Vikasit 3 vs Qwen3-235B-A22B
Qwen3-235B-A22B is a large MoE available at an aggressively low price; Vikasit 3 is a tuned generalist on the same API. Qwen3-235B is one of the cheapest large models you can call. Vikasit 3 trades a slightly higher price for tighter formatting and our support path.
Pricing comparison
| Metric | Vikasit 3 | Qwen3-235B-A22B |
|---|---|---|
| Input ($ / 1M tokens) | $0.21 | $0.09 |
| Output ($ / 1M tokens) | $0.30 | $0.10 |
| Blended (3:1 in:out) | $0.23 | $0.09 |
| OpenAI-compatible API | Yes | Yes |
Prices are per 1M tokens in USD. Blended cost assumes a 3:1 input-to-output token ratio, a common pattern for chat and generation workloads. Actual cost depends on your traffic. Vikasit 3 is available through the Vikasit Inference API.
Choose Vikasit 3 when
- You want a single supported default rather than picking models per task
- Output formatting and JSON reliability matter for your pipeline
- You prefer one vendor relationship across chat, code, and API
Choose Qwen3-235B-A22B when
- You want the lowest possible price for a large MoE model
- Your workload benefits from a big sparse model's breadth
- You are comfortable handling more output variance
Quick start with Vikasit 3
Call Vikasit 3 through the OpenAI-compatible Vikasit Inference API at https://api.vikasit.ai/v1. Change two lines in your existing OpenAI code — the base URL and your key.
from openai import OpenAI
client = OpenAI(
base_url="https://api.vikasit.ai/v1",
api_key="sk-vikasit-...",
)
resp = client.chat.completions.create(
model="vikasit-3",
messages=[{"role": "user", "content": "Hello!"}],
)
print(resp.choices[0].message.content)FAQ
Is Vikasit 3 cheaper than Qwen3-235B-A22B?
Per 1M tokens, Vikasit 3 costs $0.21 input / $0.30 output, while Qwen3-235B-A22B costs $0.09 input / $0.10 output. On output tokens — which usually dominate generation cost — Qwen3-235B-A22B is the cheaper option.
Can I call Vikasit 3 with the OpenAI SDK?
Yes. The Vikasit Inference API is OpenAI-compatible. Point any OpenAI SDK at https://api.vikasit.ai/v1 with your Vikasit API key and set the model id — chat completions, streaming, and tool calls all work.
Should I choose Vikasit 3 or Qwen3-235B-A22B?
Qwen3-235B-A22B is a large MoE available at an aggressively low price; Vikasit 3 is a tuned generalist on the same API. Qwen3-235B is one of the cheapest large models you can call. Vikasit 3 trades a slightly higher price for tighter formatting and our support path.
Start with Vikasit 3
Get an API key and 2M free tokens a day on Vikasit Nova. Pay-as-you-go, no minimums, OpenAI-compatible.