Qwen3 VL 235B A22B Thinking APIOPEN WEIGHTS131K context

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

What is Qwen3 VL 235B A22B Thinking?

Qwen3-VL-235B-A22B Thinking is a multimodal model that unifies strong text generation with visual understanding across images and video. The Thinking model is optimized for multimodal reasoning in STEM and math....

Qwen3 VL 235B A22B Thinking on the release timeline

Qwen3 VL 235B A22B Thinking pricing

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

On output price, Qwen3 VL 235B A22B Thinking is cheaper than 36% of the 309 models in the catalog. It is served by 2 providers; the best combined rate at our last snapshot was ≈ $0.26 / $2.60 per 1M via Alibaba, with upstream quantizations bf16.

What Qwen3 VL 235B A22B Thinking 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 VL 235B A22B Thinking vs alternatives

Specs

How to call Qwen3 VL 235B A22B Thinking

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-vl-235b-a22b-thinking",
    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-vl-235b-a22b-thinking","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-vl-235b-a22b-thinking",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);

The same key routes Qwen3 VL 235B A22B Thinking and 300+ other models — switch models by changing one string. How OpenAI-compatible APIs work.

Prompting tips for Qwen3 VL 235B A22B Thinking

• State the goal, not every step. Qwen3 VL 235B A22B Thinking 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.
• 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".
• Give it real tools. Pass a tools=[...] schema instead of asking it to "pretend" — let Qwen3 VL 235B A22B Thinking 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.

Tips are derived from Qwen3 VL 235B A22B Thinking's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.

Open-source tools for Qwen3 VL 235B A22B Thinking

Popular open-source projects for running and building with Qwen3 VL 235B A22B Thinking — 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 VL 235B A22B Thinking

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-vl-235b-a22b-thinking. Messages, streaming, tool calls and the rest of your code stay the same. Routing & failover guide.

Self-host or use the API?

Qwen3 VL 235B A22B Thinking ships open weights (Qwen/Qwen3-VL-235B-A22B-Thinking, ~11.1K downloads and 399 likes in the last 30 days), with community quantizations (bf16) 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 Qwen3 VL 235B A22B Thinking 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