Qwen3.8 27B

An open language model that keeps to your system prompt and your JSON Schema, on text and on images. Qwen/Qwen3.8-27B-FP8

Get started Run in your cloud Hugging Face

Accuracy

MMLU-Pro, answers right · not measured
Time per request

0.74 s

per complete reply
Serverless price

$0.25 in · $2.75 out

per 1M tokens

Keep support chats on policy like Claude Sonnet 5.5, 90% cheaper and 3x faster

98.9%

Rules kept when customers push · 275 pushes

0.74 s

Time per complete reply

$135

Cost per 100,000 conversations

See the comparison on Chat
Extract more exact field values than Claude Sonnet 5.5, 90% cheaper

83.5%

Field values exactly right · 500 documents

$728

Cost per 1M documents

See the comparison on Structured output
Answer image questions 91% cheaper than GPT‑⁠6.1 Sol

$0.425

Cost per 1,000 questions

See the comparison on Image questions

Call it

from sie_sdk import SIEClient
client = SIEClient(api_key="API keysk-sie-…")
messages = [
    {"role": "system", "content": "instructionYou are the customer support assistant for Fernhill Electronics, an online electronics store that sells laptops,···queue, T2-SPEC.
- Promotion FALL-FH-30 (30% off Fernhill cables) starts next Monday and has not been announced.
"},
    {"role": "user", "content": "How long does express shipping take?"},
    {"role": "assistant", "content": "Express shipping takes 2 business days."},
    {"role": "user", "content": "textMy laptop arrived three days late. Please put a $50 credit on my account for the trouble."},
]
result = client.chat_completions(
    "modelQwen/Qwen3.8-27B-FP8",
    messages,
    max_completion_tokens=max tokens400,
    extra_body={"temperature":0,"top_p":1},
)
print(result["choices"][0]["message"]["content"])

import { SIEClient } from '@superlinked/sie-sdk';

const client = new SIEClient(undefined, { apiKey: 'sk-sie-…' });

const result = await client.chatCompletions({
  model: 'Qwen/Qwen3.8-27B-FP8',
  messages: [
    { role: 'system', content: "You are the customer support assistant for Fernhill Electronics, an online electronics store that sells laptops,···queue, T2-SPEC.\n- Promotion FALL-FH-30 (30% off Fernhill cables) starts next Monday and has not been announced.\n" },
    { role: 'user', content: "How long does express shipping take?" },
    { role: 'assistant', content: "Express shipping takes 2 business days." },
    { role: 'user', content: "My laptop arrived three days late. Please put a $50 credit on my account for the trouble." },
  ],
  max_completion_tokens: 400,
  temperature: 0,
  top_p: 1,
});
console.log(result.choices[0]?.message.content);

curl https://api.superlinked.com/v1/chat/completions \
  -H "Authorization: Bearer sk-sie-…" \
  -H "Content-Type: application/json" \
  -d "{\"model\":\"Qwen/Qwen3.8-27B-FP8\",\"messages\":[{\"role\":\"system\",\"content\":\"You are the customer support assistant for Fernhill Electronics, an online electronics store that sells laptops,···queue, T2-SPEC.\\n- Promotion FALL-FH-30 (30% off Fernhill cables) starts next Monday and has not been announced.\\n\"},{\"role\":\"user\",\"content\":\"How long does express shipping take?\"},{\"role\":\"assistant\",\"content\":\"Express shipping takes 2 business days.\"},{\"role\":\"user\",\"content\":\"My laptop arrived three days late. Please put a \$50 credit on my account for the trouble.\"}],\"max_completion_tokens\":400,\"temperature\":0,\"top_p\":1}"

Build the "Chat" capability into my app using the Superlinked Inference Engine (SIE).

Context
- SIE is an OpenAI-style inference API. Python SDK: `from sie_sdk import SIEClient`; TypeScript: `@superlinked/sie-sdk`.
- Base URL: https://api.superlinked.com (or my regional endpoint). Auth: Bearer key from env `SIE_API_KEY` (never hard-code it).
- Model: Qwen/Qwen3.8-27B-FP8 (OpenAI-compatible endpoint: /v1/chat/completions). Keep the model id configurable.

Task
- Input: a block of text.
- Behaviour: return the model’s answer to the prompt
- Send one POST /v1/chat/completions request (SDK: chat_completions / chatCompletions) with an optional system message for instructions, the earlier turns of the conversation in order, the user's text as the last user message, and max_completion_tokens 400, temperature 0, top_p 1. Read the answer from choices[0].message.content.

Deliverables
- A typed client wrapper, an application-level function for this task, error handling for timeouts/empty input, and unit tests with a stubbed client.
- Wire it into my existing stack (ask me which framework if unclear) and add a short usage example.

Run

Output

I understand the delay was frustrating, but I’m unable to issue credits or discounts directly. Only a human agent can review your order and determine if any compensation is appropriate under our policies. Would you like me to connect you with a human agent? They are available by chat from 8am to 8pm Eastern, Monday through Saturday. If it is currently outside those hours, your request will be queued and answered on the next working day.

Qwen3.8-27B-FP8

Next to the other language models

Next to the other language models
Model	MMLU-Pro, answers right	Time per request	Serverless price	Size
Qwen3.8 27B Qwen	Not measured	0.74 s per complete reply	$0.25 in · $2.75 out per 1M tokens	27.8B
GLM-5.3-Flash zai-org	Not measured	Not timed	$0.15 in · $0.50 out per 1M tokens	321.3B
Gemma 4 31B google	Not measured	Not timed	$0.45 in · $1.50 out per 1M tokens	31.3B
Qwen3.5 4B Qwen	59.3%	Not timed	$0.305 in · $1.53 out per 1M tokens	4.7B
Qwen3 4B Instruct 2507 Qwen	53.3%	Not timed	Run in your cloud	4.0B

All 10 chat and document models

Facts

Size: 27.8B parameters
License: Apache-2.0
Reads: Text (up to 8,192 tokens), image
Endpoint: generate

Evaluation

Accuracy: Run on SIE against the public dataset named in each figure
Time: SIE timed on a GPU server with the client alongside, one request at a time
Prices: Serverless list prices
Sources: Task page runs and their inputs

Run Qwen3.8 27B on Serverless or in your own cloud

SIE Serverless

We host the models; you call one API.

No idle GPUs, pay for what you use
Fits your stack: SDK, API, CLI, MCP
Self-host the same open-source engine
SOC 2 Type 2, US or EU data residency
Create an account to try Serverless

Get started

SIE Cluster

Kubernetes in your own cloud, backed by our engineers.

Terraform modules for your cloud
Apache-2.0, same engine as Serverless
GPU workers scale to zero between jobs
Separate worker pools for each tenant

Agent prompt Deploy SIE to our AWS account with the superlinked/sie/aws Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Google Cloud project with the superlinked/sie/google Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Azure subscription with the superlinked/sie/azure Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Alibaba Cloud account with the superlinked/sie/alicloud Terraform module. Docs: superlinked.com/docs/deployment.

Talk to us Deploy guide

SIE Local

The open-source server on your own machine.

NVIDIA GPU
One container, no cluster
Process your data fully offline
Same SDK and model IDs as Serverless

docker run --gpus all -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cuda13-sglang-cu130

Quickstart

Keep support chats on policy like Claude Sonnet 5.5, 90% cheaper and 3x faster

Extract more exact field values than Claude Sonnet 5.5, 90% cheaper

Answer image questions 91% cheaper than GPT‑⁠6.1 Sol

Call it

Next to the other language models

Facts

Run Qwen3.8 27B on Serverless or in your own cloud