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Nemotron 4 340BvsGPT-o3

NVIDIA vs OpenAI — Side-by-side model comparison

GPT-o3 leads 4/5 categories

Head-to-Head Comparison

MetricNemotron 4 340BGPT-o3
Provider
NVIDIA
Arena Rank
#2
Context Window
4K
200K
Input Pricing
Free (open)/1M tokens
$2.00/1M tokens
Output Pricing
Free (open)/1M tokens
$8.00/1M tokens
Parameters
340B
Undisclosed
Open Source
Yes
No
Best For
Synthetic data generation, training pipelines
Advanced reasoning, agentic tasks, research
Release Date
Jun 14, 2024
Apr 16, 2025

Nemotron 4 340B

Nemotron 4 340B, developed by NVIDIA, is an open-source model with 340 billion parameters and a 4K token context window designed for synthetic data generation and AI training pipelines. The model excels at generating high-quality synthetic training data that can be used to train smaller, more efficient models. NVIDIA built Nemotron specifically to address the data bottleneck in AI development, where access to quality training data often limits model performance. The model demonstrates strong performance on general reasoning tasks while being particularly optimized for producing diverse, accurate synthetic datasets. Free and open-source, it can be deployed on NVIDIA GPU infrastructure. Nemotron 4 340B represents NVIDIA's strategy of contributing to the AI ecosystem beyond hardware, providing tools that make their GPU platforms more valuable for AI development workflows.

GPT-o3

GPT-o3 is OpenAI's most advanced reasoning model, succeeding o1 as the frontier of deliberative AI. It uses an enhanced chain-of-thought approach where the model spends more compute time 'thinking' before responding, dramatically improving performance on complex STEM, mathematical, and logical reasoning tasks. With a 200K token context window and the ability to use tools during reasoning, o3 represents a significant leap in AI problem-solving capabilities. It achieved state-of-the-art results on the ARC-AGI benchmark, demonstrating near-human performance on novel reasoning challenges. The model is particularly strong at multi-step mathematical proofs, complex code debugging, and scientific analysis where careful step-by-step reasoning is essential. Originally priced at a premium, an 80% price reduction in June 2025 made o3 accessible to a much broader range of developers and applications.

View OpenAI profile →

Key Differences: Nemotron 4 340B vs GPT-o3

1

GPT-o3 supports a larger context window (200K), allowing it to process longer documents in a single request.

2

Nemotron 4 340B is open-source (free to self-host and fine-tune) while GPT-o3 is proprietary (API-only access).

N

When to use Nemotron 4 340B

  • +You need to self-host or fine-tune the model
  • +Your use case involves synthetic data generation, training pipelines
View full Nemotron 4 340B specs →
G

When to use GPT-o3

  • +You need to process long documents (200K context)
  • +You prefer a managed API without infrastructure overhead
  • +Your use case involves advanced reasoning, agentic tasks, research
View full GPT-o3 specs →

The Verdict

GPT-o3 wins our head-to-head comparison with 4 out of 5 category wins. It's the stronger choice for advanced reasoning, agentic tasks, research, though Nemotron 4 340B holds an edge in synthetic data generation, training pipelines.

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

Frequently Asked Questions

Which is better, Nemotron 4 340B or GPT-o3?
In our head-to-head comparison, GPT-o3 leads in 4 out of 5 categories (arena rank, context window, input pricing, output pricing, and parameters). GPT-o3 excels at advanced reasoning, agentic tasks, research, 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 GPT-o3?
Nemotron 4 340B charges Free (open) per 1M input tokens and Free (open) per 1M output tokens. GPT-o3 charges $2.00 per 1M input tokens and $8.00 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 GPT-o3?
Nemotron 4 340B supports a 4K token context window, while GPT-o3 supports 200K tokens. GPT-o3 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 GPT-o3 for free?
Nemotron 4 340B is a paid API model starting at Free (open) per 1M input tokens. GPT-o3 is a paid API model starting at $2.00 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 GPT-o3?
Nemotron 4 340B's arena rank is not yet available, while GPT-o3 holds rank #2. Note that benchmarks don't capture every use case — we recommend testing both models on your specific tasks.
Is Nemotron 4 340B or GPT-o3 better for coding?
Nemotron 4 340B's primary strength is synthetic data generation, training pipelines. GPT-o3's primary strength is advanced reasoning, agentic tasks, research. For coding specifically, arena rank and code-specific benchmarks are the best indicators of performance.