DeepSeek-V3 vs Llama 4 Scout
DeepSeek-V3 leads the LLM Stats Score 15.1 to 7.3. Llama 4 Scout is 3.1x cheaper per token.
DeepSeek · Meta · Updated for 2026
Which is better?
DeepSeek-V3 leads the overall LLM Stats Score 15.1 to 7.3, ranking #254 overall.
In the 4 individual benchmarks reported for both models, DeepSeek-V3 wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, Llama 4 Scout is roughly 3.1x 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.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V3
- overall performance matters — it scores 15.1 and ranks #254 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 4 of 4 exact shared results
Choose Llama 4 Scout
- cost matters — it's about 3.1x 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
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
20 reported for DeepSeek-V3 · 12 for Llama 4 Scout
DeepSeek-V3 outperforms in 4 benchmarks (GPQA, LiveCodeBench, MMLU, MMLU-Pro), while Llama 4 Scout is better at 0 benchmarks.
DeepSeek-V3 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3 ($0.27/1M tokens) is 3.4x more expensive than Llama 4 Scout ($0.08/1M tokens).
For output processing, DeepSeek-V3 ($0.89/1M tokens) is 3.0x more expensive than Llama 4 Scout ($0.30/1M tokens).
In conclusion, DeepSeek-V3 is more expensive than Llama 4 Scout.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3 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-V3's 131,072 tokens. Llama 4 Scout can generate longer responses up to 10,000,000 tokens, while DeepSeek-V3 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Llama 4 Scout supports multimodal inputs, whereas DeepSeek-V3 does not.
Llama 4 Scout can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V3
Llama 4 Scout
License
Usage and distribution terms
DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), 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 + Model License (Commercial use allowed)
Open weights
Llama 4 Community License Agreement
Open weights
Release Timeline
When each model was launched
DeepSeek-V3 was released on 2024-12-25, while Llama 4 Scout was released on 2025-04-05.
Llama 4 Scout is 3 months newer than DeepSeek-V3.
Dec 25, 2024
1.8 years ago
Apr 5, 2025
1.6 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
DeepSeek-V3 is available from DeepSeek, DeepInfra. Llama 4 Scout is available from DeepInfra, Lambda, Novita, Groq, Fireworks, Together.
DeepSeek-V3
Llama 4 Scout
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3 and Llama 4 Scout side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3 vs Llama 4 Scout.