Model overview
AgentionAI Signal is a minimally invasive fine-tune of Qwen3.8-Flash-Next for lower latency and better token efficiency. On MATH-500 it spends **26% fewer reasoning tokens** than the base model with **half as many hesitation markers**, and scores higher (99.2% against 97.5%). As a coding agent on Terminal-Bench 2.0 it finishes the same tasks in **25% less wall time**. With speculative decoding it is faster still: the multi-token-prediction drafter agrees with Signal 90% of the time against 69% for the base model, so more tokens are accepted per forward pass. Signal gets there by being direct, not by cutting corners. Reasoning models spend a large share of their tokens on hesitation and re-checking rather than on new steps. Signal keeps every step that does work and drops the ones that only describe the process, and in answers it drops preambles, sign-offs, excess formatting and narration while keeping the substance. The 177B mixture-of-experts backbone, the vision encoder and the multi-token-prediction draft head all work exactly as in the base model, so it is a drop-in replacement. Signal is trained by self-distillation, on Qwen3.8-Flash-Next's own answers generated under an instruction to be direct that the released model no longer needs. No external data and no other model's outputs went into it, so it keeps the base model's knowledge and voice intact.
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：other。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。