MiniMax M1 80K vs Qwen3 VL 4B Thinking
MiniMax M1 80K significantly outperforms across most benchmarks. Qwen3 VL 4B Thinking is 3.0x cheaper per token.
MiniMax · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
MiniMax M1 80K outperforms in 3 benchmarks (AIME 2025, GPQA, MMLU-Pro), while Qwen3 VL 4B Thinking is better at 0 benchmarks. MiniMax M1 80K significantly outperforms across most benchmarks.
On price, Qwen3 VL 4B Thinking is roughly 3.0x 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.
Based on current benchmark, pricing, and model metadata for 2026.
Choose MiniMax M1 80K
- 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
Choose Qwen3 VL 4B Thinking
- cost matters — it's about 3.0x cheaper per token
- you want the most recent training data — it shipped Sep 2025
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
MiniMax M1 80K outperforms in 3 benchmarks (AIME 2025, GPQA, MMLU-Pro), while Qwen3 VL 4B Thinking is better at 0 benchmarks.
MiniMax M1 80K significantly outperforms across most benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, MiniMax M1 80K ($0.55/1M tokens) is 5.5x more expensive than Qwen3 VL 4B Thinking ($0.10/1M tokens).
For output processing, MiniMax M1 80K ($2.20/1M tokens) is 2.2x more expensive than Qwen3 VL 4B Thinking ($1.00/1M tokens).
In conclusion, MiniMax M1 80K is more expensive than Qwen3 VL 4B Thinking.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiniMax M1 80K has 452.0B more parameters than Qwen3 VL 4B Thinking, making it 11300.0% larger.
Context Window
Maximum input and output token capacity
MiniMax M1 80K accepts 1,000,000 input tokens compared to Qwen3 VL 4B Thinking's 262,144 tokens. Qwen3 VL 4B Thinking can generate longer responses up to 262,144 tokens, while MiniMax M1 80K is limited to 40,000 tokens.
Input Capabilities
Supported data types and modalities
Qwen3 VL 4B Thinking supports multimodal inputs, whereas MiniMax M1 80K does not.
Qwen3 VL 4B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.
MiniMax M1 80K
Qwen3 VL 4B Thinking
License
Usage and distribution terms
MiniMax M1 80K is licensed under MIT, while Qwen3 VL 4B 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 M1 80K was released on 2025-06-16, while Qwen3 VL 4B Thinking was released on 2025-09-22.
Qwen3 VL 4B Thinking is 3 months newer than MiniMax M1 80K.
Jun 16, 2025
1.2 years ago
Sep 22, 2025
11 months ago
3mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
MiniMax M1 80K is available from Novita. Qwen3 VL 4B Thinking is available from DeepInfra.
MiniMax M1 80K
Qwen3 VL 4B Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against MiniMax M1 80K and Qwen3 VL 4B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about MiniMax M1 80K vs Qwen3 VL 4B Thinking.