Gemma 4 31B vs Qwen3 Max Thinking
Gemma 4 31B and Qwen3 Max Thinking are closely matched at 33.2 and 33.0 on the LLM Stats Score. Gemma 4 31B is 15.7x cheaper per token.
Google · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Gemma 4 31B and Qwen3 Max Thinking are closely matched on the overall LLM Stats Score at 33.2 and 33.0.
In the 5 individual benchmarks reported for both models, Qwen3 Max Thinking wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, Gemma 4 31B is roughly 15.7x 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.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Gemma 4 31B
- cost matters — it's about 15.7x 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
- you need open weights you can self-host or fine-tune
Choose Qwen3 Max Thinking
- you value its reported benchmark strengths — it wins 4 of 5 exact shared results
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
12 reported for Gemma 4 31B · 34 for Qwen3 Max Thinking
Gemma 4 31B outperforms in 1 benchmarks (MMMLU), while Qwen3 Max Thinking is better at 4 benchmarks (AIME 2026, GPQA, LiveCodeBench v6, MMLU-Pro).
Qwen3 Max Thinking significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Gemma 4 31B ($0.09/1M tokens) is 13.3x cheaper than Qwen3 Max Thinking ($1.20/1M tokens).
For output processing, Gemma 4 31B ($0.34/1M tokens) is 17.6x cheaper than Qwen3 Max Thinking ($6.00/1M tokens).
In conclusion, Qwen3 Max Thinking is more expensive than Gemma 4 31B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3 Max Thinking has 969.3B more parameters than Gemma 4 31B, making it 3157.3% larger.
Context Window
Maximum input and output token capacity
Gemma 4 31B accepts 262,144 input tokens compared to Qwen3 Max Thinking's 256,000 tokens. Gemma 4 31B can generate longer responses up to 262,144 tokens, while Qwen3 Max Thinking is limited to 256,000 tokens.
Input capabilities
Documented input modalities across available providers
Gemma 4 31B supports multimodal inputs, whereas Qwen3 Max Thinking 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
Qwen3 Max Thinking
License
Usage and distribution terms
Gemma 4 31B is licensed under Apache 2.0, while Qwen3 Max Thinking uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
Apache 2.0
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
Gemma 4 31B was released on 2026-04-02, while Qwen3 Max Thinking was released on 2026-02-13.
Gemma 4 31B is 2 months newer than Qwen3 Max Thinking.
Apr 2, 2026
5 months ago
1mo newerFeb 13, 2026
6 months ago
Knowledge Cutoff
When training data ends
Gemma 4 31B has a documented knowledge cutoff of 2025-01-01, while Qwen3 Max Thinking'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 Qwen3 Max Thinking's cutoff date.
Jan 2025
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Provider Availability
Gemma 4 31B is available from DeepInfra, FriendliAI, Novita, Together. Qwen3 Max Thinking is available from DeepInfra.
Gemma 4 31B
Qwen3 Max Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against Gemma 4 31B and Qwen3 Max Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about Gemma 4 31B vs Qwen3 Max Thinking.