Model Comparison

DeepSeek-V3.1 vs Llama 4 Scout

DeepSeek-V3.1 significantly outperforms across most benchmarks. Llama 4 Scout is 3.4x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

DeepSeek-V3.1 outperforms in 3 benchmarks (GPQA, LiveCodeBench, MMLU-Pro), while Llama 4 Scout is better at 0 benchmarks.

DeepSeek-V3.1 significantly outperforms across most benchmarks.

Tue May 12 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Llama 4 Scout costs less

For input processing, DeepSeek-V3.1 ($0.27/1M tokens) is 3.4x more expensive than Llama 4 Scout ($0.08/1M tokens).

For output processing, DeepSeek-V3.1 ($1.00/1M tokens) is 3.3x more expensive than Llama 4 Scout ($0.30/1M tokens).

In conclusion, DeepSeek-V3.1 is more expensive than Llama 4 Scout.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Tue May 12 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.1
Input tokens$0.27
Output tokens$1.00
Best providerDeepinfra
Meta
Llama 4 Scout
Input tokens$0.08
Output tokens$0.30
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

562.0B diff

DeepSeek-V3.1 has 562.0B more parameters than Llama 4 Scout, making it 515.6% larger.

DeepSeek
DeepSeek-V3.1
671.0Bparameters
Meta
Llama 4 Scout
109.0Bparameters
671.0B
DeepSeek-V3.1
109.0B
Llama 4 Scout

Context Window

Maximum input and output token capacity

Llama 4 Scout accepts 10,000,000 input tokens compared to DeepSeek-V3.1's 163,840 tokens. Llama 4 Scout can generate longer responses up to 10,000,000 tokens, while DeepSeek-V3.1 is limited to 163,840 tokens.

DeepSeek
DeepSeek-V3.1
Input163,840 tokens
Output163,840 tokens
Meta
Llama 4 Scout
Input10,000,000 tokens
Output10,000,000 tokens
Tue May 12 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Llama 4 Scout supports multimodal inputs, whereas DeepSeek-V3.1 does not.

Llama 4 Scout can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V3.1

Text
Images
Audio
Video

Llama 4 Scout

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.1 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.

DeepSeek-V3.1

MIT

Open weights

Llama 4 Scout

Llama 4 Community License Agreement

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.1 was released on 2025-01-10, while Llama 4 Scout was released on 2025-04-05.

Llama 4 Scout is 3 months newer than DeepSeek-V3.1.

DeepSeek-V3.1

Jan 10, 2025

1.3 years ago

Llama 4 Scout

Apr 5, 2025

1.1 years ago

2mo newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

DeepSeek-V3.1 is available from DeepInfra, Novita. Llama 4 Scout is available from DeepInfra, Lambda, Novita, Groq, Fireworks, Together.

DeepSeek-V3.1

deepinfra logo
Deepinfra
Input Price:Input: $0.27/1MOutput Price:Output: $1.00/1M
novita logo
Novita
Input Price:Input: $0.27/1MOutput Price:Output: $1.00/1M

Llama 4 Scout

deepinfra logo
Deepinfra
Input Price:Input: $0.08/1MOutput Price:Output: $0.30/1M
lambda logo
Lambda
Input Price:Input: $0.08/1MOutput Price:Output: $0.30/1M
novita logo
Novita
Input Price:Input: $0.10/1MOutput Price:Output: $0.50/1M
groq logo
Groq
Input Price:Input: $0.11/1MOutput Price:Output: $0.34/1M
fireworks logo
Fireworks
Input Price:Input: $0.15/1MOutput Price:Output: $0.60/1M
together logo
Together
Input Price:Input: $0.18/1MOutput Price:Output: $0.59/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek
Higher GPQA score (74.9% vs 57.2%)
Higher LiveCodeBench score (56.4% vs 32.8%)
Higher MMLU-Pro score (83.7% vs 74.3%)
Meta
Larger context window (10,000,000 tokens)
Supports multimodal inputs
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.1
Meta
Llama 4 Scout

FAQ

Common questions about DeepSeek-V3.1 vs Llama 4 Scout.

Which is better, DeepSeek-V3.1 or Llama 4 Scout?

DeepSeek-V3.1 significantly outperforms across most benchmarks. DeepSeek-V3.1 is made by DeepSeek and Llama 4 Scout is made by Meta. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-V3.1 compare to Llama 4 Scout in benchmarks?

DeepSeek-V3.1 scores SimpleQA: 93.4%, MMLU-Redux: 91.8%, MMLU-Pro: 83.7%, GPQA: 74.9%, CodeForces: 69.7%. Llama 4 Scout scores DocVQA: 94.4%, MGSM: 90.6%, ChartQA: 88.8%, MMLU: 79.6%, MMLU-Pro: 74.3%.

Is DeepSeek-V3.1 cheaper than Llama 4 Scout?

Llama 4 Scout is 3.4x cheaper for input tokens. DeepSeek-V3.1 costs $0.27/M input and $1.00/M output via deepinfra. Llama 4 Scout costs $0.08/M input and $0.30/M output via deepinfra.

What are the context window sizes for DeepSeek-V3.1 and Llama 4 Scout?

DeepSeek-V3.1 supports 164K tokens and Llama 4 Scout supports 10.0M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V3.1 and Llama 4 Scout?

Key differences include context window (164K vs 10.0M), input pricing ($0.27 vs $0.08/M), multimodal support (no vs yes), licensing (MIT vs Llama 4 Community License Agreement). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.1 and Llama 4 Scout?

DeepSeek-V3.1 is developed by DeepSeek and Llama 4 Scout is developed by Meta.