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DeepSeek-V2.5 vs Llama 3.1 Nemotron 70B Instruct

DeepSeek-V2.5 leads the LLM Stats Score 8.1 to 0.4.

DeepSeek · NVIDIA · Updated for 2026

Which is better?

DeepSeek-V2.5 leads the overall LLM Stats Score 8.1 to 0.4, ranking #287 overall.

In the 3 individual benchmarks reported for both models, DeepSeek-V2.5 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-V2.5

  • overall performance matters — it scores 8.1 and ranks #287 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

Choose Llama 3.1 Nemotron 70B Instruct

  • you want the most recent training data — it shipped Oct 2024

At a glance

The differences that matter most.

Core performance indexes
8.1
#287
0.4
#330
8.2
#283
0.1
#323
Cost, coverage & limits
Benchmark wins
3 of 3
0 of 3
Input price
$0.14 / M
— / M
Output price
$0.28 / M
— / M
Context window
8,192

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V2.5
Llama 3.1 Nemotron 70B Instruct
14.0#223
7.0#267
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

15 reported for DeepSeek-V2.5 · 11 for Llama 3.1 Nemotron 70B Instruct

3 shared

DeepSeek-V2.5 outperforms in 3 benchmarks (GSM8k, MMLU, MT-Bench), while Llama 3.1 Nemotron 70B Instruct is better at 0 benchmarks.

DeepSeek-V2.5 significantly outperforms across most benchmarks.

Wed Sep 23 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

166.0B diff

DeepSeek-V2.5 has 166.0B more parameters than Llama 3.1 Nemotron 70B Instruct, making it 237.1% larger.

DeepSeek
DeepSeek-V2.5
236.0Bparameters
NVIDIA
Llama 3.1 Nemotron 70B Instruct
70.0Bparameters
236.0B
DeepSeek-V2.5
70.0B
Llama 3.1 Nemotron 70B Instruct

Context Window

Maximum input and output token capacity

Only DeepSeek-V2.5 specifies input context (8,192 tokens). Only DeepSeek-V2.5 specifies output context (8,192 tokens).

DeepSeek
DeepSeek-V2.5
Input8,192 tokens
Output8,192 tokens
NVIDIA
Llama 3.1 Nemotron 70B Instruct
Input- tokens
Output- tokens
Wed Sep 23 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V2.5 is licensed under deepseek, 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-V2.5

deepseek

Open weights

Llama 3.1 Nemotron 70B Instruct

Llama 3.1 Community License

Open weights

Release Timeline

When each model was launched

DeepSeek-V2.5 was released on 2024-05-08, while Llama 3.1 Nemotron 70B Instruct was released on 2024-10-01.

Llama 3.1 Nemotron 70B Instruct is 5 months newer than DeepSeek-V2.5.

DeepSeek-V2.5

May 8, 2024

2.4 years ago

Llama 3.1 Nemotron 70B Instruct

Oct 1, 2024

2.0 years ago

4mo newer

Knowledge Cutoff

When training data ends

Llama 3.1 Nemotron 70B Instruct has a documented knowledge cutoff of 2023-12-01, while DeepSeek-V2.5'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-V2.5's cutoff date.

DeepSeek-V2.5

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-V2.5 and Llama 3.1 Nemotron 70B Instruct side-by-side, then vote on the output you prefer.

DeepSeek-V2.5
✓ Preferred
Llama 3.1 Nemotron 70B Instruct
Open in Playground

FAQ

Common questions about DeepSeek-V2.5 vs Llama 3.1 Nemotron 70B Instruct.

Which is better, DeepSeek-V2.5 or Llama 3.1 Nemotron 70B Instruct?

DeepSeek-V2.5 leads the LLM Stats Score 8.1 to 0.4. DeepSeek-V2.5 is made by DeepSeek and Llama 3.1 Nemotron 70B Instruct 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-V2.5 compare to Llama 3.1 Nemotron 70B Instruct in benchmarks?

DeepSeek-V2.5 scores GSM8k: 95.1%, MT-Bench: 90.2%, HumanEval: 89.0%, BBH: 84.3%, AlignBench: 80.4%. 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-V2.5 and Llama 3.1 Nemotron 70B Instruct?

DeepSeek-V2.5 supports 8K 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-V2.5 and Llama 3.1 Nemotron 70B Instruct?

Key differences include LLM Stats Score (8.1 vs 0.4), licensing (deepseek vs Llama 3.1 Community License). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V2.5 and Llama 3.1 Nemotron 70B Instruct?

DeepSeek-V2.5 is developed by DeepSeek and Llama 3.1 Nemotron 70B Instruct is developed by NVIDIA.