Mistral Embed vs Nemotron 4 340B

Compare Mistral Embed and Nemotron 4 340B: 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 Mistral models All NVIDIA models What is an LLM API? Python Quickstart What is inference?
Feature Mistral Embed Nemotron 4 340B
CategoryEmbeddingOpen Source
Parameters~200M340B
Context Window8K128K
Input Price$0.001/1M tokens$0.07/1M tokens
Output PriceN/A/1M tokens$0.12/1M tokens
Latency~15ms~500ms

Choose Mistral Embed when:

  • ✓ RAG pipelines
  • ✓ Semantic search
  • ✓ Document clustering
Key Strengths:

Fast, Low cost, Good quality

Choose Nemotron 4 340B when:

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

Synthetic data generation, Large scale, Good quality

Verdict: Mistral Embed vs Nemotron 4 340B

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

Detailed Analysis

Pricing Comparison

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

Performance & Context

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

Best For

Mistral Embed (Embedding) is optimized for: RAG pipelines, Semantic search, Document clustering. Nemotron 4 340B (Open Source) works best for: Data generation, Training data, Research.

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 Mistral Embed
response_a = client.chat.completions.create(
    model="mistral-embed",
    messages=[{"role": "user", "content": "Your question here"}]
)

# Use Nemotron 4 340B
response_b = client.chat.completions.create(
    model="nemotron-4-340b",
    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, Mistral Embed or Nemotron 4 340B?

Mistral Embed (Embedding, ~200M) offers Fast. Nemotron 4 340B (Open Source, 340B) offers Synthetic data generation. Choose Mistral Embed for RAG pipelines or Nemotron 4 340B for Data generation.

How much does Mistral Embed cost vs Nemotron 4 340B?

Mistral Embed: $0.001/1M input, N/A/1M output. Nemotron 4 340B: $0.07/1M input, $0.12/1M 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 Mistral Embed and Nemotron 4 340B by changing the model parameter. No code changes needed.

Related Comparisons

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

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