Qwen3 30B A3B APIOPEN WEIGHTS131K context
Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks.
Pricing & specs mirror our live pricing as of July 2026 (pay-as-you-go).
What is Qwen3 30B A3B?
Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. Its unique...
Qwen3 30B A3B on the release timeline
Qwen3 30B A3B pricing
Pay-as-you-go on DataLLM Lab — these are our live list prices (identical to the pricing page):
| Model | Input / 1M | Output / 1M | Cache read | Cache write | Context |
|---|---|---|---|---|---|
| Qwen3 30B A3B | $0.12 | $0.50 | — | — | 131K |
On output price, Qwen3 30B A3B is cheaper than 75% of the 309 models in the catalog. It is served by 3 providers; the best combined rate at our last snapshot was ≈ $0.12 / $0.50 per 1M via DeepInfra, with upstream quantizations fp8.
What Qwen3 30B A3B costs per month
| Workload | Tokens in / out (monthly) | Est. cost |
|---|---|---|
| Support chatbot | 40M / 12M | $10.80 |
| RAG / knowledge base | 200M / 20M | $34.00 |
| Coding agent | 80M / 25M | $22.10 |
| Batch extraction | 150M / 8M | $22.00 |
| Content generation | 20M / 40M | $22.40 |
Estimate your own monthly cost
Cost = input price × input volume + output price × output volume. The same five workloads run on every model page, so any two compare directly.
Qwen3 30B A3B vs alternatives
| Model | Input / 1M | Output / 1M | Context | Our test |
|---|---|---|---|---|
| Qwen3 30B A3B | $0.12 | $0.50 | 131K | — |
| Qwen3 VL 30B A3B Instruct | $0.13 | $0.52 | 262K | — |
| Qwen3 8B | $0.12 | $0.45 | 131K | — |
| Qwen3 VL 8B Instruct | $0.12 | $0.45 | 256K | — |
| Olmo 3 32B Think | $0.15 | $0.50 | 66K | — |
| Cydonia 24B V4.1 | $0.30 | $0.50 | 131K | — |
Specs
| Model ID | qwen/qwen3-30b-a3b |
| Modality | text->text (input: text) |
| Context window | 131,072 tokens |
| Max output | 16,384 tokens |
| Tool / function calling | ✅ Yes |
| Structured output (JSON) | ✅ Yes |
| Released | 2025-04-28 |
| Knowledge cutoff | 2025-03-31 |
| Open weights | ✅ Qwen/Qwen3-30B-A3B · 2.8M downloads / 905 likes (30d) |
How to call Qwen3 30B A3B
from openai import OpenAI
client = OpenAI(base_url="https://api.datallmlab.com/v1", api_key="YOUR_DATALLM_LAB_KEY")
resp = client.chat.completions.create(
model="qwen/qwen3-30b-a3b",
messages=[{"role": "user", "content": "Hello"}],
# supports tools=[...] and tool_choice
)
print(resp.choices[0].message.content)curl https://api.datallmlab.com/v1/chat/completions \
-H "Authorization: Bearer YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"qwen/qwen3-30b-a3b","messages":[{"role":"user","content":"Hello"}]}'import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.datallmlab.com/v1", apiKey: process.env.DATALLM_KEY });
const r = await client.chat.completions.create({
model: "qwen/qwen3-30b-a3b",
messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);The same key routes Qwen3 30B A3B and 300+ other models — switch models by changing one string. How OpenAI-compatible APIs work.
Prompting tips for Qwen3 30B A3B
- State the goal, not every step. Qwen3 30B A3B reasons internally — give it the objective, constraints and success criteria and let it plan the approach; over-scripting each step tends to lower quality. Turn effort up for hard problems.
- Give it real tools. Pass a
tools=[...]schema instead of asking it to "pretend" — let Qwen3 30B A3B emit tool calls, execute them, and feed results back for the next turn. - Constrain JSON with a schema. Use
response_format/ structured outputs rather than "reply in JSON" to get valid, parseable objects every time. - Cheap enough to batch. Great for high-volume classification, extraction and drafting — template the prompt, batch requests, and validate outputs programmatically.
Tips are derived from Qwen3 30B A3B's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.
Open-source tools for Qwen3 30B A3B
Popular open-source projects for running and building with Qwen3 30B A3B — star counts pulled from GitHub (July 2026).
Listed by GitHub stars; inclusion is by ecosystem relevance (inference engines, agent frameworks and SDKs), not affiliation. Stars change — see each repo for current numbers.
Migrating to Qwen3 30B A3B
Coming from OpenAI or another gateway? On an OpenAI-compatible setup the only changes are base_url → https://api.datallmlab.com/v1 and model → qwen/qwen3-30b-a3b. Messages, streaming, tool calls and the rest of your code stay the same. Routing & failover guide.
Self-host or use the API?
Qwen3 30B A3B ships open weights (Qwen/Qwen3-30B-A3B, ~2.8M downloads and 905 likes in the last 30 days), with community quantizations (fp8) for smaller GPUs, so you can run it yourself. Most teams still use the API: no GPU to provision or keep warm, no inference ops, and instant access across 3 providers with automatic failover. Self-host when data residency or fixed per-token economics matter most.
Rate limits & reliability
Rate limits here are the DataLLM Lab gateway's, not the upstream vendor's. On 429 (rate limited) or 503 (provider busy), retry with exponential backoff; because Qwen3 30B A3B is served by 3 providers, requests can fail over to a healthy one automatically. See the error-code guide and failover setup.
Related reading
Call Qwen3 30B A3B with one key
300+ models behind one OpenAI-compatible endpoint — better prices, better uptime, no subscriptions.
Get an API keyCompare pricingFrequently asked questions
What is Qwen3 30B A3B?
Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. It accepts text input with a 131K-token context window and was released on 2025-04-28. On DataLLM Lab it is callable through one OpenAI-compatible endpoint.
How much does Qwen3 30B A3B cost?
On DataLLM Lab it is $0.12 per 1M input tokens and $0.50 per 1M output tokens — cheaper than about 75% of the 309-model catalog on output price. Pay-as-you-go, no subscription.
What is the context window of Qwen3 30B A3B?
131K tokens, with up to 16K max output tokens.
Is Qwen3 30B A3B open source?
Yes — open weights are published on Hugging Face (Qwen/Qwen3-30B-A3B), with about 2.8M downloads and 905 likes in the last 30 days. You can self-host it or call it via DataLLM Lab.
How do I call the Qwen3 30B A3B API?
Point any OpenAI SDK at https://api.datallmlab.com/v1 and set model to "qwen/qwen3-30b-a3b". One DataLLM Lab key routes this model and 300+ others; no code changes beyond the base URL and model string.
What are good alternatives to Qwen3 30B A3B?
Close options by price and capability include Qwen3 VL 30B A3B Instruct, Qwen3 8B, Qwen3 VL 8B Instruct — all callable with the same DataLLM Lab key.