Nemotron 4 340B vs Amazon Titan Embed v2

Compare Nemotron 4 340B and Amazon Titan Embed v2: pricing, performance, context window, latency, and best use cases. Side-by-side comparison on XALEN.

Updated 2026-05-21 · By Abhishek Raj · Our methodology

All NVIDIA models All Amazon models What is an LLM API? Python Quickstart What is inference?
Feature Nemotron 4 340B Amazon Titan Embed v2
CategoryOpen SourceEmbedding
Parameters340B~200M
Context Window128K8K
Input Price$0.07/1M tokens$0.001/1M tokens
Output Price$0.12/1M tokensN/A/1M tokens
Latency~500ms~15ms

Choose Nemotron 4 340B when:

  • ✓ Data generation
  • ✓ Training data
  • ✓ Research
Key Strengths:

Synthetic data generation, Large scale, Good quality

Choose Amazon Titan Embed v2 when:

  • ✓ AWS RAG pipelines
  • ✓ Enterprise search
  • ✓ Document indexing
Key Strengths:

AWS native, Low cost, Reliable

Verdict: Nemotron 4 340B vs Amazon Titan Embed v2

For cost efficiency, Amazon Titan Embed v2 wins at $0.001/1M input tokens. For speed, Amazon Titan Embed v2 is faster at ~15ms. Nemotron 4 340B excels at Data generation while Amazon Titan Embed v2 is better for AWS RAG pipelines. Both are available on XALEN through a single API — try them in the Playground to see which fits your workload.

Detailed Analysis

Pricing Comparison

Nemotron 4 340B costs $0.07/1M input tokens and $0.12/1M output tokens. Amazon Titan Embed v2 costs $0.001 input and N/A output. Amazon Titan Embed v2 is 70.0x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.

Performance & Context

Nemotron 4 340B has a 128K context window with ~500ms latency. Amazon Titan Embed v2 offers 8K context at ~15ms. Nemotron 4 340B has the larger context window.

Best For

Nemotron 4 340B (Open Source) is optimized for: Data generation, Training data, Research. Amazon Titan Embed v2 (Embedding) works best for: AWS RAG pipelines, Enterprise search, Document indexing.

Try Both on XALEN

Both models are available through XALEN's OpenAI-compatible API. Switch between them by changing the model parameter:

from xalen import XALEN

client = XALEN(api_key="xln_test_YOUR_KEY")

# Use Nemotron 4 340B
response_a = client.chat.completions.create(
    model="nemotron-4-340b",
    messages=[{"role": "user", "content": "Your question here"}]
)

# Use Amazon Titan Embed v2
response_b = client.chat.completions.create(
    model="amazon-titan-embed-v2",
    messages=[{"role": "user", "content": "Your question here"}]
)

Start Building with XALEN

200+ AI models. One API. Pay-as-you-go.

Get API Key Try in Playground

Frequently Asked Questions

Which is better, Nemotron 4 340B or Amazon Titan Embed v2?

Nemotron 4 340B (Open Source, 340B) offers Synthetic data generation. Amazon Titan Embed v2 (Embedding, ~200M) offers AWS native. Choose Nemotron 4 340B for Data generation or Amazon Titan Embed v2 for AWS RAG pipelines.

How much does Nemotron 4 340B cost vs Amazon Titan Embed v2?

Nemotron 4 340B: $0.07/1M input, $0.12/1M output. Amazon Titan Embed v2: $0.001/1M input, N/A/1M output. Both available on XALEN with batch processing at 50% discount.

Can I use both models on XALEN?

Yes. XALEN provides 200+ models through a single OpenAI-compatible API. Switch between Nemotron 4 340B and Amazon Titan Embed v2 by changing the model parameter. No code changes needed.

Related Comparisons

Nemotron 4 340B vs Text Embedding 3 Large Nemotron 4 340B vs Gemma 3 27B Nemotron 4 340B vs Llama 4 Scout Nemotron 4 340B vs Llama 4 Maverick Nemotron 4 340B vs Llama 3.3 70B Nemotron 4 340B vs Llama 3.1 405B

Last updated: 2026-05-21. Pricing and specifications may change. Check pricing page for latest rates.