Qwen's 122B/10B-active multimodal MoE, balancing stronger quality with efficient inference.
Qwen's 397B/17B-active multimodal flagship with broad multilingual, coding, and agentic capability.
| Provider | Role | Context | Price · in / out |
|---|---|---|---|
| ddeepinfraNo trainingNo training on your data | Primary | 262K | $0.60 / $3.60/MToksame price, any rail |
| ddigitaloceanNo trainingNo training on your data. Zero-retention available | Fallback | 262K |
routing.strategyproviders.onlyCollecting — charts appear after two days.
curl https://www.ninjachat.ai/api/v1/chat/completions \
-H "Authorization: Bearer $NINJACHAT_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen-3.5-397b-a17b",
"messages": [{ "role": "user", "content": "Hello!" }],
"stream": true
}'Qwen's 397B/17B-active multimodal flagship with broad multilingual, coding, and agentic capability.
Qwen 3.5 397B A17B costs $0.60/M input tokens and $3.60/M output tokens.
Qwen 3.5 397B A17B accepts text and image and returns text.
POST /api/v1/chat/completions with `"model": "qwen-3.5-397b-a17b"`.
Qwen 3.5 122B A10B, Qwen 3.5 27B, Qwen 3.6 27B, Qwen 3.6 35B A3B, Qwen 3.7 Plus, Qwen 3.8 2.4T A95B, Qwen 3.8 27B, Qwen 3.8 Max, Qwen3 Next 80B A3B, QwQ 32B.
providers.excludeproviders.ordermax_cost_usddata_policyQwen's compact multimodal reasoning model on a cost-efficient DeepInfra rail with ultra-fast Groq failover.
Qwen's sparse 35B/3B-active multimodal model for efficient coding and agents.
Qwen's fast multimodal flagship for agent loops, coding, and tool use.