Model overview
> **Built with mlx-optiq**, the MLX-native toolkit to > quantize, fine-tune, and serve LLMs locally on Apple Silicon (no PyTorch, no > cloud). Try the Lab · All OptiQ > quants · Docs An OptiQ mixed-precision MLX quant of **KAT-Coder-V2.5-Dev**, a `qwen3_5_moe` coding model (256-routed-expert sparse MoE with hybrid linear + full attention). `qwen3_5_moe` loads under stock `mlx-lm` too, but `optiq serve` adds mixed- precision loading, KV-cache quantization, and the OptiQ Lab.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.
| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
View full indexed rankings →Frequently asked questions
Do open weights permit free commercial use?
Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.
Are these real-time billing prices?
These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.
Sources & traceability
许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。