DeepSeek-V4.1-Flash-1017 vs Llama 4 Scout
DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 7.3. Llama 4 Scout is 3.9x cheaper per token.
DeepSeek · Meta · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.2 to 7.3, ranking #15 overall.
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.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4.1-Flash-1017
- overall performance matters — it scores 51.2 and ranks #15 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you want the most recent training data — it shipped Oct 2026
Choose Llama 4 Scout
- cost matters — it's about 3.9x cheaper per token
- you process long inputs — it offers a 10,000,000 token context window
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
11 reported for DeepSeek-V4.1-Flash-1017 · 12 for Llama 4 Scout
DeepSeek-V4.1-Flash-1017 and Llama 4 Scoutdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4.1-Flash-1017 ($0.30/1M tokens) is 3.8x more expensive than Llama 4 Scout ($0.08/1M tokens).
For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) is 4.0x more expensive than Llama 4 Scout ($0.30/1M tokens).
In conclusion, DeepSeek-V4.1-Flash-1017 is more expensive than Llama 4 Scout.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash-1017 has 654.2B more parameters than Llama 4 Scout, making it 600.2% larger.
Context Window
Maximum input and output token capacity
Llama 4 Scout accepts 10,000,000 input tokens compared to DeepSeek-V4.1-Flash-1017's 1,048,576 tokens. Llama 4 Scout can generate longer responses up to 10,000,000 tokens, while DeepSeek-V4.1-Flash-1017 is limited to 393,216 tokens.
Input capabilities
Documented input modalities across available providers
Both DeepSeek-V4.1-Flash-1017 and Llama 4 Scout support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
DeepSeek-V4.1-Flash-1017
Llama 4 Scout
License
Usage and distribution terms
DeepSeek-V4.1-Flash-1017 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-V4.1-Flash-1017 was released on 2026-10-17, while Llama 4 Scout was released on 2025-04-05.
DeepSeek-V4.1-Flash-1017 is 19 months newer than Llama 4 Scout.
Oct 17, 2026
2 days ago
1.5yr newerApr 5, 2025
1.5 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-V4.1-Flash-1017 is available from DeepSeek. Llama 4 Scout is available from DeepInfra, Lambda, Novita, Groq, Fireworks, Together.
DeepSeek-V4.1-Flash-1017
Llama 4 Scout
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash-1017 and Llama 4 Scout side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash-1017 vs Llama 4 Scout.