Qwen2.5 72B Instruct APIOPEN WEIGHTS131K context

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

What is Qwen2.5 72B Instruct?

Qwen2.5 72B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and...

Qwen2.5 72B Instruct on the release timeline

Qwen2.5 72B Instruct pricing

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

On output price, Qwen2.5 72B Instruct is cheaper than 78% of the 309 models in the catalog. It is served by 2 providers; the best combined rate at our last snapshot was ≈ $0.36 / $0.40 per 1M via DeepInfra, with upstream quantizations bf16, fp8.

What Qwen2.5 72B Instruct 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.

Qwen2.5 72B Instruct vs alternatives

Specs

How to call Qwen2.5 72B Instruct

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/qwen-2.5-72b-instruct",
    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/qwen-2.5-72b-instruct","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/qwen-2.5-72b-instruct",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);

The same key routes Qwen2.5 72B Instruct and 300+ other models — switch models by changing one string. How OpenAI-compatible APIs work.

Prompting tips for Qwen2.5 72B Instruct

• 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 — Qwen2.5 72B Instruct follows concrete instructions better than abstract ones.
• Give it real tools. Pass a tools=[...] schema instead of asking it to "pretend" — let Qwen2.5 72B Instruct 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 Qwen2.5 72B Instruct's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.

Open-source tools for Qwen2.5 72B Instruct

Popular open-source projects for running and building with Qwen2.5 72B Instruct — 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 Qwen2.5 72B Instruct

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/qwen-2.5-72b-instruct. Messages, streaming, tool calls and the rest of your code stay the same. Routing & failover guide.

Self-host or use the API?

Qwen2.5 72B Instruct ships open weights (Qwen/Qwen2.5-72B-Instruct, ~626.4K downloads and 960 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 2 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 Qwen2.5 72B Instruct is served by 2 providers, requests can fail over to a healthy one automatically. See the error-code guide and failover setup.

Related reading

Frequently asked questions