DeepSeek R1 Zero vs Llama 3.2 90B Instruct
DeepSeek R1 Zero leads the LLM Stats Score 16.0 to 5.2.
DeepSeek · Meta · Updated for 2026
Which is better?
DeepSeek R1 Zero leads the overall LLM Stats Score 16.0 to 5.2, ranking #233 overall.
In the 1 individual benchmarks reported for both models, DeepSeek R1 Zero wins 1; this is a narrower head-to-head signal than the composite indexes.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek R1 Zero
- overall performance matters — it scores 16.0 and ranks #233 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you want the most recent training data — it shipped Jan 2025
Choose Llama 3.2 90B Instruct
- you want predictable pricing at $0.35/M input and $0.40/M output
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
4 reported for DeepSeek R1 Zero · 13 for Llama 3.2 90B Instruct
DeepSeek R1 Zero outperforms in 1 benchmarks (GPQA), while Llama 3.2 90B Instruct is better at 0 benchmarks.
DeepSeek R1 Zero significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
DeepSeek R1 Zero has 581.0B more parameters than Llama 3.2 90B Instruct, making it 645.6% larger.
Context Window
Maximum input and output token capacity
Only Llama 3.2 90B Instruct specifies input context (128,000 tokens). Only Llama 3.2 90B Instruct specifies output context (128,000 tokens).
Input capabilities
Documented input modalities across available providers
Llama 3.2 90B Instruct supports multimodal inputs, whereas DeepSeek R1 Zero does not.
Llama 3.2 90B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek R1 Zero
Llama 3.2 90B Instruct
License
Usage and distribution terms
DeepSeek R1 Zero is licensed under MIT, while Llama 3.2 90B Instruct uses Llama 3.2.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Llama 3.2
Open weights
Release Timeline
When each model was launched
DeepSeek R1 Zero was released on 2025-01-20, while Llama 3.2 90B Instruct was released on 2024-09-25.
DeepSeek R1 Zero is 4 months newer than Llama 3.2 90B 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.
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek R1 Zero and Llama 3.2 90B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek R1 Zero vs Llama 3.2 90B Instruct.