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DeepSeek-V4-Pro-0813 vs Nemotron Nano 9B v2

Comparing DeepSeek-V4-Pro-0813 and Nemotron Nano 9B v2 across benchmarks, pricing, and capabilities.

DeepSeek · NVIDIA · Updated for 2026

Which is better?

DeepSeek-V4-Pro-0813 and Nemotron Nano 9B v2 trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

Based on current benchmark, pricing, and model metadata for 2026.

Choose DeepSeek-V4-Pro-0813

  • you want the most recent training data — it shipped Aug 2026

Choose Nemotron Nano 9B v2

  • you are already invested in the NVIDIA ecosystem

At a glance

The differences that matter most.

DeepSeek-V4-Pro-0813
DeepSeek
Nemotron Nano 9B v2
NVIDIA
Benchmark wins
—
—
Input price
$0.43 / M
— / M
Output price
$0.87 / M
— / M
Context window
1,048,576
—
Released
Aug 2026
Aug 2025
License
MIT
NVIDIA Open Model License Agreement

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V4-Pro-0813 and Nemotron Nano 9B v2don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

1591.1B diff

DeepSeek-V4-Pro-0813 has 1591.1B more parameters than Nemotron Nano 9B v2, making it 17877.5% larger.

DeepSeek
DeepSeek-V4-Pro-0813
1.6Tparameters
NVIDIA
Nemotron Nano 9B v2
8.9Bparameters
1600.0B
DeepSeek-V4-Pro-0813
8.9B
Nemotron Nano 9B v2

Context Window

Maximum input and output token capacity

Only DeepSeek-V4-Pro-0813 specifies input context (1,048,576 tokens). Only DeepSeek-V4-Pro-0813 specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4-Pro-0813
Input1,048,576 tokens
Output393,216 tokens
NVIDIA
Nemotron Nano 9B v2
Input- tokens
Output- tokens
Tue Aug 25 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V4-Pro-0813 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-V4-Pro-0813

MIT

Open weights

Nemotron Nano 9B v2

NVIDIA Open Model License Agreement

Open weights

Release Timeline

When each model was launched

DeepSeek-V4-Pro-0813 was released on 2026-08-13, while Nemotron Nano 9B v2 was released on 2025-08-18.

DeepSeek-V4-Pro-0813 is 12 months newer than Nemotron Nano 9B v2.

DeepSeek-V4-Pro-0813

Aug 13, 2026

1 weeks ago

12mo 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-V4-Pro-0813'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-V4-Pro-0813's cutoff date.

DeepSeek-V4-Pro-0813

—

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-V4-Pro-0813 and Nemotron Nano 9B v2 side-by-side, then vote on the output you prefer.

DeepSeek-V4-Pro-0813
✓ Preferred
Nemotron Nano 9B v2
Open in Playground

FAQ

Common questions about DeepSeek-V4-Pro-0813 vs Nemotron Nano 9B v2.

Which is better, DeepSeek-V4-Pro-0813 or Nemotron Nano 9B v2?

DeepSeek-V4-Pro-0813 (DeepSeek) and Nemotron Nano 9B v2 (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-V4-Pro-0813 compare to Nemotron Nano 9B v2 in benchmarks?

DeepSeek-V4-Pro-0813 scores Terminal-Bench 2.1: 87.9%, CyberGym: 83.3%, Toolathlon: 74.1%, DSBench-FullStack: 71.1%, DSBench-Hard: 67.2%. 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-V4-Pro-0813 and Nemotron Nano 9B v2?

DeepSeek-V4-Pro-0813 supports 1.0M 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-V4-Pro-0813 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-V4-Pro-0813 and Nemotron Nano 9B v2?

DeepSeek-V4-Pro-0813 is developed by DeepSeek and Nemotron Nano 9B v2 is developed by NVIDIA.

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