Model Comparison

DeepSeek-R1 vs Llama 3.1 70B Instruct

Comparing DeepSeek-R1 and Llama 3.1 70B Instruct across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-R1 and Llama 3.1 70B Instruct don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Llama 3.1 70B Instruct costs less

For input processing, DeepSeek-R1 ($0.55/1M tokens) is 2.8x more expensive than Llama 3.1 70B Instruct ($0.20/1M tokens).

For output processing, DeepSeek-R1 ($2.19/1M tokens) is 10.9x more expensive than Llama 3.1 70B Instruct ($0.20/1M tokens).

In conclusion, DeepSeek-R1 is more expensive than Llama 3.1 70B Instruct.*

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

Lowest available price from all providers
Sun Apr 05 2026 • llm-stats.com
DeepSeek
DeepSeek-R1
Input tokens$0.55
Output tokens$2.19
Best providerDeepSeek
Meta
Llama 3.1 70B Instruct
Input tokens$0.20
Output tokens$0.20
Best providerLambda
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

601.0B diff

DeepSeek-R1 has 601.0B more parameters than Llama 3.1 70B Instruct, making it 858.6% larger.

DeepSeek
DeepSeek-R1
671.0Bparameters
Meta
Llama 3.1 70B Instruct
70.0Bparameters
671.0B
DeepSeek-R1
70.0B
Llama 3.1 70B Instruct

Context Window

Maximum input and output token capacity

DeepSeek-R1 accepts 131,072 input tokens compared to Llama 3.1 70B Instruct's 128,000 tokens. DeepSeek-R1 can generate longer responses up to 131,072 tokens, while Llama 3.1 70B Instruct is limited to 128,000 tokens.

DeepSeek
DeepSeek-R1
Input131,072 tokens
Output131,072 tokens
Meta
Llama 3.1 70B Instruct
Input128,000 tokens
Output128,000 tokens
Sun Apr 05 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-R1 is licensed under MIT, while Llama 3.1 70B Instruct uses Llama 3.1 Community License.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek-R1

MIT

Open weights

Llama 3.1 70B Instruct

Llama 3.1 Community License

Open weights

Release Timeline

When each model was launched

DeepSeek-R1 was released on 2025-01-20, while Llama 3.1 70B Instruct was released on 2024-07-23.

DeepSeek-R1 is 6 months newer than Llama 3.1 70B Instruct.

DeepSeek-R1

Jan 20, 2025

1.2 years ago

6mo newer
Llama 3.1 70B Instruct

Jul 23, 2024

1.7 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 is available from DeepSeek, DeepInfra, Together, Fireworks. Llama 3.1 70B Instruct is available from Lambda, DeepInfra, Hyperbolic, Groq, Cerebras, Together, Fireworks, Bedrock, Sambanova.

DeepSeek-R1

deepseek logo
DeepSeek
Input Price:Input: $0.55/1MOutput Price:Output: $2.19/1M
deepinfra logo
Deepinfra
Input Price:Input: $0.85/1MOutput Price:Output: $2.50/1M
together logo
Together
Input Price:Input: $7.00/1MOutput Price:Output: $7.00/1M
fireworks logo
Fireworks
Input Price:Input: $8.00/1MOutput Price:Output: $8.00/1M

Llama 3.1 70B Instruct

lambda logo
Lambda
Input Price:Input: $0.20/1MOutput Price:Output: $0.20/1M
deepinfra logo
Deepinfra
Input Price:Input: $0.35/1MOutput Price:Output: $0.40/1M
hyperbolic logo
Hyperbolic
Input Price:Input: $0.40/1MOutput Price:Output: $0.40/1M
groq logo
Groq
Input Price:Input: $0.59/1MOutput Price:Output: $0.78/1M
cerebras logo
Cerebras
Input Price:Input: $0.60/1MOutput Price:Output: $0.60/1M
together logo
Together
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
fireworks logo
Fireworks
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
bedrock logo
AWS Bedrock
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
sambanova logo
Sambanova
Input Price:Input: $5.00/1MOutput Price:Output: $10.00/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
Meta
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-R1
Meta
Llama 3.1 70B Instruct

FAQ

Common questions about DeepSeek-R1 vs Llama 3.1 70B Instruct

DeepSeek-R1 (DeepSeek) and Llama 3.1 70B Instruct (Meta) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
Llama 3.1 70B Instruct scores GSM-8K (CoT): 95.1%, ARC-C: 94.8%, API-Bank: 90.0%, IFEval: 87.5%, Multilingual MGSM (CoT): 86.9%.
Llama 3.1 70B Instruct is 2.8x cheaper for input tokens. DeepSeek-R1 costs $0.55/M input and $2.19/M output via deepseek. Llama 3.1 70B Instruct costs $0.20/M input and $0.20/M output via lambda.
DeepSeek-R1 supports 131K tokens and Llama 3.1 70B Instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (131K vs 128K), input pricing ($0.55 vs $0.20/M), licensing (MIT vs Llama 3.1 Community License). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-R1 is developed by DeepSeek and Llama 3.1 70B Instruct is developed by Meta.