Qwen/Qwen3-14B

Qwen/Qwen3-14B is a budget-tier language model from Deep Infra with 14B parameters. Qwen/Qwen3-14B is an open-weights model, meaning the weights are publicly available for self-hosting, fine-tuning, and deployment on your own infrastructure — not just through Deep Infra's API. It is a reasoning model that uses extended chain-of-thought (CoT) processing before producing a final answer — significantly more capable on complex math, science, and multi-step logic tasks, but slower and more expensive per query than standard models. Qwen is Alibaba's open-weights model family, with strong multilingual performance (especially Chinese/English) and a wide parameter range from 0.5B to 72B.

Qwen/Qwen3-14B is priced at $0.12/1M input tokens and $0.24/1M output tokens. Output tokens are 2× more expensive than input tokens — this ratio reflects the sequential nature of autoregressive generation, which requires a separate forward pass per output token versus parallel processing of all input tokens simultaneously. At a typical usage pattern of 1,000 input tokens and 500 output tokens per call, Qwen/Qwen3-14B costs approximately $0.24 per 1,000 API calls. Deep Infra does not publish a prompt caching rate for this model, so repeated long contexts are billed at the standard $0.12/1M input rate.

Its 41K-token context window is sufficient for most production use cases, handling documents up to ~25,000 words or multi-turn conversations spanning dozens of exchanges.

Qwen/Qwen3-14B is best suited to cost, speed, reasoning. Supported capabilities include function calling / tool use for agentic workflows, streaming token output for real-time UIs. Function calling support makes it suitable for building AI agents, structured data extraction pipelines, and any workflow requiring the model to invoke external APIs or return typed JSON. When evaluating this model for a specific workload, the most important factors are: context window (does your input fit?), output price (how long are your generations?), and latency (is this interactive or batch?). The pricing table above shows how Qwen/Qwen3-14B compares to similar models across these dimensions.

Qwen/Qwen3-14B sits in the budget tier — the most cost-efficient models, best for high-volume simple tasks, classification, extraction, and applications where cost per call is the primary constraint. As an open-weights model, Qwen/Qwen3-14B can be self-hosted on GPU compute (see the GPU rental comparison on this site) to eliminate per-token API costs entirely — the break-even point versus API pricing depends on your volume and the GPU hourly rate. When comparing Qwen/Qwen3-14B to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (62/100) reflects aggregate benchmark performance — a useful proxy for general capability, though real-world performance varies significantly by task type. Always test with your specific prompts and use case before committing to a model for production.