Model Comparison
DeepSeek-R1 vs Llama 3.1 70B InstructWhich is better in 2026?
Comparing DeepSeek-R1 and Llama 3.1 70B Instruct across benchmarks, pricing, and capabilities.
Verdict: DeepSeek-R1 vs Llama 3.1 70B Instruct — which is better?
DeepSeek-R1 (by DeepSeek) and Llama 3.1 70B Instruct (by Meta) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
On price, Llama 3.1 70B Instruct is roughly 4.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-R1 also accepts a larger context window (131,072 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-R1 if…
- you process long inputs — it offers a 131,072 token context window
- you want the most recent training data — it shipped Jan 2025
Choose Llama 3.1 70B Instruct if…
- cost matters — it's about 4.8x cheaper per token
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-R1 and Llama 3.1 70B Instructdon'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
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
Model Size
Parameter count comparison
DeepSeek-R1 has 601.0B more parameters than Llama 3.1 70B Instruct, making it 858.6% larger.
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.
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.
MIT
Open weights
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.
Jan 20, 2025
1.4 years ago
6mo newerJul 23, 2024
1.9 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-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
Llama 3.1 70B Instruct
Outputs Comparison
Key Takeaways
DeepSeek-R1
View detailsDeepSeek
Detailed Comparison
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FAQ
Common questions about DeepSeek-R1 vs Llama 3.1 70B Instruct.