MODEL PROFILE

KAT-Coder-V2.5-Dev-MTP-APEX-GGUF

Myric · KAT-Coder-V2.5-Dev-MTP-APEX-GGUF

MoE-aware, mixed-precision **APEX** quantization of Kwaipilot/KAT-Coder-V2.5-Dev, an agentic-coding fine-tune on the Qwen3.6-35B-A3B backbone (`qwen3_5_moe`: 40 layers, 256 routed + 1 shared expert, hybrid GatedDeltaNet linear-attention with periodic full attention). Kwaipilot reports SWE-bench Verified 69.40 for this size class, against Qwen3.6-35B-A3B's 64.40. **What makes this build different: it has a working MTP head.** KAT-Coder ships `mtp_num_hidden_layers: 0` — no multi-token-prediction head at all, so no speculative decoding is possible out of the box, and that is true of the vendor release and of every other quant of this model I am aware of. This repo transplants Qwen3.6-35B-A3B's own trained MTP head onto KAT-Coder and quantizes the result. Measured below: **2.03x on a hard agentic-coding suite, with correctness unchanged.** Full draft-depth x temperature surface, measured on `-v2`. One long-context prompt (~82 KB), `max_tokens=400`, 3 seeds per cell, median tok/s, `top_p 0.95 top_k 20`, ctx 32768, build `6d05498`. Speedup is against the no-drafter baseline **at the same temperature**:

Text generationlicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/31 02:23:19
OVERVIEW

Model overview

MoE-aware, mixed-precision **APEX** quantization of Kwaipilot/KAT-Coder-V2.5-Dev, an agentic-coding fine-tune on the Qwen3.6-35B-A3B backbone (`qwen3_5_moe`: 40 layers, 256 routed + 1 shared expert, hybrid GatedDeltaNet linear-attention with periodic full attention). Kwaipilot reports SWE-bench Verified 69.40 for this size class, against Qwen3.6-35B-A3B's 64.40. **What makes this build different: it has a working MTP head.** KAT-Coder ships `mtp_num_hidden_layers: 0` — no multi-token-prediction head at all, so no speculative decoding is possible out of the box, and that is true of the vendor release and of every other quant of this model I am aware of. This repo transplants Qwen3.6-35B-A3B's own trained MTP head onto KAT-Coder and quantizes the result. Measured below: **2.03x on a hard agentic-coding suite, with correctness unchanged.** Full draft-depth x temperature surface, measured on `-v2`. One long-context prompt (~82 KB), `max_tokens=400`, 3 seeds per cell, median tok/s, `top_p 0.95 top_k 20`, ctx 32768, build `6d05498`. Speedup is against the no-drafter baseline **at the same temperature**:

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.

PRICING & COST

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 / channelInputOutputBilling unitCollected 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.

INDEPENDENT BENCHMARKS

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。