Qwen Models & API
Compare the Qwen models available on TokenHub, including Qwen3.8 Max, Qwen3.6, Qwen 2.5, and Qwen Coder. Review pricing, context, capabilities, and model IDs, then call Qwen, Tongyi Qianwen, and Qianwen through one OpenAI-compatible API workflow.
Qwen Models & API Pricing
Compare current Qwen API pricing, input and output token costs, context windows, endpoints, and model IDs. The catalog may include Qwen3.8 Max, Qwen3.6, Qwen 2.5, and Qwen Coder; availability follows the live TokenHub model list.
Compare input, output, and cache-read prices for the available Qwen models. Open a model page to confirm the current billing unit and model ID.
Compare context windows, maximum output, reasoning, tool calling, endpoints, and release dates across Qwen3.8 Max, Qwen3.6, Qwen 2.5, and Qwen Coder.
Pricing, model availability, context windows, and endpoint support reflect the current TokenHub catalog. Confirm the model detail page before a production release because specifications and availability can change.
Last updated: 2026-08-25Get Started With the Qwen API
Create a TokenHub API key, copy a published Qwen model ID, and call it with OpenAI Chat Completions, Responses, or Claude Messages. Existing OpenAI SDK projects can usually switch by changing the API key, Base URL, and model ID.
- 01Create a TokenHub API key
- 02Choose an available Qwen model
- 03Choose an API protocol
- 04Copy the published Qwen model ID
- 05Send your first request
Choose an API protocol
import OpenAI from "openai"
const client = new OpenAI({
apiKey: process.env.TOKENHUB_API_KEY,
baseURL: "https://us-api.tokenhub.com/v1",
})
const result = await client.chat.completions.create({
"model": "qwen3.5-flash",
"messages": [
{
"role": "user",
"content": "Explain why low latency matters for an AI product in one sentence."
}
]
})
console.log(result.choices[0]?.message?.content)import json
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ.get("TOKENHUB_API_KEY"),
base_url="https://us-api.tokenhub.com/v1",
)
request = json.loads("{\"model\":\"qwen3.5-flash\",\"messages\":[{\"role\":\"user\",\"content\":\"Explain why low latency matters for an AI product in one sentence.\"}]}")
result = client.chat.completions.create(**request)
print(result.choices[0].message.content)curl 'https://us-api.tokenhub.com/v1/chat/completions' \
-X 'POST' \
-H "Authorization: Bearer $TOKENHUB_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen3.5-flash",
"messages": [
{
"role": "user",
"content": "Explain why low latency matters for an AI product in one sentence."
}
]
}'const response = await fetch("https://us-api.tokenhub.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.TOKENHUB_API_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"model": "qwen3.5-flash",
"messages": [
{
"role": "user",
"content": "Explain why low latency matters for an AI product in one sentence."
}
]
}),
})
const data = await response.json()
console.log(data)import os
import requests
response = requests.request(method="POST", url="https://us-api.tokenhub.com/v1/chat/completions",
headers={
"Authorization": f"Bearer {os.environ['TOKENHUB_API_KEY']}",
"Content-Type": "application/json",
},
json=__import__("json").loads("{\"model\":\"qwen3.5-flash\",\"messages\":[{\"role\":\"user\",\"content\":\"Explain why low latency matters for an AI product in one sentence.\"}]}"),
)
response.raise_for_status()
print(response.json())import OpenAI from "openai"
const client = new OpenAI({
apiKey: process.env.TOKENHUB_API_KEY,
baseURL: "https://us-api.tokenhub.com/v1",
})
const result = await client.responses.create({
"model": "qwen3.5-flash",
"input": "Explain why low latency matters for an AI product in one sentence."
})
console.log(result.output_text)import json
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ.get("TOKENHUB_API_KEY"),
base_url="https://us-api.tokenhub.com/v1",
)
request = json.loads("{\"model\":\"qwen3.5-flash\",\"input\":\"Explain why low latency matters for an AI product in one sentence.\"}")
result = client.responses.create(**request)
print(result.output_text)curl 'https://us-api.tokenhub.com/v1/responses' \
-X 'POST' \
-H "Authorization: Bearer $TOKENHUB_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen3.5-flash",
"input": "Explain why low latency matters for an AI product in one sentence."
}'const response = await fetch("https://us-api.tokenhub.com/v1/responses", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.TOKENHUB_API_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"model": "qwen3.5-flash",
"input": "Explain why low latency matters for an AI product in one sentence."
}),
})
const data = await response.json()
console.log(data)import os
import requests
response = requests.request(method="POST", url="https://us-api.tokenhub.com/v1/responses",
headers={
"Authorization": f"Bearer {os.environ['TOKENHUB_API_KEY']}",
"Content-Type": "application/json",
},
json=__import__("json").loads("{\"model\":\"qwen3.5-flash\",\"input\":\"Explain why low latency matters for an AI product in one sentence.\"}"),
)
response.raise_for_status()
print(response.json())curl 'https://us-api.tokenhub.com/v1/messages' \
-X 'POST' \
-H "Authorization: Bearer $TOKENHUB_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen3.5-flash",
"max_tokens": 1024,
"messages": [
{
"role": "user",
"content": "Explain why low latency matters for an AI product in one sentence."
}
]
}'const response = await fetch("https://us-api.tokenhub.com/v1/messages", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.TOKENHUB_API_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"model": "qwen3.5-flash",
"max_tokens": 1024,
"messages": [
{
"role": "user",
"content": "Explain why low latency matters for an AI product in one sentence."
}
]
}),
})
const data = await response.json()
console.log(data)import os
import requests
response = requests.request(method="POST", url="https://us-api.tokenhub.com/v1/messages",
headers={
"Authorization": f"Bearer {os.environ['TOKENHUB_API_KEY']}",
"Content-Type": "application/json",
},
json=__import__("json").loads("{\"model\":\"qwen3.5-flash\",\"max_tokens\":1024,\"messages\":[{\"role\":\"user\",\"content\":\"Explain why low latency matters for an AI product in one sentence.\"}]}"),
)
response.raise_for_status()
print(response.json())Which Qwen Model Should You Choose?
Use this Qwen model selection guide for coding, Qwen CLI workflows, general chat, long context, and multimodal generation. Compare price, context, reasoning, tool calling, and endpoint support before shipping.
| API workload | Start with | When it fits | Verify before launch |
|---|---|---|---|
| High-volume Qwen API calls | Qwen3.5 Flash | Fast iteration, chat, extraction, summarization, and batch workloads using Qwen. | Qwen API pricing, throughput, and output-token cost |
| Qwen reasoning and coding | Qwen3.5 35B-A3B | Multi-step analysis, code generation, debugging, and agent tasks where answer quality matters. | Reasoning support, tool calling, latency, and total request cost |
| General Qwen applications | Qwen3.5 35B-A3B | Everyday assistants, content generation, information extraction, and product features. | Task accuracy, endpoint support, and production price |
| Long-context Qwen workloads | Qwen3.5 Plus | Documents, codebases, research, and conversations where input length is the constraint. | Context window, input-token price, maximum output, and streaming |
| Quality-first Qwen workloads | Qwen3.6 Max Preview | Production outputs where capability matters more than the lowest unit cost. | Task quality, latency, and the price difference versus a faster model |
Choose and Compare Qwen Models
Use the live catalog to compare Qwen API price, model specifications, context limits, and supported endpoints.
Integrate Qwen
Use TokenHub to connect Qwen to the development tools already in your workflow.
Qwen API FAQ
What are Qwen, Tongyi Qianwen, and Qianwen?
Qwen, Tongyi Qianwen, and Qianwen are names and search terms associated with this model family. TokenHub uses the published provider and model IDs shown in the live catalog.
Which Qwen models are available on TokenHub?
The live list above shows current availability. Relevant model searches include Qwen3.8 Max, Qwen3.6, Qwen 2.5, and Qwen Coder, but only models displayed in the TokenHub catalog can be called through this page.
How do I get a Qwen API key?
Qwen API and Qwen API key searches lead to the same TokenHub workflow: create an API key, select a published Qwen model ID, and use the TokenHub Base URL in your application.
How is Qwen API pricing calculated?
Pricing depends on the selected model and billing type. Compare input, output, cache, or per-request prices in the table and confirm the model detail page before production use.
Can I use the OpenAI SDK with Qwen?
Yes. Use the TokenHub Base URL, API key, and published Qwen model ID with the OpenAI Python or Node.js SDK. Endpoint support is shown for each model.
Which Qwen model should I use?
Choose based on the workload: coding, Qwen CLI workflows, general chat, long context, and multimodal generation. Compare actual price, context, reasoning, tool calling, and output limits in the live catalog.
Does this page include every Qwen modality?
Qwen Image and Qwen Image Edit models are listed separately when available in the TokenHub catalog.
Start Building With Qwen
Choose a Qwen model, copy its model ID, create a TokenHub API key, and send your first request through one compatible API workflow.