Model Comparison

DeepSeek-V3.2 (Non-thinking) vs Llama 3.1 Nemotron Nano 8B V1Which is better in 2026?

Comparing DeepSeek-V3.2 (Non-thinking) and Llama 3.1 Nemotron Nano 8B V1 across benchmarks, pricing, and capabilities.

Verdict: DeepSeek-V3.2 (Non-thinking) vs Llama 3.1 Nemotron Nano 8B V1 — which is better?

DeepSeek-V3.2 (Non-thinking) (by DeepSeek) and Llama 3.1 Nemotron Nano 8B V1 (by NVIDIA) 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.

Choose DeepSeek-V3.2 (Non-thinking) if…

  • you want the most recent training data — it shipped Dec 2025

Choose Llama 3.1 Nemotron Nano 8B V1 if…

  • you are already invested in the NVIDIA ecosystem
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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V3.2 (Non-thinking) and Llama 3.1 Nemotron Nano 8B V1don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Model Size

Parameter count comparison

677.0B diff

DeepSeek-V3.2 (Non-thinking) has 677.0B more parameters than Llama 3.1 Nemotron Nano 8B V1, making it 8462.5% larger.

DeepSeek
DeepSeek-V3.2 (Non-thinking)
685.0Bparameters
NVIDIA
Llama 3.1 Nemotron Nano 8B V1
8.0Bparameters
685.0B
DeepSeek-V3.2 (Non-thinking)
8.0B
Llama 3.1 Nemotron Nano 8B V1

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.2 (Non-thinking) specifies input context (131,072 tokens). Only DeepSeek-V3.2 (Non-thinking) specifies output context (8,192 tokens).

DeepSeek
DeepSeek-V3.2 (Non-thinking)
Input131,072 tokens
Output8,192 tokens
NVIDIA
Llama 3.1 Nemotron Nano 8B V1
Input- tokens
Output- tokens
Wed Jun 24 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3.2 (Non-thinking) is licensed under MIT, while Llama 3.1 Nemotron Nano 8B V1 uses Llama 3.1 Community License.

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

DeepSeek-V3.2 (Non-thinking)

MIT

Open weights

Llama 3.1 Nemotron Nano 8B V1

Llama 3.1 Community License

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2 (Non-thinking) was released on 2025-12-01, while Llama 3.1 Nemotron Nano 8B V1 was released on 2025-03-18.

DeepSeek-V3.2 (Non-thinking) is 9 months newer than Llama 3.1 Nemotron Nano 8B V1.

DeepSeek-V3.2 (Non-thinking)

Dec 1, 2025

6 months ago

8mo newer
Llama 3.1 Nemotron Nano 8B V1

Mar 18, 2025

1.3 years ago

Knowledge Cutoff

When training data ends

Llama 3.1 Nemotron Nano 8B V1 has a documented knowledge cutoff of 2023-12-31, while DeepSeek-V3.2 (Non-thinking)'s cutoff date is not specified.

We can confirm Llama 3.1 Nemotron Nano 8B V1's training data extends to 2023-12-31, but cannot make a direct comparison without DeepSeek-V3.2 (Non-thinking)'s cutoff date.

DeepSeek-V3.2 (Non-thinking)

—

Llama 3.1 Nemotron Nano 8B V1

Dec 2023

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
NVIDIA

No standout differentiators in the data we have for this pair.

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.2 (Non-thinking)
NVIDIA
Llama 3.1 Nemotron Nano 8B V1

FAQ

Common questions about DeepSeek-V3.2 (Non-thinking) vs Llama 3.1 Nemotron Nano 8B V1.

Which is better, DeepSeek-V3.2 (Non-thinking) or Llama 3.1 Nemotron Nano 8B V1?

DeepSeek-V3.2 (Non-thinking) (DeepSeek) and Llama 3.1 Nemotron Nano 8B V1 (NVIDIA) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does DeepSeek-V3.2 (Non-thinking) compare to Llama 3.1 Nemotron Nano 8B V1 in benchmarks?

Llama 3.1 Nemotron Nano 8B V1 scores MATH-500: 95.4%, MBPP: 84.6%, MT-Bench: 81.0%, IFEval: 79.3%, BFCL v2: 63.6%.

What are the context window sizes for DeepSeek-V3.2 (Non-thinking) and Llama 3.1 Nemotron Nano 8B V1?

DeepSeek-V3.2 (Non-thinking) supports 131K tokens and Llama 3.1 Nemotron Nano 8B V1 supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V3.2 (Non-thinking) and Llama 3.1 Nemotron Nano 8B V1?

Key differences include licensing (MIT vs Llama 3.1 Community License). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.2 (Non-thinking) and Llama 3.1 Nemotron Nano 8B V1?

DeepSeek-V3.2 (Non-thinking) is developed by DeepSeek and Llama 3.1 Nemotron Nano 8B V1 is developed by NVIDIA.

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