Model Comparison

DeepSeek-V3 vs Llama-3.3 Nemotron Super 49B v1Which is better in 2026?

Llama-3.3 Nemotron Super 49B v1 significantly outperforms across most benchmarks.

Verdict: DeepSeek-V3 vs Llama-3.3 Nemotron Super 49B v1 — which is better?

DeepSeek-V3 (by DeepSeek) and Llama-3.3 Nemotron Super 49B v1 (by NVIDIA) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

DeepSeek-V3 outperforms in 0 benchmarks, while Llama-3.3 Nemotron Super 49B v1 is better at 2 benchmarks (GPQA, MATH-500). Llama-3.3 Nemotron Super 49B v1 significantly outperforms across most benchmarks.

Choose DeepSeek-V3 if…

  • you want predictable pricing at $0.27/M input and $1.10/M output

Choose Llama-3.3 Nemotron Super 49B v1 if…

  • you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
  • you want the most recent training data — it shipped Mar 2025
Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek-V3 outperforms in 0 benchmarks, while Llama-3.3 Nemotron Super 49B v1 is better at 2 benchmarks (GPQA, MATH-500).

Llama-3.3 Nemotron Super 49B v1 significantly outperforms across most benchmarks.

Sat Jun 06 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

621.1B diff

DeepSeek-V3 has 621.1B more parameters than Llama-3.3 Nemotron Super 49B v1, making it 1244.7% larger.

DeepSeek
DeepSeek-V3
671.0Bparameters
NVIDIA
Llama-3.3 Nemotron Super 49B v1
49.9Bparameters
671.0B
DeepSeek-V3
49.9B
Llama-3.3 Nemotron Super 49B v1

Context Window

Maximum input and output token capacity

Only DeepSeek-V3 specifies input context (131,072 tokens). Only DeepSeek-V3 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
NVIDIA
Llama-3.3 Nemotron Super 49B v1
Input- tokens
Output- tokens
Sat Jun 06 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), while Llama-3.3 Nemotron Super 49B v1 uses Llama 3.1 Community License.

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

DeepSeek-V3

MIT + Model License (Commercial use allowed)

Open weights

Llama-3.3 Nemotron Super 49B v1

Llama 3.1 Community License

Open weights

Release Timeline

When each model was launched

DeepSeek-V3 was released on 2024-12-25, while Llama-3.3 Nemotron Super 49B v1 was released on 2025-03-18.

Llama-3.3 Nemotron Super 49B v1 is 3 months newer than DeepSeek-V3.

DeepSeek-V3

Dec 25, 2024

1.4 years ago

Llama-3.3 Nemotron Super 49B v1

Mar 18, 2025

1.2 years ago

2mo newer

Knowledge Cutoff

When training data ends

Llama-3.3 Nemotron Super 49B v1 has a documented knowledge cutoff of 2023-12-31, while DeepSeek-V3's cutoff date is not specified.

We can confirm Llama-3.3 Nemotron Super 49B v1's training data extends to 2023-12-31, but cannot make a direct comparison without DeepSeek-V3's cutoff date.

DeepSeek-V3

—

Llama-3.3 Nemotron Super 49B v1

Dec 2023

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
NVIDIA
Higher GPQA score (66.7% vs 59.1%)
Higher MATH-500 score (96.6% vs 90.2%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3
NVIDIA
Llama-3.3 Nemotron Super 49B v1

FAQ

Common questions about DeepSeek-V3 vs Llama-3.3 Nemotron Super 49B v1.

Which is better, DeepSeek-V3 or Llama-3.3 Nemotron Super 49B v1?

Llama-3.3 Nemotron Super 49B v1 significantly outperforms across most benchmarks. DeepSeek-V3 is made by DeepSeek and Llama-3.3 Nemotron Super 49B v1 is made by NVIDIA. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-V3 compare to Llama-3.3 Nemotron Super 49B v1 in benchmarks?

DeepSeek-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%. Llama-3.3 Nemotron Super 49B v1 scores MATH-500: 96.6%, MT-Bench: 91.7%, MBPP: 91.3%, Arena Hard: 88.3%, BFCL v2: 73.7%.

What are the context window sizes for DeepSeek-V3 and Llama-3.3 Nemotron Super 49B v1?

DeepSeek-V3 supports 131K tokens and Llama-3.3 Nemotron Super 49B v1 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 and Llama-3.3 Nemotron Super 49B v1?

Key differences include licensing (MIT + Model License (Commercial use allowed) vs Llama 3.1 Community License). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3 and Llama-3.3 Nemotron Super 49B v1?

DeepSeek-V3 is developed by DeepSeek and Llama-3.3 Nemotron Super 49B v1 is developed by NVIDIA.

Related comparisons