Model overview
**Agate Preview 003 renders at 512 × 512.** It is the first Agate to score above Stable Diffusion 1.5 on Qwen-Image-Bench: **32.6** against SD 1.5's 29.1 and Preview 002's 28.9, on all 1,000 prompts, with 261M parameters. On GenEval (official scorer) it scores **0.554**, level with Preview 001 (0.550) and below Preview 002 (0.577). It is Agate's 256 px network made multi-resolution (additions that start as exact no-ops), trained at 256 and 512 px, then fine-tuned with perceptual losses. It runs in Python, in ComfyUI (0.4.0) and in the browser (WebGPU demo, the default version there). **Why we built Agate:** it is the result of an architecture study for glyph and symbol generation; see Why we built Agate below.
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：mit。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。