Qwen3 30B A3B Instruct 2507 APIOPEN WEIGHTS131K context

Pricing & specs mirror our live pricing as of July 2026 (pay-as-you-go).

What is Qwen3 30B A3B Instruct 2507?

Qwen3-30B-A3B-Instruct-2507 is a 30.5B-parameter mixture-of-experts language model from Qwen, with 3.3B active parameters per inference. It operates in non-thinking mode and is designed for high-quality instruction following, multilingual understanding, and...

Qwen3 30B A3B Instruct 2507 on the release timeline

Qwen3 30B A3B Instruct 2507 pricing

Pay-as-you-go on DataLLM Lab — these are our live list prices (identical to the pricing page):

On output price, Qwen3 30B A3B Instruct 2507 is cheaper than 91% of the 309 models in the catalog. It is served by 5 providers; the best combined rate at our last snapshot was ≈ $0.05 / $0.19 per 1M via StreamLake, with upstream quantizations bf16, fp8.

What Qwen3 30B A3B Instruct 2507 costs per month

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 Instruct 2507 vs alternatives

Specs

How to call Qwen3 30B A3B Instruct 2507

Copyfrom 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-instruct-2507",
    messages=[{"role": "user", "content": "Hello"}],
    # supports tools=[...] and tool_choice
)
print(resp.choices[0].message.content)

Copycurl https://api.datallmlab.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen/qwen3-30b-a3b-instruct-2507","messages":[{"role":"user","content":"Hello"}]}'

Copyimport 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-instruct-2507",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);

The same key routes Qwen3 30B A3B Instruct 2507 and 300+ other models — switch models by changing one string. How OpenAI-compatible APIs work.

Prompting tips for Qwen3 30B A3B Instruct 2507

• Be explicit and show the shape of the answer. Spell out format, tone and constraints up front, and include one short example of the output you want — Qwen3 30B A3B Instruct 2507 follows concrete instructions better than abstract ones.
• Give it real tools. Pass a tools=[...] schema instead of asking it to "pretend" — let Qwen3 30B A3B Instruct 2507 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 Instruct 2507's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.

Open-source tools for Qwen3 30B A3B Instruct 2507

Popular open-source projects for running and building with Qwen3 30B A3B Instruct 2507 — 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 Instruct 2507

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-instruct-2507. 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 Instruct 2507 ships open weights (Qwen/Qwen3-30B-A3B-Instruct-2507, ~907.8K downloads and 819 likes in the last 30 days), with community quantizations (bf16, 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 5 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 Instruct 2507 is served by 5 providers, requests can fail over to a healthy one automatically. See the error-code guide and failover setup.

Related reading

Frequently asked questions