This is a simplified guide to an AI model called Price-Predict-V1 maintained by Humbleworth. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.
Overview
price-predict-v1 is a domain valuation model that predicts the monetary value of domain names using machine learning. Built by humbleworth, this model accepts a comma-separated list of up to 2,560 domains and returns predicted valuations for each. The model runs on Replicate's infrastructure and processes domains efficiently in batch format. The single most important consideration before using this model is understanding that domain valuation involves significant uncertainty—predicted values should be treated as estimates rather than authoritative market prices, and actual resale value depends on many external factors including market conditions, buyer demand, and branding potential that no algorithm can fully capture.
Best use cases
Bulk domain portfolio assessment. If you manage a portfolio of dozens or hundreds of domains, this model allows you to quickly generate estimated valuations across your entire inventory without manually researching each domain. This is useful for portfolio auditing, determining which domains might be worth monetizing, or understanding the aggregate asset value of your holdings. The batch processing capability (up to 2,560 domains per request) makes it practical for large-scale portfolio analysis that would be time-consuming to perform manually.
Domain marketplace pricing strategy. When listing domains for sale on marketplaces like Sedo, Namecheap, or GoDaddy, having an initial valuation estimate helps you set competitive opening prices. The model can quickly generate baseline asking prices before you apply your own domain expertise and market knowledge. This accelerates the pricing workflow when you have multiple domains to list.
Due diligence in domain acquisitions. Before acquiring a domain from another party, you can use this model to validate whether the asking price aligns with algorithmic estimates. While the model should not be the sole basis for an acquisition decision, it provides a quick sanity check against obviously overpriced or underpriced domains relative to comparable assets.
Domain investment research. Investors evaluating whether to register or purchase domains in specific categories (like technology terms, geographic modifiers, or emerging keywords) can use bulk valuations to understand the relative value distribution across different domain characteristics. This helps identify patterns in which types of domains tend to command higher valuations.
Limitations
The model has several significant constraints. Domain valuation is inherently uncertain—predicted values are statistical estimates based on training data and do not account for subjective factors like brand potential, emotional attachment to specific words, or sudden shifts in market demand. The model cannot accept more than 2,560 domains in a single request, so extremely large portfolios require multiple API calls. The model provides point estimates without confidence intervals or uncertainty quantification, making it difficult to assess how reliable any individual prediction is.
The output schema includes an optional error field, indicating that requests can fail partially or completely—the documentation does not specify under what conditions errors occur or how to handle them. There is no information available about the model's training data, age, or how frequently it is updated, so predictions may reflect outdated market conditions. The model's performance on newer generic top-level domains (gTLDs), non-English domains, or extremely short/valuable domains is unknown. No information is provided about the model's accuracy, typical error ranges, or performance benchmarks against actual market prices, limiting your ability to quantify confidence in individual predictions.
Commercial use rights are not documented in the available materials, and no license information is provided. The model appears to be actively maintained (latest version created September 3, 2025), but backward compatibility or breaking changes in future versions are not specified.
How it compares
The similar models provided in the reference set are audio and video processing tools (v3, sabuhi-model-v2, whisper-timestamped, bel-tts, and whisperx), which are not directly comparable to a domain valuation model. No alternative domain pricing or valuation models appear in the reference set. If you need domain valuation, price-predict-v1 is the only option provided. For tasks involving audio transcription, text-to-speech, or speech recognition, the audio-focused models would be appropriate instead.
Technical specifications
The model is deployed as a Replicate inference service (Cog version 0.16.6). The latest version was deployed on September 3, 2025. The input accepts domain names as a string field with a default value of "example.com" and supports up to 2,560 domains in a comma-separated list format. The output returns a JSON object containing an optional error field (nullable string) and a required valuations array of DomainValuation objects. The exact structure of individual DomainValuation objects is not detailed in the schema but presumably includes the domain name and predicted value.
No information is available regarding the model architecture, parameter count, training dataset composition or size, inference requirements (CPU/GPU), inference speed, or any quantization options. The model description and schema provide only functional information, not the underlying technical implementation details typical of published machine learning models.
Model inputs and outputs
Inputs
- domains (string): Comma-separated list of domain names or a single domain. Maximum of 2,560 domains per request. Default value: "example.com"
Outputs
-
valuations (array of objects): Array of
DomainValuationobjects containing predicted valuations for each input domain - error (string, nullable): Optional error message if the request partially or fully fails
Getting started
import replicate
client = replicate.Client(api_token="your-replicate-api-token")
input_domains = "example.com,google.com,test.org,mybusiness.io"
output = client.run(
"humbleworth/price-predict-v1:a925db842c707850e4ca7b7e86b217692b0353a9ca05eb028802c4a85db93843",
input={"domains": input_domains}
)
print(output)
Frequently asked questions
Q: What format should I use when submitting multiple domains?
A: Provide domains as a comma-separated string (e.g., "domain1.com,domain2.com,domain3.io"). The model accepts up to 2,560 domains in a single request.
Q: How accurate are the valuations this model produces?
A: The accuracy is not documented. No performance benchmarks, error rates, or comparison against actual market prices are provided in the available materials. Treat predictions as estimates rather than definitive valuations.
Q: Can I use this model to price domains for commercial resale?
A: You can use the model to generate baseline pricing estimates, but you should validate predictions against current market comparables and apply your own domain expertise. The model's predictions alone should not be the sole basis for setting commercial prices.
Q: Does the model work with non-English domains or new generic top-level domains?
A: This is not documented. The model's performance on internationalized domain names (IDNs), non-standard TLDs, or extremely new domain extensions is unknown.
Q: What happens if a domain in my request is invalid or causes an error?
A: The schema indicates an optional error field in the response, but the documentation does not specify which invalid inputs trigger errors or how partial failures are handled. Test with your specific domain types to understand failure behavior.
Q: Is the model actively maintained?
A: The latest version was deployed on September 3, 2025, indicating recent activity. However, no information is provided about update frequency, deprecation plans, or how breaking changes would be communicated.
Q: How long does a valuation request take?
A: Inference speed is not documented. Response time depends on the number of domains submitted and Replicate's queue, but specific latency data is not available.
Q: Can I get confidence intervals or uncertainty estimates alongside the valuations?
A: The output schema returns point estimates only. No confidence intervals, percentile ranges, or uncertainty quantification are provided in the model's output format.