Model Comparison
MiniMax M2 vs Qwen3 VL 235B A22B ThinkingWhich is better in 2026?
Qwen3 VL 235B A22B Thinking significantly outperforms across most benchmarks. MiniMax M2 is 2.3x cheaper per token.
Verdict: MiniMax M2 vs Qwen3 VL 235B A22B Thinking — which is better?
MiniMax M2 (by MiniMax) and Qwen3 VL 235B A22B Thinking (by Alibaba Cloud / Qwen Team) 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.
MiniMax M2 outperforms in 0 benchmarks, while Qwen3 VL 235B A22B Thinking is better at 3 benchmarks (AIME 2025, Humanity's Last Exam, MMLU-Pro). Qwen3 VL 235B A22B Thinking significantly outperforms across most benchmarks.
On price, MiniMax M2 is roughly 2.3x 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 MiniMax M2 if…
- cost matters — it's about 2.3x cheaper per token
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Oct 2025
Choose Qwen3 VL 235B A22B Thinking if…
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
Performance Benchmarks
Comparative analysis across standard metrics
MiniMax M2 outperforms in 0 benchmarks, while Qwen3 VL 235B A22B Thinking is better at 3 benchmarks (AIME 2025, Humanity's Last Exam, MMLU-Pro).
Qwen3 VL 235B A22B Thinking significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, MiniMax M2 ($0.30/1M tokens) is 1.5x cheaper than Qwen3 VL 235B A22B Thinking ($0.45/1M tokens).
For output processing, MiniMax M2 ($1.20/1M tokens) is 2.9x cheaper than Qwen3 VL 235B A22B Thinking ($3.49/1M tokens).
In conclusion, Qwen3 VL 235B A22B Thinking is more expensive than MiniMax M2.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3 VL 235B A22B Thinking has 6.0B more parameters than MiniMax M2, making it 2.6% larger.
Context Window
Maximum input and output token capacity
MiniMax M2 accepts 1,000,000 input tokens compared to Qwen3 VL 235B A22B Thinking's 262,144 tokens. MiniMax M2 can generate longer responses up to 1,000,000 tokens, while Qwen3 VL 235B A22B Thinking is limited to 262,144 tokens.
Input Capabilities
Supported data types and modalities
Qwen3 VL 235B A22B Thinking supports multimodal inputs, whereas MiniMax M2 does not.
Qwen3 VL 235B A22B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.
MiniMax M2
Qwen3 VL 235B A22B Thinking
License
Usage and distribution terms
MiniMax M2 is licensed under MIT, while Qwen3 VL 235B A22B Thinking uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
MiniMax M2 was released on 2025-10-27, while Qwen3 VL 235B A22B Thinking was released on 2025-09-22.
MiniMax M2 is 1 month newer than Qwen3 VL 235B A22B Thinking.
Oct 27, 2025
7 months ago
1mo newerSep 22, 2025
8 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
MiniMax M2 is available from MiniMax, Novita. Qwen3 VL 235B A22B Thinking is available from DeepInfra, Novita.
MiniMax M2
Qwen3 VL 235B A22B Thinking
Outputs Comparison
Key Takeaways
MiniMax M2
View detailsMiniMax
Qwen3 VL 235B A22B Thinking
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about MiniMax M2 vs Qwen3 VL 235B A22B Thinking.