Vedika Pro Ultra vs Cohere Embed v4
Compare Vedika Pro Ultra 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 | Vedika Pro Ultra | Cohere Embed v4 |
|---|---|---|
| Category | Domain Specialist | Embedding |
| Parameters | 120B | ~400M |
| Context Window | 256K | 128K |
| Input Price | $0.12/1M tokens | $0.001/1M tokens |
| Output Price | $0.20/1M tokens | N/A/1M tokens |
| Latency | ~600ms | ~15ms |
Choose Vedika Pro Ultra when:
- ✓ Kundali matching reports
- ✓ Multi-chart analysis
- ✓ Enterprise platforms
256K context, Deep yoga reasoning, Multi-system comparison
Choose Cohere Embed v4 when:
- ✓ Long document RAG
- ✓ Multimodal search
- ✓ Large knowledge bases
128K context, Multimodal embedding, Matryoshka
Verdict: Vedika Pro Ultra 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. Vedika Pro Ultra excels at Kundali matching reports 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
Vedika Pro Ultra costs $0.12/1M input tokens and $0.20/1M output tokens. Cohere Embed v4 costs $0.001 input and N/A output. Cohere Embed v4 is 120.0x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.
Performance & Context
Vedika Pro Ultra has a 256K context window with ~600ms latency. Cohere Embed v4 offers 128K context at ~15ms. Vedika Pro Ultra has the larger context window.
Best For
Vedika Pro Ultra (Domain Specialist) is optimized for: Kundali matching reports, Multi-chart analysis, Enterprise platforms. 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 Vedika Pro Ultra
response_a = client.chat.completions.create(
model="vedika-pro-ultra",
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, Vedika Pro Ultra or Cohere Embed v4?
Vedika Pro Ultra (Domain Specialist, 120B) offers 256K context. Cohere Embed v4 (Embedding, ~400M) offers 128K context. Choose Vedika Pro Ultra for Kundali matching reports or Cohere Embed v4 for Long document RAG.
How much does Vedika Pro Ultra cost vs Cohere Embed v4?
Vedika Pro Ultra: $0.12/1M input, $0.20/1M 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 Vedika Pro Ultra 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.