qwen3-32b
Provider: Alibaba, Context: 131072, Output Limit: 16384
| Provider | Source | Input Price ($/1M) | Output Price ($/1M) | Description | Free |
|---|---|---|---|---|---|
| alibaba | models-dev | Input: $0.70 | Output: $2.80 | Provider: Alibaba, Context: 131072, Output Limit: 16384 | |
| groq | models-dev | Input: $0.29 | Output: $0.59 | Provider: Groq, Context: 131072, Output Limit: 16384 | |
| alibabacn | models-dev | Input: $0.29 | Output: $1.15 | Provider: Alibaba (China), Context: 131072, Output Limit: 16384 | |
| siliconflowcn | models-dev | Input: $0.14 | Output: $0.57 | Provider: SiliconFlow (China), Context: 131000, Output Limit: 131000 | |
| chutes | models-dev | Input: $0.08 | Output: $0.24 | Provider: Chutes, Context: 40960, Output Limit: 40960 | |
| cortecs | models-dev | Input: $0.10 | Output: $0.33 | Provider: Cortecs, Context: 16384, Output Limit: 16384 | |
| siliconflow | models-dev | Input: $0.14 | Output: $0.57 | Provider: SiliconFlow, Context: 131000, Output Limit: 131000 | |
| helicone | models-dev | Input: $0.29 | Output: $0.59 | Provider: Helicone, Context: 131072, Output Limit: 40960 | |
| ovhcloud | models-dev | Input: $0.09 | Output: $0.25 | Provider: OVHcloud AI Endpoints, Context: 32000, Output Limit: 32000 | |
| iflowcn | models-dev | Input: $0.00 | Output: $0.00 | Provider: iFlow, Context: 128000, Output Limit: 32000 | |
| friendli | models-dev | Input: - | Output: - | Provider: Friendli, Context: 131072, Output Limit: 8000 | |
| deepinfra | litellm | Input: $0.10 | Output: $0.28 | Source: deepinfra, Context: 40960 | |
| sambanova | litellm | Input: $0.40 | Output: $0.80 | Source: sambanova, Context: 8192 | |
| fireworksai | litellm | Input: $0.90 | Output: $0.90 | Source: fireworks_ai, Context: 131072 | |
| openrouter | openrouter | Input: $0.08 | Output: $0.24 | Qwen3-32B is a dense 32.8B parameter causal language model from the Qwen3 series, optimized for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for tasks like math, coding, and logical inference, and a "non-thinking" mode for faster, general-purpose conversation. The model demonstrates strong performance in instruction-following, agent tool use, creative writing, and multilingual tasks across 100+ languages and dialects. It natively handles 32K token contexts and can extend to 131K tokens using YaRN-based scaling. Context: 40960 |