InternLM 2.5 20B vs Cohere Embed v4

Compare InternLM 2.5 20B 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

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Feature InternLM 2.5 20B Cohere Embed v4
CategoryOpen SourceEmbedding
Parameters20B~400M
Context Window256K128K
Input Price$0.02/1M tokens$0.001/1M tokens
Output Price$0.04/1M tokensN/A/1M tokens
Latency~180ms~15ms

Choose InternLM 2.5 20B when:

  • ✓ Long context tasks
  • ✓ Research
  • ✓ Multilingual
Key Strengths:

256K context, Strong reasoning, Good multilingual

Choose Cohere Embed v4 when:

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

128K context, Multimodal embedding, Matryoshka

Verdict: InternLM 2.5 20B 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. InternLM 2.5 20B excels at Long context tasks 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

InternLM 2.5 20B costs $0.02/1M input tokens and $0.04/1M output tokens. Cohere Embed v4 costs $0.001 input and N/A output. Cohere Embed v4 is 20.0x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.

Performance & Context

InternLM 2.5 20B has a 256K context window with ~180ms latency. Cohere Embed v4 offers 128K context at ~15ms. InternLM 2.5 20B has the larger context window.

Best For

InternLM 2.5 20B (Open Source) is optimized for: Long context tasks, Research, Multilingual. 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 InternLM 2.5 20B
response_a = client.chat.completions.create(
    model="internlm-2-5-20b",
    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"}]
)

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Get API Key Try in Playground

Frequently Asked Questions

Which is better, InternLM 2.5 20B or Cohere Embed v4?

InternLM 2.5 20B (Open Source, 20B) offers 256K context. Cohere Embed v4 (Embedding, ~400M) offers 128K context. Choose InternLM 2.5 20B for Long context tasks or Cohere Embed v4 for Long document RAG.

How much does InternLM 2.5 20B cost vs Cohere Embed v4?

InternLM 2.5 20B: $0.02/1M input, $0.04/1M 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 InternLM 2.5 20B 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.