Qwen-Max is a large-scale Mixture-of-Experts (MoE) model from Qwen, based on Qwen2.5, and provides the best inference performance among Qwen models, especially for complex multi-step tasks. Pretrained on over 20 trillion tokens and further post-trained with curated Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF), it is designed for high-accuracy, high-recall applications. The exact parameter count is undisclosed.
$0.80
$0.16
$3.20
Throughput is how fast the model writes (tokens per second, higher is better). Time to first token is how long before the answer starts (lower is better).
Share of requests served without a server error. Client errors (4xx) do not count.
Not enough throughput data
Not enough TTFT data
Works with the OpenAI SDK pointed at our base URL. Every parameter is in the API reference.
Tokens processed per day on NagaAI.
Qwen-Max is a large-scale Mixture-of-Experts (MoE) model from Qwen, based on Qwen2.5, and provides the best inference performance among Qwen models, especially for complex multi-step tasks.
On NagaAI, Qwen Max costs $0.80 per 1M input tokens and $3.20 per 1M output tokens. You pay only for what you use, with no subscription.
Qwen Max reads up to 32.8k tokens in one request, counting the prompt and the answer together.
Yes. Qwen Max can call your functions as tools and can answer in a JSON schema you give it.
It takes text as input and returns text.
Create an API key, copy the model ID from the top of this page and send requests with the OpenAI or Anthropic SDK pointed at NagaAI. The API section on this page has a ready example.
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