Model Comparison

DeepSeek-V3.1 vs Nemotron Nano 9B v2

Nemotron Nano 9B v2 shows notably better performance in the majority of benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

DeepSeek-V3.1 outperforms in 1 benchmarks (GPQA), while Nemotron Nano 9B v2 is better at 2 benchmarks (AIME 2025, LiveCodeBench).

Nemotron Nano 9B v2 shows notably better performance in the majority of benchmarks.

Thu May 14 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

662.1B diff

DeepSeek-V3.1 has 662.1B more parameters than Nemotron Nano 9B v2, making it 7439.3% larger.

DeepSeek
DeepSeek-V3.1
671.0Bparameters
NVIDIA
Nemotron Nano 9B v2
8.9Bparameters
671.0B
DeepSeek-V3.1
8.9B
Nemotron Nano 9B v2

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.1 specifies input context (163,840 tokens). Only DeepSeek-V3.1 specifies output context (163,840 tokens).

DeepSeek
DeepSeek-V3.1
Input163,840 tokens
Output163,840 tokens
NVIDIA
Nemotron Nano 9B v2
Input- tokens
Output- tokens
Thu May 14 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3.1 is licensed under MIT, while Nemotron Nano 9B v2 uses NVIDIA Open Model License Agreement .

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

DeepSeek-V3.1

MIT

Open weights

Nemotron Nano 9B v2

NVIDIA Open Model License Agreement

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.1 was released on 2025-01-10, while Nemotron Nano 9B v2 was released on 2025-08-18.

Nemotron Nano 9B v2 is 7 months newer than DeepSeek-V3.1.

DeepSeek-V3.1

Jan 10, 2025

1.3 years ago

Nemotron Nano 9B v2

Aug 18, 2025

8 months ago

7mo newer

Knowledge Cutoff

When training data ends

Nemotron Nano 9B v2 has a documented knowledge cutoff of 2024-09-01, while DeepSeek-V3.1's cutoff date is not specified.

We can confirm Nemotron Nano 9B v2's training data extends to 2024-09-01, but cannot make a direct comparison without DeepSeek-V3.1's cutoff date.

DeepSeek-V3.1

—

Nemotron Nano 9B v2

Sep 2024

Outputs Comparison

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Key Takeaways

DeepSeek
Larger context window (163,840 tokens)
Higher GPQA score (74.9% vs 64.0%)
NVIDIA
Higher AIME 2025 score (72.1% vs 49.8%)
Higher LiveCodeBench score (71.1% vs 56.4%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.1
NVIDIA
Nemotron Nano 9B v2

FAQ

Common questions about DeepSeek-V3.1 vs Nemotron Nano 9B v2.

Which is better, DeepSeek-V3.1 or Nemotron Nano 9B v2?

Nemotron Nano 9B v2 shows notably better performance in the majority of benchmarks. DeepSeek-V3.1 is made by DeepSeek and Nemotron Nano 9B v2 is made by NVIDIA. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-V3.1 compare to Nemotron Nano 9B v2 in benchmarks?

DeepSeek-V3.1 scores SimpleQA: 93.4%, MMLU-Redux: 91.8%, MMLU-Pro: 83.7%, GPQA: 74.9%, CodeForces: 69.7%. Nemotron Nano 9B v2 scores MATH-500: 97.8%, IFEval: 90.3%, AIME 2025: 72.1%, LiveCodeBench: 71.1%, BFCL_v3_MultiTurn: 66.9%.

What are the context window sizes for DeepSeek-V3.1 and Nemotron Nano 9B v2?

DeepSeek-V3.1 supports 164K tokens and Nemotron Nano 9B v2 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-V3.1 and Nemotron Nano 9B v2?

Key differences include licensing (MIT vs NVIDIA Open Model License Agreement ). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.1 and Nemotron Nano 9B v2?

DeepSeek-V3.1 is developed by DeepSeek and Nemotron Nano 9B v2 is developed by NVIDIA.