import requests r = requests.post( "https://api.aimlapi.com/v1/chat/completions", headers={"Authorization": "Bearer " + AIMLAPI_KEY}, json={ "model": "alibaba/qwen3.8-27b", "messages": [ { "role": "user", "content": "Hello!" } ] }, ) print(r.json())
const r = await fetch("https://api.aimlapi.com/v1/chat/completions", { method: "POST", headers: { Authorization: `Bearer ${process.env.AIMLAPI_KEY}`, "Content-Type": "application/json", }, body: JSON.stringify({ "model": "alibaba/qwen3.8-27b", "messages": [ { "role": "user", "content": "Hello!" } ] }), }); console.log(await r.json());
curl -X POST https://api.aimlapi.com/v1/chat/completions \ -H "Authorization: Bearer $AIMLAPI_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"alibaba/qwen3.8-27b","messages":[{"role":"user","content":"Hello!"}]}'
OpenAI-compatible — swap the base URL and it works with your existing SDK.
| Type | Price |
|---|---|
| Input | |
| Output | |
| Benchmark | Score | What it measures | Source | Retrieved |
|---|---|---|---|---|
| Intelligence | 33.9 | Composite score across standardised reasoning, knowledge and problem-solving evaluations, measured independently by Artificial Analysis | Source | September 12, 2026 |
| Coding | 68.1 | Composite score across standardised coding evaluations, measured independently by Artificial Analysis | Source | September 12, 2026 |
| Terminal-Bench | 73.0 | Autonomous shell/terminal task completion | Source | September 22, 2026 |
| LiveCodeBench | 90.3 | Contamination-free competitive programming problems | Source | September 22, 2026 |
| OSWorld | 84.3 | Computer-use across real desktop applications | Source | September 22, 2026 |
| GPQA Diamond | 89.2 | Google-proof graduate science questions (hardest subset) | Source | September 22, 2026 |
| Humanity's Last Exam | 30.8 | Expert-level questions across many domains | Source | September 22, 2026 |
| Model | Input | Output | Context | Best for |
|---|---|---|---|---|
Qwen 3.8 27B This page | Multimodal + agents | |||
| Reasoning + agents | ||||
| Balanced coding + agents | ||||
| Long-context, multimodal & agentic workflows | ||||
| Reasoning + agents |
Qwen 3.8 27B has a 262,144 tokens context window and can return up to 131,072 tokens.
Qwen 3.8 27B takes image, text, video as input and returns text.
Use alibaba/qwen3.8-27b as the model id. Requests go to https://api.aimlapi.com/v1/chat/completions.
Qwen 3.8 27B became available on August 14, 2026.
Qwen 3.8 27B is priced at input $0.61893 / 1M tokens, output $4.40128 / 1M tokens.
Yes, Qwen 3.8 27B can stream responses as they are generated.
Yes, Qwen 3.8 27B accepts image input alongside text.
Qwen 3.8 27B was built by Alibaba Cloud.
It is an open-weight dense vision-language chat model from Alibaba Cloud, designed for coding, research, multimodal interaction and long-running agent tasks.
Yes, it accepts image and video inputs in addition to text, and produces text output.
Yes, it includes a reasoning capability that can be tuned per request.
Yes, it supports structured output generation.