DeepSeek-V4-Pro-0813 vs Llama 4 Scout
Comparing DeepSeek-V4-Pro-0813 and Llama 4 Scout across benchmarks, pricing, and capabilities.
DeepSeek · Meta · Updated for 2026
Which is better?
DeepSeek-V4-Pro-0813 and Llama 4 Scout trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, Llama 4 Scout is roughly 4.0x 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.
Based on current benchmark, pricing, and model metadata for 2026.
Choose DeepSeek-V4-Pro-0813
- you want the most recent training data — it shipped Aug 2026
Choose Llama 4 Scout
- cost matters — it's about 4.0x cheaper per token
- you process long inputs — it offers a 10,000,000 token context window
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Pro-0813 and Llama 4 Scoutdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Pro-0813 ($0.43/1M tokens) is 5.4x more expensive than Llama 4 Scout ($0.08/1M tokens).
For output processing, DeepSeek-V4-Pro-0813 ($0.87/1M tokens) is 2.9x more expensive than Llama 4 Scout ($0.30/1M tokens).
In conclusion, DeepSeek-V4-Pro-0813 is more expensive than Llama 4 Scout.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Pro-0813 has 1491.0B more parameters than Llama 4 Scout, making it 1367.9% larger.
Context Window
Maximum input and output token capacity
Llama 4 Scout accepts 10,000,000 input tokens compared to DeepSeek-V4-Pro-0813's 1,048,576 tokens. Llama 4 Scout can generate longer responses up to 10,000,000 tokens, while DeepSeek-V4-Pro-0813 is limited to 393,216 tokens.
Input Capabilities
Supported data types and modalities
Llama 4 Scout supports multimodal inputs, whereas DeepSeek-V4-Pro-0813 does not.
Llama 4 Scout can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Pro-0813
Llama 4 Scout
License
Usage and distribution terms
DeepSeek-V4-Pro-0813 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-V4-Pro-0813 was released on 2026-08-13, while Llama 4 Scout was released on 2025-04-05.
DeepSeek-V4-Pro-0813 is 17 months newer than Llama 4 Scout.
Aug 13, 2026
1 weeks ago
1.4yr newerApr 5, 2025
1.4 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V4-Pro-0813 is available from DeepSeek, DeepInfra, Novita, Together. Llama 4 Scout is available from DeepInfra, Lambda, Novita, Groq, Fireworks, Together.
DeepSeek-V4-Pro-0813
Llama 4 Scout
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-0813 and Llama 4 Scout side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-0813 vs Llama 4 Scout.