M tokens) is 55.0x cheaper than MiniMax M1 80K ($2.20/1M tokens).
In conclusion, MiniMax M1 80K is more expensive than Gemma 3 4B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiniMax M1 80K has 452.0B more parameters than Gemma 3 4B, making it 11300.0% larger.
Context Window
Maximum input and output token capacity
MiniMax M1 80K accepts 1,000,000 input tokens compared to Gemma 3 4B's 131,072 tokens. Gemma 3 4B can generate longer responses up to 131,072 tokens, while MiniMax M1 80K is limited to 40,000 tokens.
Input Capabilities
Supported data types and modalities
Gemma 3 4B supports multimodal inputs, whereas MiniMax M1 80K does not.
Gemma 3 4B can handle both text and other forms of data like images, making it suitable for multimodal applications.
Gemma 3 4B
MiniMax M1 80K
License
Usage and distribution terms
Gemma 3 4B is licensed under Gemma, while MiniMax M1 80K 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 4B was released on 2025-03-12, while MiniMax M1 80K was released on 2025-06-16.
MiniMax M1 80K is 3 months newer than Gemma 3 4B.
Mar 12, 2025
1.2 years ago
Jun 16, 2025
11 months ago
3mo newerKnowledge Cutoff
When training data ends
Gemma 3 4B has a documented knowledge cutoff of 2024-08-01, while MiniMax M1 80K's cutoff date is not specified.
We can confirm Gemma 3 4B's training data extends to 2024-08-01, but cannot make a direct comparison without MiniMax M1 80K's cutoff date.
Aug 2024
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Provider Availability
Gemma 3 4B is available from DeepInfra. MiniMax M1 80K is available from Novita.
Gemma 3 4B
MiniMax M1 80K
Outputs Comparison
Key Takeaways
Gemma 3 4B
View details
MiniMax M1 80K
View detailsMiniMax
Detailed Comparison
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FAQ
Common questions about Gemma 3 4B vs MiniMax M1 80K.