Model Comparison
DeepSeek-R1 vs Llama 4 ScoutWhich is better in 2026?
Comparing DeepSeek-R1 and Llama 4 Scout across benchmarks, pricing, and capabilities.
Verdict: DeepSeek-R1 vs Llama 4 Scout — which is better?
DeepSeek-R1 (by DeepSeek) and Llama 4 Scout (by Meta) 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.
On price, Llama 4 Scout is roughly 7.1x 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 DeepSeek-R1 if…
- you want predictable pricing at $0.55/M input and $2.19/M output
Choose Llama 4 Scout if…
- cost matters — it's about 7.1x cheaper per token
- you process long inputs — it offers a 10,000,000 token context window
- you want the most recent training data — it shipped Apr 2025
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-R1 and Llama 4 Scoutdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-R1 ($0.55/1M tokens) is 6.9x more expensive than Llama 4 Scout ($0.08/1M tokens).
For output processing, DeepSeek-R1 ($2.19/1M tokens) is 7.3x more expensive than Llama 4 Scout ($0.30/1M tokens).
In conclusion, DeepSeek-R1 is more expensive than Llama 4 Scout.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-R1 has 562.0B more parameters than Llama 4 Scout, making it 515.6% larger.
Context Window
Maximum input and output token capacity
Llama 4 Scout accepts 10,000,000 input tokens compared to DeepSeek-R1's 131,072 tokens. Llama 4 Scout can generate longer responses up to 10,000,000 tokens, while DeepSeek-R1 is limited to 131,072 tokens.
Input Capabilities
Supported data types and modalities
Llama 4 Scout supports multimodal inputs, whereas DeepSeek-R1 does not.
Llama 4 Scout can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-R1
Llama 4 Scout
License
Usage and distribution terms
DeepSeek-R1 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 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.
Jan 20, 2025
1.4 years ago
Apr 5, 2025
1.2 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.
Provider Availability
DeepSeek-R1 is available from DeepSeek, DeepInfra, Together, Fireworks. Llama 4 Scout is available from DeepInfra, Lambda, Novita, Groq, Fireworks, Together.
DeepSeek-R1
Llama 4 Scout
Outputs Comparison
Key Takeaways
DeepSeek-R1
View detailsDeepSeek
No standout differentiators in the data we have for this pair.
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-R1 and Llama 4 Scout side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-R1 vs Llama 4 Scout.