Alibaba / QwenQwen 3chatOpen Weight
Qwen3 Coder Next
Canonical Model Slug: qwen3-coder-next · License Source: Alibaba Qwen Apache 2.0 / Tongyi Qianwen Research License (Open Weight)
Vision: NoTool Calling: YesReasoning: NoPrompt Caching: Yes
收錄報價總數9across 4 providers
可比較標準報價數9USD / 1M Token basis
最低 Input / 1M$0.12Standard tier min
最低 Output / 1M$0.80Standard tier min
最大 Context Window262,144Max tokens
Max Output Tokens235,929Completion limit
跨供應商 Input / Output 費率視覺化比較 (USD / 1M Tokens)
Showing top 9 comparable offerings sorted by input price
跨供應商價格比較表 (9)
所有價格均可追溯至 LiteLLM 或國網中心原始紀錄，國網報價固定置頂。勾選多列可鎖定圖表比較，點擊「原始紀錄」可檢視完整 JSON 欄位。
| Select | Marketplace · Underlying | Context Window | 計價方案 (Tier) · Region | 操作 | |||
|---|---|---|---|---|---|---|---|
OpenRouter openrouter/qwen/qwen3-coder-next | OpenRouter Marketplace Inference: Not Specified | $0.12 ★ 最低價 | $0.80 ★ 最低價 | 262,144 | StandardGlobal + Cache / Batch rates | ||
Novita AI novita/qwen/qwen3-coder-next | Novita AI Inference: Novita AI | $0.20 | $1.50 | 262,144 | StandardGlobal | ||
AWS Bedrock bedrock/us-east-1/qwen.qwen3-coder-next | AWS Bedrock Inference: AWS Bedrock | $0.50 | $1.20 | 262,144 | Standardus-east-1 | ||
AWS Bedrock bedrock/us-east-2/qwen.qwen3-coder-next | AWS Bedrock Inference: AWS Bedrock | $0.50 | $1.20 | 262,144 | Standardus-east-2 | ||
AWS Bedrock bedrock/us-west-2/qwen.qwen3-coder-next | AWS Bedrock Inference: AWS Bedrock | $0.50 | $1.20 | 262,144 | Standardus-west-2 | ||
AWS Bedrock (Converse) qwen.qwen3-coder-next | AWS Bedrock Inference: AWS Bedrock | $0.50 | $1.20 | 256,000 | StandardGlobal | ||
AWS Bedrock bedrock/ap-northeast-1/qwen.qwen3-coder-next | AWS Bedrock Inference: AWS Bedrock | $0.60 | $1.44 | 262,144 | Standardap-northeast-1 | ||
AWS Bedrock bedrock/eu-central-1/qwen.qwen3-coder-next | AWS Bedrock Inference: AWS Bedrock | $0.60 | $1.44 | 262,144 | Standardeu-central-1 | ||
AWS Bedrock bedrock/eu-west-1/qwen.qwen3-coder-next | AWS Bedrock Inference: AWS Bedrock | $0.60 | $1.44 | 262,144 | Standardeu-west-1 |
同系列獨立變體與不同參數規模模型（嚴格分離、不強制合併）
依據平台標準化規則，不同參數規模（如 20B 與 120B）、Safeguard 安全微調版、Preview 版與 GGUF 量化版本皆維持獨立比較，不混入上方報價表。
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