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Qwen3.8 27B is an open-weight dense vision-language model from Qwen. It accepts text, image and video inputs and is suited for coding, professional workflows, research, multimodal interaction and long-running agent tasks, with flexible thinking that can be tuned per request.
Meet Qwen3.8-27B - a multimodal open-weight model built for coding, research, and agent workflows.
alibaba/qwen3.8-27bhttps://api.aimlapi.com/v1Qwen3.8-27B is Alibaba Cloud's latest open-weight, dense vision-language model. It accepts text, image, and video input and is built for coding, professional workflows, research, multimodal interaction, and long-running agent tasks, with reasoning effort that can be tuned per request.
| Specification | Details |
|---|---|
| Developer | Alibaba Cloud (Qwen) |
| Model ID | alibaba/qwen3.8-27b |
| Release Date | August 14, 2026 |
| Architecture | Dense, open-weight vision-language model |
| Context Window | 262,144 tokens (~256K) |
| Max Output Tokens | 131,072 |
| Modalities (input) | Text · Image · Video |
| Modalities (output) | Text |
| Capabilities | Vision, tunable reasoning, streaming, structured output |
Text, images, and video are all first-class inputs, so the model can be used for document QA, visual analysis, and video-aware tasks without bolting on a separate vision pipeline.
Reasoning effort can be adjusted low, medium, or high depending on the task, fast and cheap for simple queries, deeper and slower when a problem needs more careful multi-step thinking.
Responses can be returned as strict structured JSON or streamed token-by-token, making the model workable both for user-facing chat interfaces and for backend pipelines that need machine-readable output.
Native text, image, and video input means it can be used directly on screenshots, scanned documents, or video frames without a separate vision model in front of it.
Qwen3.8 27B is documented as suited to coding and software engineering tasks, including repository-level code generation, refactoring, bug fixing, and multi-file edits.
It is also documented as suited to long-horizon agentic work, such as autonomous planning, multi-step task execution, and tool orchestration across long sessions.
The model is documented for multimodal vision-language use, including image and video understanding, OCR, visual question answering, and chart, diagram, and document understanding.
It is also documented for complex problem solving and professional work, including multi-step reasoning, mathematical problem solving, research, and decision support.
See how Qwen 3.8 27B compares in intelligence, generation speed, cost per task and latency. Data retrieved Nov 3, 2026.
Compare 13 models available on AI/ML API across 5 independent benchmarks.
Expert-written questions across academic subjects.
Graduate-level science questions written to resist web search.
Agentic tasks completed in a command-line environment.
Reasoning across long documents of about 100k tokens.
Python coding tasks drawn from scientific research.
| Usage | Price | Unit |
|---|---|---|
| Input | $0.4761 | 1M tokens |
| Output | $3.39 | 1M tokens |
Prices from the AI/ML API price list, checked on Nov 4, 2026.
AI/ML API input and output rates for 5 models, per million tokens.
| Model | Input | Output |
|---|---|---|
| $0.4761 | $3.39 | |
| $0.15 | $0.47 | |
| $0.16 | $0.47 | |
| $2.65 | $6.61 | |
| $4.23 | $12.7 |
Give your agent the model details, code and documentation.
export AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"curl '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": "Write a haiku about the ocean."
}
]
}'import os
import requests
headers = {
"Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
"Content-Type": "application/json",
}
payload = {
"model": "alibaba/qwen3.8-27b",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}
response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())const response = 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": "Write a haiku about the ocean."
}
]
}),
});
if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());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 costs $0.4761 input and $3.39 output per 1M tokens on AI/ML API, billed as you go with no subscription.
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.