MiniMax-01 vs Cohere Embed v4

Compare MiniMax-01 and Cohere Embed v4: 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 MiniMax models All Cohere models What is an LLM API? Python Quickstart What is inference?
Feature MiniMax-01 Cohere Embed v4
CategoryOpen SourceEmbedding
Parameters456B (46B active)~400M
Context Window4M128K
Input Price$0.05/1M tokens$0.001/1M tokens
Output Price$0.10/1M tokensN/A/1M tokens
Latency~400ms~15ms

Choose MiniMax-01 when:

  • ✓ Full library processing
  • ✓ Massive document analysis
  • ✓ Historical research
Key Strengths:

4M context, MoE efficiency, Strong multilingual

Choose Cohere Embed v4 when:

  • ✓ Long document RAG
  • ✓ Multimodal search
  • ✓ Large knowledge bases
Key Strengths:

128K context, Multimodal embedding, Matryoshka

Verdict: MiniMax-01 vs Cohere Embed v4

For cost efficiency, Cohere Embed v4 wins at $0.001/1M input tokens. For speed, Cohere Embed v4 is faster at ~15ms. MiniMax-01 excels at Full library processing while Cohere Embed v4 is better for Long document RAG. Both are available on XALEN through a single API — try them in the Playground to see which fits your workload.

Detailed Analysis

Pricing Comparison

MiniMax-01 costs $0.05/1M input tokens and $0.10/1M output tokens. Cohere Embed v4 costs $0.001 input and N/A output. Cohere Embed v4 is 50.0x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.

Performance & Context

MiniMax-01 has a 4M context window with ~400ms latency. Cohere Embed v4 offers 128K context at ~15ms. MiniMax-01 has the larger context window.

Best For

MiniMax-01 (Open Source) is optimized for: Full library processing, Massive document analysis, Historical research. Cohere Embed v4 (Embedding) works best for: Long document RAG, Multimodal search, Large knowledge bases.

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 MiniMax-01
response_a = client.chat.completions.create(
    model="minimax-01",
    messages=[{"role": "user", "content": "Your question here"}]
)

# Use Cohere Embed v4
response_b = client.chat.completions.create(
    model="embed-v4",
    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, MiniMax-01 or Cohere Embed v4?

MiniMax-01 (Open Source, 456B (46B active)) offers 4M context. Cohere Embed v4 (Embedding, ~400M) offers 128K context. Choose MiniMax-01 for Full library processing or Cohere Embed v4 for Long document RAG.

How much does MiniMax-01 cost vs Cohere Embed v4?

MiniMax-01: $0.05/1M input, $0.10/1M output. Cohere Embed v4: $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 MiniMax-01 and Cohere Embed v4 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.