Model Comparison

DeepSeek-R1-0528 vs Llama 4 Scout

DeepSeek-R1-0528 significantly outperforms across most benchmarks. Llama 4 Scout is 6.8x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

DeepSeek-R1-0528 outperforms in 3 benchmarks (GPQA, LiveCodeBench, MMLU-Pro), while Llama 4 Scout is better at 0 benchmarks.

DeepSeek-R1-0528 significantly outperforms across most benchmarks.

Sat May 02 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-R1-0528 ($0.50/1M tokens) is 6.3x more expensive than Llama 4 Scout ($0.08/1M tokens).

For output processing, DeepSeek-R1-0528 ($2.15/1M tokens) is 7.2x more expensive than Llama 4 Scout ($0.30/1M tokens).

In conclusion, DeepSeek-R1-0528 is more expensive than Llama 4 Scout.*

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

Lowest available price from all providers
Sat May 02 2026 • llm-stats.com
DeepSeek
DeepSeek-R1-0528
Input tokens$0.50
Output tokens$2.15
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-R1-0528 has 562.0B more parameters than Llama 4 Scout, making it 515.6% larger.

DeepSeek
DeepSeek-R1-0528
671.0Bparameters
Meta
Llama 4 Scout
109.0Bparameters
671.0B
DeepSeek-R1-0528
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-R1-0528's 131,072 tokens. Llama 4 Scout can generate longer responses up to 10,000,000 tokens, while DeepSeek-R1-0528 is limited to 131,072 tokens.

DeepSeek
DeepSeek-R1-0528
Input131,072 tokens
Output131,072 tokens
Meta
Llama 4 Scout
Input10,000,000 tokens
Output10,000,000 tokens
Sat May 02 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Llama 4 Scout supports multimodal inputs, whereas DeepSeek-R1-0528 does not.

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

DeepSeek-R1-0528

Text
Images
Audio
Video

Llama 4 Scout

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-R1-0528 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-R1-0528

MIT

Open weights

Llama 4 Scout

Llama 4 Community License Agreement

Open weights

Release Timeline

When each model was launched

DeepSeek-R1-0528 was released on 2025-05-28, while Llama 4 Scout was released on 2025-04-05.

DeepSeek-R1-0528 is 2 months newer than Llama 4 Scout.

DeepSeek-R1-0528

May 28, 2025

11 months ago

1mo newer
Llama 4 Scout

Apr 5, 2025

1.1 years ago

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-R1-0528 is available from DeepInfra, DeepSeek, Novita. Llama 4 Scout is available from DeepInfra, Lambda, Novita, Groq, Fireworks, Together.

DeepSeek-R1-0528

deepinfra logo
Deepinfra
Input Price:Input: $0.50/1MOutput Price:Output: $2.15/1M
deepseek logo
DeepSeek
Input Price:Input: $0.55/1MOutput Price:Output: $2.19/1M
novita logo
Novita
Input Price:Input: $0.70/1MOutput Price:Output: $2.50/1M

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 (81.0% vs 57.2%)
Higher LiveCodeBench score (73.3% vs 32.8%)
Higher MMLU-Pro score (85.0% 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-R1-0528
Meta
Llama 4 Scout

FAQ

Common questions about DeepSeek-R1-0528 vs Llama 4 Scout

DeepSeek-R1-0528 significantly outperforms across most benchmarks. DeepSeek-R1-0528 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.
DeepSeek-R1-0528 scores MMLU-Redux: 93.4%, SimpleQA: 92.3%, AIME 2024: 91.4%, AIME 2025: 87.5%, MMLU-Pro: 85.0%. Llama 4 Scout scores DocVQA: 94.4%, MGSM: 90.6%, ChartQA: 88.8%, MMLU: 79.6%, MMLU-Pro: 74.3%.
Llama 4 Scout is 6.3x cheaper for input tokens. DeepSeek-R1-0528 costs $0.50/M input and $2.15/M output via deepinfra. Llama 4 Scout costs $0.08/M input and $0.30/M output via deepinfra.
DeepSeek-R1-0528 supports 131K 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.
Key differences include context window (131K vs 10.0M), input pricing ($0.50 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.
DeepSeek-R1-0528 is developed by DeepSeek and Llama 4 Scout is developed by Meta.