Mistral Embed vs WizardLM 2 8x22B

Compare Mistral Embed and WizardLM 2 8x22B: 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 Microsoft models What is an LLM API? Python Quickstart What is inference?
Feature Mistral Embed WizardLM 2 8x22B
CategoryEmbeddingOpen Source
Parameters~200M176B (22B active)
Context Window8K65K
Input Price$0.001/1M tokens$0.04/1M tokens
Output PriceN/A/1M tokens$0.08/1M tokens
Latency~15ms~350ms

Choose Mistral Embed when:

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

Fast, Low cost, Good quality

Choose WizardLM 2 8x22B when:

  • ✓ Complex instructions
  • ✓ Math tasks
  • ✓ Structured output
Key Strengths:

Strong instruction following, Good math, MoE efficient

Verdict: Mistral Embed vs WizardLM 2 8x22B

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 WizardLM 2 8x22B is better for Complex instructions. 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. WizardLM 2 8x22B costs $0.04 input and $0.08 output. Mistral Embed is 40.0x 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. WizardLM 2 8x22B offers 65K context at ~350ms. WizardLM 2 8x22B has the larger context window.

Best For

Mistral Embed (Embedding) is optimized for: RAG pipelines, Semantic search, Document clustering. WizardLM 2 8x22B (Open Source) works best for: Complex instructions, Math tasks, Structured output.

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 WizardLM 2 8x22B
response_b = client.chat.completions.create(
    model="wizardlm-2-8x22b",
    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 WizardLM 2 8x22B?

Mistral Embed (Embedding, ~200M) offers Fast. WizardLM 2 8x22B (Open Source, 176B (22B active)) offers Strong instruction following. Choose Mistral Embed for RAG pipelines or WizardLM 2 8x22B for Complex instructions.

How much does Mistral Embed cost vs WizardLM 2 8x22B?

Mistral Embed: $0.001/1M input, N/A/1M output. WizardLM 2 8x22B: $0.04/1M input, $0.08/1M 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 WizardLM 2 8x22B by changing the model parameter. No code changes needed.

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