DeepSeek R1 Zero vs Llama 4 Scout
DeepSeek R1 Zero leads the LLM Stats Score 16.1 to 7.9.
DeepSeek · Meta · Updated for 2026
Which is better?
DeepSeek R1 Zero leads the overall LLM Stats Score 16.1 to 7.9, ranking #224 overall.
In the 2 individual benchmarks reported for both models, DeepSeek R1 Zero wins 2; this is a narrower head-to-head signal than the composite indexes.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek R1 Zero
- overall performance matters — it scores 16.1 and ranks #224 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
Choose Llama 4 Scout
- you want the most recent training data — it shipped Apr 2025
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
4 reported for DeepSeek R1 Zero · 12 for Llama 4 Scout
DeepSeek R1 Zero outperforms in 2 benchmarks (GPQA, LiveCodeBench), while Llama 4 Scout is better at 0 benchmarks.
DeepSeek R1 Zero significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
DeepSeek R1 Zero has 562.0B more parameters than Llama 4 Scout, making it 515.6% larger.
Context Window
Maximum input and output token capacity
Only Llama 4 Scout specifies input context (10,000,000 tokens). Only Llama 4 Scout specifies output context (10,000,000 tokens).
Input capabilities
Documented input modalities across available providers
Llama 4 Scout supports multimodal inputs, whereas DeepSeek R1 Zero does not.
Llama 4 Scout can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek R1 Zero
Llama 4 Scout
License
Usage and distribution terms
DeepSeek R1 Zero is licensed under MIT, while Llama 4 Scout uses Llama 4 Community License Agreement.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Llama 4 Community License Agreement
Open weights
Release Timeline
When each model was launched
DeepSeek R1 Zero was released on 2025-01-20, while Llama 4 Scout was released on 2025-04-05.
Llama 4 Scout is 3 months newer than DeepSeek R1 Zero.
Jan 20, 2025
1.6 years ago
Apr 5, 2025
1.4 years ago
2mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek R1 Zero and Llama 4 Scout side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek R1 Zero vs Llama 4 Scout.