Model Comparison
Llama 4 Scout vs MiniMax M2Which is better in 2026?
MiniMax M2 significantly outperforms across most benchmarks. Llama 4 Scout is 3.9x cheaper per token.
Verdict: Llama 4 Scout vs MiniMax M2 — which is better?
Llama 4 Scout (by Meta) and MiniMax M2 (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.
Llama 4 Scout outperforms in 0 benchmarks, while MiniMax M2 is better at 3 benchmarks (GPQA, LiveCodeBench, MMLU-Pro). MiniMax M2 significantly outperforms across most benchmarks.
On price, Llama 4 Scout is roughly 3.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Llama 4 Scout also accepts a larger context window (10,000,000 input tokens), making it the stronger choice for long documents and large codebases.
Choose Llama 4 Scout if…
- cost matters — it's about 3.9x cheaper per token
- you process long inputs — it offers a 10,000,000 token context window
Choose MiniMax M2 if…
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
- you want the most recent training data — it shipped Oct 2025
Performance Benchmarks
Comparative analysis across standard metrics
Llama 4 Scout outperforms in 0 benchmarks, while MiniMax M2 is better at 3 benchmarks (GPQA, LiveCodeBench, MMLU-Pro).
MiniMax M2 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, Llama 4 Scout ($0.08/1M tokens) is 3.8x cheaper than MiniMax M2 ($0.30/1M tokens).
For output processing, Llama 4 Scout ($0.30/1M tokens) is 4.0x cheaper than MiniMax M2 ($1.20/1M tokens).
In conclusion, MiniMax M2 is more expensive than Llama 4 Scout.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiniMax M2 has 121.0B more parameters than Llama 4 Scout, making it 111.0% larger.
Context Window
Maximum input and output token capacity
Llama 4 Scout accepts 10,000,000 input tokens compared to MiniMax M2's 1,000,000 tokens. Llama 4 Scout can generate longer responses up to 10,000,000 tokens, while MiniMax M2 is limited to 1,000,000 tokens.
Input Capabilities
Supported data types and modalities
Llama 4 Scout supports multimodal inputs, whereas MiniMax M2 does not.
Llama 4 Scout can handle both text and other forms of data like images, making it suitable for multimodal applications.
Llama 4 Scout
MiniMax M2
License
Usage and distribution terms
Llama 4 Scout is licensed under Llama 4 Community License Agreement, while MiniMax M2 uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Llama 4 Community License Agreement
Open weights
MIT
Open weights
Release Timeline
When each model was launched
Llama 4 Scout was released on 2025-04-05, while MiniMax M2 was released on 2025-10-27.
MiniMax M2 is 7 months newer than Llama 4 Scout.
Apr 5, 2025
1.3 years ago
Oct 27, 2025
9 months ago
6mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Llama 4 Scout is available from DeepInfra, Lambda, Novita, Groq, Fireworks, Together. MiniMax M2 is available from MiniMax, Novita.
Llama 4 Scout
MiniMax M2
Outputs Comparison
Key Takeaways
MiniMax M2
View detailsMiniMax
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against Llama 4 Scout and MiniMax M2 side-by-side, then vote on the output you prefer.
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FAQ
Common questions about Llama 4 Scout vs MiniMax M2.