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DeepSeek-V3.2 (Non-thinking) vs Llama 3.1 Nemotron 70B Instruct

Comparing DeepSeek-V3.2 (Non-thinking) and Llama 3.1 Nemotron 70B Instruct across benchmarks, pricing, and capabilities.

DeepSeek · NVIDIA · Updated for 2026

Which is better?

DeepSeek-V3.2 (Non-thinking) and Llama 3.1 Nemotron 70B Instruct 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-V3.2 (Non-thinking)

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

Choose Llama 3.1 Nemotron 70B Instruct

  • you are already invested in the NVIDIA ecosystem

At a glance

The differences that matter most.

DeepSeek-V3.2 (Non-thinking)
DeepSeek
Llama 3.1 Nemotron 70B Instruct
NVIDIA
Benchmark wins
—
—
Input price
$0.28 / M
— / M
Output price
$0.42 / M
— / M
Context window
131,072
—
Released
Dec 2025
Oct 2024
License
MIT
Llama 3.1 Community License

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V3.2 (Non-thinking) and Llama 3.1 Nemotron 70B Instructdon'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

615.0B diff

DeepSeek-V3.2 (Non-thinking) has 615.0B more parameters than Llama 3.1 Nemotron 70B Instruct, making it 878.6% larger.

DeepSeek
DeepSeek-V3.2 (Non-thinking)
685.0Bparameters
NVIDIA
Llama 3.1 Nemotron 70B Instruct
70.0Bparameters
685.0B
DeepSeek-V3.2 (Non-thinking)
70.0B
Llama 3.1 Nemotron 70B Instruct

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 70B Instruct
Input- tokens
Output- tokens
Wed Aug 26 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3.2 (Non-thinking) is licensed under MIT, while Llama 3.1 Nemotron 70B Instruct 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 70B Instruct

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 70B Instruct was released on 2024-10-01.

DeepSeek-V3.2 (Non-thinking) is 14 months newer than Llama 3.1 Nemotron 70B Instruct.

DeepSeek-V3.2 (Non-thinking)

Dec 1, 2025

8 months ago

1.2yr newer
Llama 3.1 Nemotron 70B Instruct

Oct 1, 2024

1.9 years ago

Knowledge Cutoff

When training data ends

Llama 3.1 Nemotron 70B Instruct has a documented knowledge cutoff of 2023-12-01, while DeepSeek-V3.2 (Non-thinking)'s cutoff date is not specified.

We can confirm Llama 3.1 Nemotron 70B Instruct's training data extends to 2023-12-01, but cannot make a direct comparison without DeepSeek-V3.2 (Non-thinking)'s cutoff date.

DeepSeek-V3.2 (Non-thinking)

—

Llama 3.1 Nemotron 70B Instruct

Dec 2023

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against DeepSeek-V3.2 (Non-thinking) and Llama 3.1 Nemotron 70B Instruct side-by-side, then vote on the output you prefer.

DeepSeek-V3.2 (Non-thinking)
✓ Preferred
Llama 3.1 Nemotron 70B Instruct
Open in Playground

FAQ

Common questions about DeepSeek-V3.2 (Non-thinking) vs Llama 3.1 Nemotron 70B Instruct.

Which is better, DeepSeek-V3.2 (Non-thinking) or Llama 3.1 Nemotron 70B Instruct?

DeepSeek-V3.2 (Non-thinking) (DeepSeek) and Llama 3.1 Nemotron 70B Instruct (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 70B Instruct in benchmarks?

Llama 3.1 Nemotron 70B Instruct scores GSM8k: 91.4%, HellaSwag: 85.6%, Winogrande: 84.5%, GSM8K Chat: 81.9%, MMLU Chat: 80.6%.

What are the context window sizes for DeepSeek-V3.2 (Non-thinking) and Llama 3.1 Nemotron 70B Instruct?

DeepSeek-V3.2 (Non-thinking) supports 131K tokens and Llama 3.1 Nemotron 70B Instruct 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 70B Instruct?

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 70B Instruct?

DeepSeek-V3.2 (Non-thinking) is developed by DeepSeek and Llama 3.1 Nemotron 70B Instruct is developed by NVIDIA.

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