Vedika Pro Ultra vs QwQ 32B
Compare Vedika Pro Ultra and QwQ 32B: 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 | QwQ 32B |
|---|---|---|
| Category | Domain Specialist | Reasoning |
| Parameters | 120B | 32B |
| Context Window | 256K | 128K |
| Input Price | $0.12/1M tokens | $0.03/1M tokens |
| Output Price | $0.20/1M tokens | $0.06/1M tokens |
| Latency | ~600ms | ~400ms |
Choose Vedika Pro Ultra when:
- ✓ Kundali matching reports
- ✓ Multi-chart analysis
- ✓ Enterprise platforms
256K context, Deep yoga reasoning, Multi-system comparison
Choose QwQ 32B when:
- ✓ Math reasoning
- ✓ Logic tasks
- ✓ Analysis
Strong reasoning, Compact for reasoning, Cost-efficient
Verdict: Vedika Pro Ultra vs QwQ 32B
For cost efficiency, QwQ 32B wins at $0.03/1M input tokens. For speed, QwQ 32B is faster at ~400ms. Vedika Pro Ultra excels at Kundali matching reports while QwQ 32B is better for Math reasoning. 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. QwQ 32B costs $0.03 input and $0.06 output. QwQ 32B is 4.0x 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. QwQ 32B offers 128K context at ~400ms. 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. QwQ 32B (Reasoning) works best for: Math reasoning, Logic tasks, Analysis.
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 QwQ 32B
response_b = client.chat.completions.create(
model="qwq-32b",
messages=[{"role": "user", "content": "Your question here"}]
)Frequently Asked Questions
Which is better, Vedika Pro Ultra or QwQ 32B?
Vedika Pro Ultra (Domain Specialist, 120B) offers 256K context. QwQ 32B (Reasoning, 32B) offers Strong reasoning. Choose Vedika Pro Ultra for Kundali matching reports or QwQ 32B for Math reasoning.
How much does Vedika Pro Ultra cost vs QwQ 32B?
Vedika Pro Ultra: $0.12/1M input, $0.20/1M output. QwQ 32B: $0.03/1M input, $0.06/1M 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 QwQ 32B 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.