Model Comparison

DeepSeek R1 Zero vs Llama 3.3 70B Instruct

DeepSeek R1 Zero significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

DeepSeek R1 Zero outperforms in 1 benchmarks (GPQA), while Llama 3.3 70B Instruct is better at 0 benchmarks.

DeepSeek R1 Zero significantly outperforms across most benchmarks.

Thu May 14 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

601.0B diff

DeepSeek R1 Zero has 601.0B more parameters than Llama 3.3 70B Instruct, making it 858.6% larger.

DeepSeek
DeepSeek R1 Zero
671.0Bparameters
Meta
Llama 3.3 70B Instruct
70.0Bparameters
671.0B
DeepSeek R1 Zero
70.0B
Llama 3.3 70B Instruct

Context Window

Maximum input and output token capacity

Only Llama 3.3 70B Instruct specifies input context (128,000 tokens). Only Llama 3.3 70B Instruct specifies output context (128,000 tokens).

DeepSeek
DeepSeek R1 Zero
Input- tokens
Output- tokens
Meta
Llama 3.3 70B Instruct
Input128,000 tokens
Output128,000 tokens
Thu May 14 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek R1 Zero is licensed under MIT, while Llama 3.3 70B Instruct uses Llama 3.3 Community License Agreement.

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

DeepSeek R1 Zero

MIT

Open weights

Llama 3.3 70B Instruct

Llama 3.3 Community License Agreement

Open weights

Release Timeline

When each model was launched

DeepSeek R1 Zero was released on 2025-01-20, while Llama 3.3 70B Instruct was released on 2024-12-06.

DeepSeek R1 Zero is 2 months newer than Llama 3.3 70B Instruct.

DeepSeek R1 Zero

Jan 20, 2025

1.3 years ago

1mo newer
Llama 3.3 70B Instruct

Dec 6, 2024

1.4 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

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Higher GPQA score (73.3% vs 50.5%)
Meta
Larger context window (128,000 tokens)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek R1 Zero
Meta
Llama 3.3 70B Instruct

FAQ

Common questions about DeepSeek R1 Zero vs Llama 3.3 70B Instruct.

Which is better, DeepSeek R1 Zero or Llama 3.3 70B Instruct?

DeepSeek R1 Zero significantly outperforms across most benchmarks. DeepSeek R1 Zero is made by DeepSeek and Llama 3.3 70B Instruct is made by Meta. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek R1 Zero compare to Llama 3.3 70B Instruct in benchmarks?

DeepSeek R1 Zero scores MATH-500: 95.9%, AIME 2024: 86.7%, GPQA: 73.3%, LiveCodeBench: 50.0%. Llama 3.3 70B Instruct scores IFEval: 92.1%, MGSM: 91.1%, HumanEval: 88.4%, MBPP EvalPlus: 87.6%, MMLU: 86.0%.

What are the context window sizes for DeepSeek R1 Zero and Llama 3.3 70B Instruct?

DeepSeek R1 Zero supports an unknown number of tokens and Llama 3.3 70B Instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek R1 Zero and Llama 3.3 70B Instruct?

Key differences include licensing (MIT vs Llama 3.3 Community License Agreement). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek R1 Zero and Llama 3.3 70B Instruct?

DeepSeek R1 Zero is developed by DeepSeek and Llama 3.3 70B Instruct is developed by Meta.