Qwen3.5 397B A17B API Pricing
As of October 10, 2026, Qwen3.5 397B A17B costs $0.1644 per 1M input tokens and $0.9864 per 1M output tokens from AIHubMix, the lowest of 7 providers. It has a 262,144-token context window and returns up to 65,536 tokens per response.
- Input
- $0.1644 / 1M tokens
- Output
- $0.9864 / 1M tokens
- Context window
- 262,144 tokens
- Max output
- 65,536 tokens
- Providers
- 7
- Added to LiteLLM
- March 2026
Qwen3.5 397B A17B pricing by provider
7 listings across 7 providers. Token prices are in US dollars per 1M tokens. The highlighted row is the price quoted above.
| Provider | Model name in LiteLLM | Input | Output | Cache read | Context |
|---|---|---|---|---|---|
| AIHubMix | aihubmix/qwen3.5-397b-a17b | $0.1644 | $0.9864 | – | 256K |
| DeepInfra | deepinfra/Qwen/Qwen3.5-397B-A17B | $0.45 | $3.00 | $0.22 | 256K |
| Nebius AI Studio | nebius/Qwen/Qwen3.5-397B-A17B | $0.60 | $3.60 | – | 256K |
| Novita AI | novita/qwen/qwen3.5-397b-a17b | $0.60 | $3.60 | – | 256K |
| Scaleway | scaleway/qwen/qwen3.5-397b-a17b | $0.60 | $3.60 | – | 256K |
| Together AI | together_ai/Qwen/Qwen3.5-397B-A17B | $0.60 | $3.60 | $0.35 | 256K |
| Tensormeshfp8 | tensormesh/Qwen/Qwen3.5-397B-A17B-FP8 | $0.60 | $3.60 | $0 | 256K |
What Qwen3.5 397B A17B costs in practice
1,000 requests with 2,000 input and 500 output tokens each
| AIHubMix | $0.822 |
| DeepInfra | $2.40 |
| Nebius AI Studio | $3.00 |
| Novita AI | $3.00 |
100 long-document requests with 100,000 input and 2,000 output tokens each
| AIHubMix | $1.841 |
| DeepInfra | $5.10 |
| Nebius AI Studio | $6.72 |
| Novita AI | $6.72 |
Qwen3.5 397B A17B features
- Function calling
- Structured output
- Image input
- Reasoning
- Prompt caching
- Web search
Use Qwen3.5 397B A17B with LiteLLM
Call Qwen3.5 397B A17B through the LiteLLM Python SDK, or put it behind the LiteLLM proxy and send OpenAI-format requests to /v1/chat/completions. LiteLLM tracks the cost of every request at these prices.
from litellm import completion
response = completion(
model="aihubmix/qwen3.5-397b-a17b",
messages=[{"role": "user", "content": "Hello!"}],
)model_list:
- model_name: qwen3.5-397b-a17b
litellm_params:
model: aihubmix/qwen3.5-397b-a17b
# credentials: see the provider's LiteLLM docs pagecurl http://0.0.0.0:4000/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer sk-1234" \
-d '{
"model": "qwen3.5-397b-a17b",
"messages": [{"role": "user", "content": "Hello!"}]
}'Qwen3.5 397B A17B FAQ
How much does Qwen3.5 397B A17B cost?
Qwen3.5 397B A17B costs $0.1644 per 1M input tokens and $0.9864 per 1M output tokens from AIHubMix, the lowest of 7 providers.
What is the context window of Qwen3.5 397B A17B?
Qwen3.5 397B A17B has a 262,144-token context window and returns up to 65,536 tokens per response.
Which providers offer Qwen3.5 397B A17B?
Qwen3.5 397B A17B is available from 7 providers through LiteLLM: AIHubMix, DeepInfra, Nebius AI Studio, Novita AI, Scaleway, Together AI and Tensormesh. The lowest price is on AIHubMix.
How do I call Qwen3.5 397B A17B with an OpenAI-compatible API?
Use the LiteLLM Python SDK with model="aihubmix/qwen3.5-397b-a17b", or add the model to the LiteLLM proxy's config.yaml and send requests to /v1/chat/completions from any OpenAI client. LiteLLM tracks the cost of every request at the prices on this page.
What features does Qwen3.5 397B A17B support?
Qwen3.5 397B A17B supports function calling, structured output, image input, reasoning, prompt caching and web search.
When did LiteLLM add Qwen3.5 397B A17B?
Qwen3.5 397B A17B was added to LiteLLM's model price file in March 2026.
Sources
Prices come from LiteLLM's open-source model_prices_and_context_window.json, which LiteLLM uses to calculate the cost of every request. Provider pricing pages:
- aihubmix.com/api/v1/models
- deepinfra.com/pricing
- tokenfactory.nebius.com/models/catalog/text2text/Qwen%2FQwen3.5-397B-A17B
- novita.ai/pricing
LiteLLM docs: AIHubMix, DeepInfra, Nebius AI Studio, Scaleway, Together AI, Tensormesh.
Provider pages: AIHubMix, DeepInfra, Nebius AI Studio, Novita AI, Scaleway, Together AI, Tensormesh.
