Arctic Large vs Cohere Embed v4
Compare Arctic Large 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
| Feature | Arctic Large | Cohere Embed v4 |
|---|---|---|
| Category | Enterprise | Embedding |
| Parameters | 480B (17B active) | ~400M |
| Context Window | 128K | 128K |
| Input Price | $0.06/1M tokens | $0.001/1M tokens |
| Output Price | $0.10/1M tokens | N/A/1M tokens |
| Latency | ~400ms | ~15ms |
Choose Arctic Large when:
- ✓ Data analysis
- ✓ SQL generation
- ✓ Business intelligence
Strong SQL, Data analysis, Enterprise features
Choose Cohere Embed v4 when:
- ✓ Long document RAG
- ✓ Multimodal search
- ✓ Large knowledge bases
128K context, Multimodal embedding, Matryoshka
Verdict: Arctic Large 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. Arctic Large excels at Data analysis 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
Arctic Large costs $0.06/1M input tokens and $0.10/1M output tokens. Cohere Embed v4 costs $0.001 input and N/A output. Cohere Embed v4 is 60.0x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.
Performance & Context
Arctic Large has a 128K context window with ~400ms latency. Cohere Embed v4 offers 128K context at ~15ms. Both have identical context windows.
Best For
Arctic Large (Enterprise) is optimized for: Data analysis, SQL generation, Business intelligence. 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 Arctic Large
response_a = client.chat.completions.create(
model="arctic-large",
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"}]
)Frequently Asked Questions
Which is better, Arctic Large or Cohere Embed v4?
Arctic Large (Enterprise, 480B (17B active)) offers Strong SQL. Cohere Embed v4 (Embedding, ~400M) offers 128K context. Choose Arctic Large for Data analysis or Cohere Embed v4 for Long document RAG.
How much does Arctic Large cost vs Cohere Embed v4?
Arctic Large: $0.06/1M 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 Arctic Large 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.