per 1M tokens
Qwen3.5 397B A17B
novita/qwen/qwen3.5-397b-a17b
The Qwen3.5 series 397B-A17B native vision-language model is based on a hybrid architecture design that integrates linear attention mechanisms with sparse Mixture-of-Experts (MoE), achieving higher inference efficiency. Across a variety of tasks—including language understanding, logical reasoning, code generation, agentic tasks, image understanding, video understanding, and graphical user interface (GUI) interaction—it demonstrates exceptional performance comparable to current top-tier frontier models. Possessing robust code generation and agentic capabilities, it exhibits strong generalization across various agent scenarios.
Price
per 1M tokens
Providers
Each row is one seller. Prices are the published rate from that source. A dash means that source did not publish the amount. Sorted by a 3:1 blend of input and output.
No provider prices yet.
Monthly bill
Token models use the token counts. Image, video, and audio models use units times the flat rate.
$4.20 at list price for this workload
Every rate
| Unit | Input | Output | Flat | Confidence |
|---|---|---|---|---|
| token | $0.6/1M | $3.6/1M | Not published— | official |
Price history
input or flat output
Daily snapshots
| UTC date | Unit | Input | Output | Flat |
|---|---|---|---|---|
| 2026-10-08 | token | $0.6 | $3.6 | Not published— |
| 2026-10-09 | token | $0.6 | $3.6 | Not published— |
| 2026-10-10 | token | $0.6 | $3.6 | Not published— |
| 2026-10-11 | token | $0.6 | $3.6 | Not published— |
| 2026-10-12 | token | $0.6 | $3.6 | Not published— |
| 2026-10-13 | token | $0.6 | $3.6 | Not published— |
| 2026-10-14 | token | $0.6 | $3.6 | Not published— |
| 2026-10-15 | token | $0.6 | $3.6 | Not published— |
| 2026-10-16 | token | $0.6 | $3.6 | Not published— |
| 2026-10-17 | token | $0.6 | $3.6 | Not published— |
| 2026-10-18 | token | $0.6 | $3.6 | Not published— |
| 2026-10-19 | token | $0.6 | $3.6 | Not published— |
| 2026-10-20 | token | $0.6 | $3.6 | Not published— |
| 2026-10-21 | token | $0.6 | $3.6 | Not published— |
| 2026-10-22 | token | $0.6 | $3.6 | Not published— |
| 2026-10-23 | token | $0.6 | $3.6 | Not published— |
| 2026-10-24 | token | $0.6 | $3.6 | Not published— |
| 2026-10-25 | token | $0.6 | $3.6 | Not published— |
| 2026-10-26 | token | $0.6 | $3.6 | Not published— |
| 2026-10-27 | token | $0.6 | $3.6 | Not published— |
| 2026-10-28 | token | $0.6 | $3.6 | Not published— |
| 2026-10-29 | token | $0.6 | $3.6 | Not published— |
| 2026-10-30 | token | $0.6 | $3.6 | Not published— |
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Call this model
OpenRouter id novita/qwen/qwen3.5-397b-a17b. InferenceList does not send the request.
curl
curl https://openrouter.ai/api/v1/chat/completions \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"novita/qwen/qwen3.5-397b-a17b","messages":[{"role":"user","content":"Hello"}]}'
JavaScript
await fetch("https://openrouter.ai/api/v1/chat/completions", {
method: "POST",
headers: {
Authorization: "Bearer " + process.env.OPENROUTER_API_KEY,
"Content-Type": "application/json",
},
body: JSON.stringify({
model: "novita/qwen/qwen3.5-397b-a17b",
messages: [{ role: "user", content: "Hello" }],
}),
});
Python
import os, json, urllib.request
req = urllib.request.Request(
"https://openrouter.ai/api/v1/chat/completions",
data=json.dumps({"model": "novita/qwen/qwen3.5-397b-a17b", "messages": [{"role": "user", "content": "Hello"}]}).encode(),
headers={"Authorization": "Bearer " + os.environ["OPENROUTER_API_KEY"], "Content-Type": "application/json"},
)
print(urllib.request.urlopen(req).read().decode())