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Nemotron 4 340BvsDeepSeek R1

NVIDIA vs DeepSeek — Side-by-side model comparison

DeepSeek R1 leads 5/5 categories

Head-to-Head Comparison

MetricNemotron 4 340BDeepSeek R1
Provider
Arena Rank
#3
Context Window
4K
128K
Input Pricing
Free (open)/1M tokens
$0.55/1M tokens
Output Pricing
Free (open)/1M tokens
$2.19/1M tokens
Parameters
340B
671B (37B active)
Open Source
Yes
Yes
Best For
Synthetic data generation, training pipelines
Complex reasoning, math, science, coding
Release Date
Jun 14, 2024
Jan 20, 2025

Nemotron 4 340B

Nemotron 4 340B is NVIDIA's large language model designed specifically for generating high-quality synthetic training data. With 340 billion parameters, it can produce diverse, accurate training examples for fine-tuning smaller models. NVIDIA released it to accelerate the development of custom AI models, recognizing that high-quality training data is often the biggest bottleneck in model development.

View NVIDIA profile →

DeepSeek R1

DeepSeek R1 is DeepSeek's reasoning model that rivals OpenAI's o1 at a fraction of the cost. Using reinforcement learning to develop chain-of-thought reasoning capabilities, R1 excels at complex mathematics, scientific reasoning, and coding challenges. Its open-source release sent shockwaves through the AI industry, demonstrating that advanced reasoning capabilities could be replicated outside of major Western labs and at dramatically lower training costs.

View DeepSeek profile →

Key Differences: Nemotron 4 340B vs DeepSeek R1

1

DeepSeek R1 supports a larger context window (128K), allowing it to process longer documents in a single request.

2

Nemotron 4 340B has 340B parameters vs DeepSeek R1's 671B (37B active), which affects inference speed and capability.

N

When to use Nemotron 4 340B

  • +Your use case involves synthetic data generation, training pipelines
View full Nemotron 4 340B specs →
D

When to use DeepSeek R1

  • +You need to process long documents (128K context)
  • +Your use case involves complex reasoning, math, science, coding
View full DeepSeek R1 specs →

The Verdict

DeepSeek R1 wins our head-to-head comparison with 5 out of 5 category wins. It's the stronger choice for complex reasoning, math, science, coding, though Nemotron 4 340B holds an edge in synthetic data generation, training pipelines.

Last compared: March 2026 · Data sourced from public benchmarks and official pricing pages

Frequently Asked Questions

Which is better, Nemotron 4 340B or DeepSeek R1?
In our head-to-head comparison, DeepSeek R1 leads in 5 out of 5 categories (arena rank, context window, input pricing, output pricing, and parameters). DeepSeek R1 excels at complex reasoning, math, science, coding, while Nemotron 4 340B is better suited for synthetic data generation, training pipelines. The best choice depends on your specific requirements, budget, and use case.
How does Nemotron 4 340B pricing compare to DeepSeek R1?
Nemotron 4 340B charges Free (open) per 1M input tokens and Free (open) per 1M output tokens. DeepSeek R1 charges $0.55 per 1M input tokens and $2.19 per 1M output tokens. For high-volume production workloads, the pricing difference can significantly impact total cost of ownership.
What is the context window difference between Nemotron 4 340B and DeepSeek R1?
Nemotron 4 340B supports a 4K token context window, while DeepSeek R1 supports 128K tokens. DeepSeek R1 can process longer documents, codebases, and conversations in a single request. Context window size matters most for tasks involving long documents, large codebases, or extended conversations.
Can I use Nemotron 4 340B or DeepSeek R1 for free?
Nemotron 4 340B is a paid API model starting at Free (open) per 1M input tokens. DeepSeek R1 is a paid API model starting at $0.55 per 1M input tokens. Open-source models can be self-hosted for free but require your own GPU infrastructure.
Which model has better benchmarks, Nemotron 4 340B or DeepSeek R1?
Nemotron 4 340B's arena rank is not yet available, while DeepSeek R1 holds rank #3. Note that benchmarks don't capture every use case — we recommend testing both models on your specific tasks.
Is Nemotron 4 340B or DeepSeek R1 better for coding?
Nemotron 4 340B's primary strength is synthetic data generation, training pipelines. DeepSeek R1 is specifically optimized for coding tasks. For coding specifically, arena rank and code-specific benchmarks are the best indicators of performance.