DeepSeek-R1 vs Llama 3.2 3B Instruct
Comparing DeepSeek-R1 and Llama 3.2 3B Instruct across benchmarks, pricing, and capabilities.
DeepSeek · Meta · Updated for 2026
Which is better?
DeepSeek-R1 and Llama 3.2 3B Instruct trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, Llama 3.2 3B Instruct is roughly 76.8x 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.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-R1
- 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 3.2 3B Instruct
- cost matters — it's about 76.8x cheaper per token
At a glance
The differences that matter most.
Individual benchmarks
0 reported for DeepSeek-R1 · 15 for Llama 3.2 3B Instruct
DeepSeek-R1 and Llama 3.2 3B Instructdon'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-R1 ($0.55/1M tokens) is 55.0x more expensive than Llama 3.2 3B Instruct ($0.01/1M tokens).
For output processing, DeepSeek-R1 ($2.19/1M tokens) is 109.5x more expensive than Llama 3.2 3B Instruct ($0.02/1M tokens).
In conclusion, DeepSeek-R1 is more expensive than Llama 3.2 3B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-R1 has 667.8B more parameters than Llama 3.2 3B Instruct, making it 20803.4% larger.
Context Window
Maximum input and output token capacity
DeepSeek-R1 accepts 131,072 input tokens compared to Llama 3.2 3B Instruct's 128,000 tokens. DeepSeek-R1 can generate longer responses up to 131,072 tokens, while Llama 3.2 3B Instruct is limited to 128,000 tokens.
License
Usage and distribution terms
DeepSeek-R1 is licensed under MIT, while Llama 3.2 3B Instruct uses Llama 3.2 Community License.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Llama 3.2 Community License
Open weights
Release Timeline
When each model was launched
DeepSeek-R1 was released on 2025-01-20, while Llama 3.2 3B Instruct was released on 2024-09-25.
DeepSeek-R1 is 4 months newer than Llama 3.2 3B Instruct.
Jan 20, 2025
1.7 years ago
3mo newerSep 25, 2024
2.0 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 3.2 3B Instruct is available from DeepInfra.
DeepSeek-R1
Llama 3.2 3B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-R1 and Llama 3.2 3B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1 vs Llama 3.2 3B Instruct.