Trading activity:
Reported 24-hour volume equals 0.00% of market capitalization. The local markets snapshot lists Uniswap V3 (Base) and Uniswap V2 (Ethereum) among venues with observed trading activity.
YearBull indicators:
YearBull Rank #7,695. Bull Score 39/100.
YB Market Risk Low. This relative market-volatility label is not an investment-safety assessment.
Cycle Early. Observed price change: 24h -9.20% · 7d -3.33% · 30d -22.72%.
Values are descriptive and should be read together rather than as a price forecast.
Read the YearBull methodology.
Snapshot date: 2026-10-27.Data history: 90 days available in the latest 90-day window.
Methodology responsibility:
YearBull’s analytical methodology and presentation rules are developed and maintained by
Alan Zelvin, Founder & Lead Crypto Researcher.
This note identifies responsibility for the methodology; it does not attribute authorship of this data snapshot.
What is Cortensor (COR)?
Source description
“Cortensor is a decentralized AI inference network that leverages distributed computation and open-source models to provide scalable, cost-effective processing for AI tasks and inference. The network is powered by community-driven miners who contribute computational resources and are incentivized for their work. By overcoming the limitations of centralized platforms, Cortensor ensures broad access to advanced AI tools and services while offering flexibility for both Web2 and Web3 integrations. This approach enables businesses and developers to utilize AI capabilities without the high costs associated with traditional, centralized AI providers.”
This source-supplied description may contain old, promotional, or unverified claims and is not YearBull editorial analysis.
Official links and contract records appear in Key Facts. Project details can change, so verify current information with the project.
Cortensor (COR) FAQ
How does Cortensor use community-provided computing resources for AI inference?
The project presents Cortensor as a decentralized inference network in which community-driven miners contribute computational capacity. According to the project, these participants are incentivized for their work, allowing AI tasks to be processed across distributed resources rather than relying solely on centralized providers. The specific hardware requirements, reward formula, and miner selection process are not specified.
What cost and scalability benefits does Cortensor claim for AI processing?
Cortensor states that distributed computation can provide scalable and cost-effective processing for AI tasks and inference. Its positioning contrasts this approach with traditional centralized platforms, which the project says can involve high costs and capacity limitations. The project does not specify performance benchmarks, pricing structures, throughput targets, or the conditions under which these claimed benefits apply.
How does Cortensor aim to support both Web2 and Web3 integrations?
The project says its network offers flexibility for Web2 and Web3 integrations, allowing businesses and developers to access AI capabilities across both application environments. This positioning suggests an intended role as infrastructure that can serve different software ecosystems. Cortensor does not specify supported frameworks, APIs, integration procedures, or examples demonstrating how those connections are implemented.
What role do openly available AI models play in Cortensor’s network?
Cortensor describes its network as leveraging distributed computation together with openly available AI models for inference tasks. The project presents this combination as a way to broaden access to AI tools and services while reducing dependence on centralized platforms. It does not identify particular models, explain model governance, or clarify how model updates and quality control are managed.
Who is Cortensor intended to serve, and what access problem does it address?
Cortensor positions its network for businesses and developers that want to use AI capabilities without the high costs associated with traditional centralized providers. The project also says its decentralized structure is intended to make advanced AI tools and services more broadly accessible. It does not define specific industries, user eligibility requirements, service tiers, or supported application categories.
Cortensor metric comparison
This comparison is a stored snapshot generated 2026-10-23 06:30 UTC from 293 daily observations available from 2025-12-30 through 2026-10-23. It is separate from the latest analytical cards above. Percentiles compare the snapshot value with that day's analytical universe; a higher percentile means a larger observed value, not necessarily a better investment characteristic.
Metric
Snapshot
30d before
90d before
Change vs 30d
Universe percentile
Price
$0.00147855
$0.00179574
$0.00129484
-17.7%
n/a
Market cap
$1.13M
$1.37M
$990.6K
-17.7%
P66.6
YearBull Rank
n/a
#856
#2,900
n/a
n/a
Bull Score
n/a
64/100
51/100
n/a
n/a
Turnover
0.00%
0.63%
0.27%
-0.6 pts
P6.5
YB Market Risk
n/a
Low
Low
n/a
n/a
Cycle
n/a
Early
Early
n/a
n/a
Median absolute daily movement 2.39%; distance from the highest local daily price -70.3%; circulating supply change +23.4%. These measurements are descriptive and do not predict future direction.
YearBull Rank on this page
Most recent YearBull Rank reading for cortensor is #7695.
Rank timeline (last 365 days)
Rank change (daily snapshots).
Reading rule: smaller rank numbers are better.
7d window (2026-10-20): #4839 → #7695 (down by 2856).
30d window (2026-09-28): #889 → #7695 (down by 6806).
Regime context: If the 30d is noisy, increase the lookback to avoid over-reading. cycle shifts often show up as slope changes, not spikes.
Flow context: If the curve improves and holds, it is usually more structural. rank can move when liquidity redistributes across the cohort.
Listing context: If the line is step-like, watch for discrete market changes. a new route can show up as a step change.
Risk note: If the curve is step-like, it may be reacting to discrete inputs. ranking moves can reflect regime shifts rather than one-off events.
YearBull Rank is a relative placement score used on YearBull to compare a coin against peers within the same dataset. Lower rank numbers indicate stronger placement in the current snapshot. Treat it as a directional context tool rather than a standalone verdict.
Editorial note:
This analysis was prepared by the YearBull research team under the direction of
Alan Zelvin,
Founder and Lead Crypto Researcher.
The assessment follows YearBull’s internal research methodology and editorial standards.
Methodology ·
Editorial Policy
Related Research
Continue with contextually connected profiles and category data:
Venue refresh pending. Markets last checked: 2026-07-26. The next refresh is queued in the hourly updater. Venue listings and volumes are stored snapshots, not live quotes.
