Vedika Pro Ultra vs Cohere Rerank 3.5
Compare Vedika Pro Ultra and Cohere Rerank 3.5: 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 Rerank 3.5 |
|---|---|---|
| Category | Domain Specialist | Reranking |
| Parameters | 120B | ~600M |
| Context Window | 256K | 4K |
| Input Price | $0.12/1M tokens | $0.002/search/1M tokens |
| Output Price | $0.20/1M tokens | N/A/1M tokens |
| Latency | ~600ms | ~25ms |
Choose Vedika Pro Ultra when:
- ✓ Kundali matching reports
- ✓ Multi-chart analysis
- ✓ Enterprise platforms
256K context, Deep yoga reasoning, Multi-system comparison
Choose Cohere Rerank 3.5 when:
- ✓ Search reranking
- ✓ RAG improvement
- ✓ Result quality
Higher quality, Multilingual, Fast
Verdict: Vedika Pro Ultra vs Cohere Rerank 3.5
For cost efficiency, Cohere Rerank 3.5 wins at $0.002/search/1M input tokens. For speed, Cohere Rerank 3.5 is faster at ~25ms. Vedika Pro Ultra excels at Kundali matching reports while Cohere Rerank 3.5 is better for Search reranking. 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 Rerank 3.5 costs $0.002/search input and N/A output. Cohere Rerank 3.5 is 60.0x 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 Rerank 3.5 offers 4K context at ~25ms. 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 Rerank 3.5 (Reranking) works best for: Search reranking, RAG improvement, Result quality.
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 Rerank 3.5
response_b = client.chat.completions.create(
model="rerank-v3-5",
messages=[{"role": "user", "content": "Your question here"}]
)Frequently Asked Questions
Which is better, Vedika Pro Ultra or Cohere Rerank 3.5?
Vedika Pro Ultra (Domain Specialist, 120B) offers 256K context. Cohere Rerank 3.5 (Reranking, ~600M) offers Higher quality. Choose Vedika Pro Ultra for Kundali matching reports or Cohere Rerank 3.5 for Search reranking.
How much does Vedika Pro Ultra cost vs Cohere Rerank 3.5?
Vedika Pro Ultra: $0.12/1M input, $0.20/1M output. Cohere Rerank 3.5: $0.002/search/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 Rerank 3.5 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.