Qwen2.5 VL 72B Instruct APIOPEN WEIGHTS131K context

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

What is Qwen2.5 VL 72B Instruct?

Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.

Qwen2.5 VL 72B Instruct on the release timeline

Qwen2.5 VL 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 VL 72B Instruct is cheaper than 60% of the 309 models in the catalog.

What Qwen2.5 VL 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 VL 72B Instruct vs alternatives

When not to use Qwen2.5 VL 72B Instruct

• It is served by a single provider, so there is less failover headroom during an outage.

Specs

How to call Qwen2.5 VL 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/qwen2.5-vl-72b-instruct",
    messages=[{"role": "user", "content": "Hello"}],
)
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/qwen2.5-vl-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/qwen2.5-vl-72b-instruct",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);

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

Prompting tips for Qwen2.5 VL 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 VL 72B Instruct follows concrete instructions better than abstract ones.
• It reads images. Send image parts alongside your text and ask for specific outputs (named JSON fields, a table, "what changed") rather than "describe this".
• Constrain JSON with a schema. Use response_format / structured outputs rather than "reply in JSON" to get valid, parseable objects every time.

Tips are derived from Qwen2.5 VL 72B Instruct's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.

Open-source tools for Qwen2.5 VL 72B Instruct

Popular open-source projects for running and building with Qwen2.5 VL 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 VL 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/qwen2.5-vl-72b-instruct. Messages, streaming and the rest of your code stay the same. Routing & failover guide.

Self-host or use the API?

Qwen2.5 VL 72B Instruct ships open weights (Qwen/Qwen2.5-VL-72B-Instruct, ~419.9K downloads and 631 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 1 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. See the error-code guide and failover setup.

Related reading

Frequently asked questions