Use this model when
- The workload fits the text or chat tasks shown in this model's current catalog record.
- The input fits within the published 32K-token context record, with output and system overhead budgeted separately.
Pricing
Input$0 / 1M
Output$0 / 1M
Cache Write$0 / 1M
Cache Read$0 / 1M
Prompt cache writes and reads are included at no additional cost.
Quick Start
Select an endpoint and copy a working example for this model.
Endpoint:
python
from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.chat.completions.create( model="qwen3-1.7b:free", messages=[ {"role": "user", "content": "Hello!"} ], max_tokens=1024, temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(# model="qwen3-1.7b:free",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )Supported Parameters
API docsCommon7 params
modelmessagesmax_tokenstemperaturetop_pstreamtoolsExtended4 params
reasoning_effortstream_optionsthinkingextra_bodyCursor IDE Model IDs
Use these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
Decision guidance
Fit the request contract before production.
Check before production
- Confirm the production client uses one of the listed request surfaces: /v1/chat/completions, /v1/responses, /v1/messages.
- Estimate a representative request from the current Free price fields instead of extrapolating from a tiny prompt.
- Check the observed-availability card and run your own timeout, retry, and fallback test before relying on the model.
Compare with Other Models
See how this model compares to others from the same provider.
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- Context
- 262K
- Input
- $0.45/M
- Output
- $3.20/M
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B is Qwen's open-weight sparse Mixture-of-Experts (MoE) model and the open-weight counterpart to Qwen3.8 Max. It features 2.4T total parameters with 95B activated per token, combining frontier-scale capacity with efficient sparse inference. Designed for coding, research, complex reasoning, and agentic workflows, the model is well suited for demanding long-horizon tasks and advanced autonomous systems while providing the flexibility and customization benefits of open weights.
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- Input
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- Output
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Qwen3.8 Max
Qwen3.8 Max is the flagship model in Alibaba's Qwen3.8 series and the general-availability successor to Qwen3.8 Max Preview. It is a multimodal reasoning model designed for complex tasks across reasoning, visual understanding, coding, and agentic workflows. As the production-ready top tier of the Qwen3.8 family, it is well suited for advanced problem solving, multimodal analysis, software engineering, and long-running tool-driven applications.
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- 1M
- Input
- $2.00/M
- Output
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Qwen Plus 2025-07-28 Thinking
Qwen Plus 0728 is a hybrid reasoning model built on the Qwen3 foundation, featuring a 1M-token context window and a balanced trade-off between performance, speed, and cost.
- Context
- 1M
- Input
- $0.40/M
- Output
- $4.00/M