MiniMax M1 80K vs Qwen3 VL 8B Thinking
MiniMax M1 80K leads the LLM Stats Score 21.4 to 16.2. Qwen3 VL 8B Thinking is 1.5x cheaper per token.
MiniMax · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
MiniMax M1 80K leads the overall LLM Stats Score 21.4 to 16.2, ranking #204 overall.
The models split the 4 individual benchmarks reported for both models evenly.
On price, Qwen3 VL 8B Thinking is roughly 1.5x 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.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose MiniMax M1 80K
- overall performance matters — it scores 21.4 and ranks #204 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you process long inputs — it offers a 1,000,000 token context window
Choose Qwen3 VL 8B Thinking
- cost matters — it's about 1.5x cheaper per token
- you want the most recent training data — it shipped Sep 2025
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
16 reported for MiniMax M1 80K · 50 for Qwen3 VL 8B Thinking
MiniMax M1 80K outperforms in 2 benchmarks (GPQA, MMLU-Pro), while Qwen3 VL 8B Thinking is better at 2 benchmarks (AIME 2025, SimpleQA).
Both models are evenly matched across the benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, MiniMax M1 80K ($0.55/1M tokens) is 3.1x more expensive than Qwen3 VL 8B Thinking ($0.18/1M tokens).
For output processing, MiniMax M1 80K ($2.20/1M tokens) is 1.1x more expensive than Qwen3 VL 8B Thinking ($2.09/1M tokens).
In conclusion, MiniMax M1 80K is more expensive than Qwen3 VL 8B Thinking.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiniMax M1 80K has 447.0B more parameters than Qwen3 VL 8B Thinking, making it 4966.7% larger.
Context Window
Maximum input and output token capacity
MiniMax M1 80K accepts 1,000,000 input tokens compared to Qwen3 VL 8B Thinking's 262,144 tokens. Qwen3 VL 8B Thinking can generate longer responses up to 262,144 tokens, while MiniMax M1 80K is limited to 40,000 tokens.
Input capabilities
Documented input modalities across available providers
Qwen3 VL 8B Thinking supports multimodal inputs, whereas MiniMax M1 80K does not.
Qwen3 VL 8B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.
MiniMax M1 80K
Qwen3 VL 8B Thinking
License
Usage and distribution terms
MiniMax M1 80K is licensed under MIT, while Qwen3 VL 8B 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 8B Thinking was released on 2025-09-22.
Qwen3 VL 8B Thinking is 3 months newer than MiniMax M1 80K.
Jun 16, 2025
1.3 years ago
Sep 22, 2025
1.0 years 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 8B Thinking is available from DeepInfra.
MiniMax M1 80K
Qwen3 VL 8B Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against MiniMax M1 80K and Qwen3 VL 8B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about MiniMax M1 80K vs Qwen3 VL 8B Thinking.