qwen-plus
Qwen-Plus is based on the Qwen2.5 foundation model and offers a balanced combination of performance, speed and cost.
Precio: Entrada: 62.4 créditos / 1 M de tokens (≈ $0.31); salida: 187.2 créditos / 1 M de tokens (≈ $0.94).
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Qwen-Plus API on ModelFold
Qwen-Plus is based on the Qwen2.5 foundation model and offers a balanced combination of performance, speed and cost. Try Qwen-Plus in the ModelFold Playground, then call it from your own code through an OpenAI-compatible chat completions API.
What Is the Qwen-Plus API
Qwen-Plus at a glance
Qwen-Plus is a chat model from Qwen. It accepts text as input and returns text, with a context window of up to 1M tokens.
Use it without hosting anything
On ModelFold you call Qwen-Plus with a single API key — no model weights to download and no inference servers to run. The same key also works for the other chat, image, video and audio models on ModelFold.
Core Capabilities of Qwen-Plus
Up to 1M tokens of context
Keep long conversations, large documents and sizeable code in a single request, so the model can reason over the full material instead of fragments.
Function calling
Qwen-Plus supports tool (function) calling: describe your functions in the request and the model returns structured calls your code can run — the basis for agents and integrations.
Structured outputs
Ask for JSON that follows a schema (response_format), which makes extraction, classification and system integrations more reliable.
How to Use Qwen-Plus on ModelFold
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Step 1: Try it in the Playground
Open the Playground at the top of this page, type a prompt and see how Qwen-Plus responds before writing any code.
- 2
Step 2: Create an API key
Sign in to ModelFold and create an API key in the console. Keep it secret and send it as a Bearer token.
- 3
Step 3: Send a request
Call POST https://modelfold.ai/v1/chat/completions with "model": "qwen-plus" and your messages. Existing OpenAI-compatible SDKs work by changing the base URL and key.
- ✓
Step 4: Ship and monitor
Integrate the response into your product and check token usage and spend for every call in the console logs.
What You Can Build with Qwen-Plus
High-volume pipelines
Run classification, extraction, rewriting and batch jobs at scale where cost per request matters.
Long-document workflows
Feed in long reports, contracts, manuals or codebases and ask for summaries, comparisons and answers grounded in the material.
Writing and content
Draft, edit and translate articles, emails, product copy and documentation in a consistent tone.
Tool-calling backends
Route requests, call functions and chain API calls as the worker model inside larger agent systems.
Frequently Asked Questions
What is Qwen-Plus?⌄
Qwen-Plus is based on the Qwen2.5 foundation model and offers a balanced combination of performance, speed and cost.
Which inputs does Qwen-Plus support?⌄
Qwen-Plus accepts text and returns text. Through ModelFold you send text messages; documents attached in the Playground are converted to text.
How long is the context window of Qwen-Plus?⌄
Up to 1M tokens, shared between your input and the model’s output.
Does Qwen-Plus support function calling?⌄
Yes. Qwen-Plus supports tools / function calling through the standard tools parameter of the chat completions API.
How is Qwen-Plus billed on ModelFold?⌄
Pay as you go by tokens: input and output tokens are charged at the per-million-token prices shown on this page. There is no monthly fee, and failed requests are not charged.
How do I call Qwen-Plus through the API?⌄
Send a POST request to https://modelfold.ai/v1/chat/completions with "model": "qwen-plus", using your ModelFold API key as the Bearer token.
| Entrada | 62.4 créditos 1M tokens≈ $0.31 |
| Salida | 187.2 créditos 1M tokens≈ $0.94 |
Se cobra por token, con tarifas separadas para entrada y salida. Los tokens de razonamiento cuentan como salida. El entorno de pruebas aplica exactamente el mismo cálculo.
Leer la documentación de la API →AI Agents · Claude Code / Cursor / Trae →curl https://modelfold.ai/v1/chat/completions \
-H "Authorization: Bearer $MODELFOLD_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"qwen-plus","messages":[{"role":"user","content":"Hello"}]}'