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DeepSeek-V3.2 (Thinking) vs Nemotron Nano 9B v2

DeepSeek-V3.2 (Thinking) leads the LLM Stats Score 32.9 to 18.6.

DeepSeek · NVIDIA · Updated for 2026

Which is better?

DeepSeek-V3.2 (Thinking) leads the overall LLM Stats Score 32.9 to 18.6, ranking #96 overall.

In the 3 individual benchmarks reported for both models, DeepSeek-V3.2 (Thinking) wins 3; 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-V3.2 (Thinking)

  • overall performance matters — it scores 32.9 and ranks #96 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 3 of 3 exact shared results
  • you want the most recent training data — it shipped Dec 2025

Choose Nemotron Nano 9B v2

  • you are already invested in the NVIDIA ecosystem

At a glance

The differences that matter most.

DeepSeek-V3.2 (Thinking)
DeepSeek
Nemotron Nano 9B v2
NVIDIA
Core performance indexes
LLM Stats Score
32.9
#96
18.6
#200
Reasoning
32.9
#94
17.4
#198
Coding
22.9
#74
14.6
#125
Cost, coverage & limits
Benchmark wins
3 of 3
0 of 3
Input price
$0.28 / M
— / M
Output price
$0.42 / M
— / M
Context window
131,072
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V3.2 (Thinking)
Nemotron Nano 9B v2
Math
30.6#71
16.5#197
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

14 reported for DeepSeek-V3.2 (Thinking) · 6 for Nemotron Nano 9B v2

3 shared

DeepSeek-V3.2 (Thinking) outperforms in 3 benchmarks (AIME 2025, GPQA, LiveCodeBench), while Nemotron Nano 9B v2 is better at 0 benchmarks.

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.

Fri Sep 04 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

676.1B diff

DeepSeek-V3.2 (Thinking) has 676.1B more parameters than Nemotron Nano 9B v2, making it 7596.6% larger.

DeepSeek
DeepSeek-V3.2 (Thinking)
685.0Bparameters
NVIDIA
Nemotron Nano 9B v2
8.9Bparameters
685.0B
DeepSeek-V3.2 (Thinking)
8.9B
Nemotron Nano 9B v2

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.2 (Thinking) specifies input context (131,072 tokens). Only DeepSeek-V3.2 (Thinking) specifies output context (65,536 tokens).

DeepSeek
DeepSeek-V3.2 (Thinking)
Input131,072 tokens
Output65,536 tokens
NVIDIA
Nemotron Nano 9B v2
Input- tokens
Output- tokens
Fri Sep 04 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3.2 (Thinking) 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-V3.2 (Thinking)

MIT

Open weights

Nemotron Nano 9B v2

NVIDIA Open Model License Agreement

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2 (Thinking) was released on 2025-12-01, while Nemotron Nano 9B v2 was released on 2025-08-18.

DeepSeek-V3.2 (Thinking) is 4 months newer than Nemotron Nano 9B v2.

DeepSeek-V3.2 (Thinking)

Dec 1, 2025

9 months ago

3mo newer
Nemotron Nano 9B v2

Aug 18, 2025

1.0 years ago

Knowledge Cutoff

When training data ends

Nemotron Nano 9B v2 has a documented knowledge cutoff of 2024-09-01, while DeepSeek-V3.2 (Thinking)'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-V3.2 (Thinking)'s cutoff date.

DeepSeek-V3.2 (Thinking)

—

Nemotron Nano 9B v2

Sep 2024

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against DeepSeek-V3.2 (Thinking) and Nemotron Nano 9B v2 side-by-side, then vote on the output you prefer.

DeepSeek-V3.2 (Thinking)
✓ Preferred
Nemotron Nano 9B v2
Open in Playground

FAQ

Common questions about DeepSeek-V3.2 (Thinking) vs Nemotron Nano 9B v2.

Which is better, DeepSeek-V3.2 (Thinking) or Nemotron Nano 9B v2?

DeepSeek-V3.2 (Thinking) leads the LLM Stats Score 32.9 to 18.6. DeepSeek-V3.2 (Thinking) is made by DeepSeek and Nemotron Nano 9B v2 is made by NVIDIA. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does DeepSeek-V3.2 (Thinking) compare to Nemotron Nano 9B v2 in benchmarks?

DeepSeek-V3.2 (Thinking) scores AIME 2025: 93.1%, HMMT 2025: 90.2%, MMLU-Pro: 85.0%, LiveCodeBench: 83.3%, GPQA: 82.4%. 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-V3.2 (Thinking) and Nemotron Nano 9B v2?

DeepSeek-V3.2 (Thinking) supports 131K 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-V3.2 (Thinking) and Nemotron Nano 9B v2?

Key differences include LLM Stats Score (32.9 vs 18.6), licensing (MIT vs NVIDIA Open Model License Agreement ). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.2 (Thinking) and Nemotron Nano 9B v2?

DeepSeek-V3.2 (Thinking) is developed by DeepSeek and Nemotron Nano 9B v2 is developed by NVIDIA.

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