Sei’s momentum vs. Avalanche’s depth – Which network is winning 2026 race?

ambcryptoPublished on 2026-01-27Last updated on 2026-01-27

Abstract

In early 2026, Sei Network briefly lost its lead in perpetual DEX volume to Avalanche, but quickly rebounded by adding $23 million within 48 hours, reestablishing dominance. Sei also led in user engagement with 7.6 million monthly active addresses compared to Avalanche’s 1.6 million. However, Avalanche maintained a significant advantage in Open Interest at $443 million versus Sei’s $74 million, reflecting deeper liquidity and stronger market confidence. While Sei shows strong momentum and growing adoption among traders, Avalanche’s superior capital depth and stability position it as the more established network. The competition hinges on whether Sei can convert its user growth into sustained financial strength.

Sei Network’s early 2026 momentum faced a brief test as Avalanche overtook it in perpetual DEX volume.

According to YAP Network data, Avalanche’s 7-day Perp Volume stood at $3.19 million, down 34.66%. Sei’s Perp Volume reached $28.81 million, rising 0.54% over the same period.

That shift proved short-lived.

Within 48 hours, Sei added roughly $23 million in Perp Volume, reasserting control over the leaderboard. The rebound restored Sei’s lead and highlighted its ability to attract short-term trading activity.

However, volume alone did not settle the contest.

Active addresses tilted toward Sei

Sei Network recorded 7.6 million Active Addresses on a monthly basis, per Token Terminal data. Avalanche, by comparison, logged 1.6 million Active Addresses.

That gap pointed to stronger user participation on Sei’s network. The growth suggested rising adoption, especially among traders seeking faster execution and lower latency.

Even so, Active Addresses did not fully capture capital commitment.

Open Interest favored Avalanche

Despite Sei’s dominance in Active Addresses, Avalanche continued to outperform it in Open Interest (OI), with Avalanche’s OI standing at a commanding $443 million, compared to Sei’s much smaller $74 million.

At press time, Sei [SEI] was trading at $0.11.

This disparity highlighted Avalanche’s deeper liquidity and market confidence.

Open Interest is key in DeFi, and Avalanche’s dominance gave it an edge in stability and long-term viability. At press time, Avalanche [AVAX] was trading at $11.48 within the $8.77-$12 range, confirming its stronger position.

Sei’s volume surge showed potential, but it’s still lagging in market depth. To challenge Avalanche, Sei must turn its growing user base into real financial strength and liquidity.

Momentum vs. market depth

Sei’s Perp Volume rebound showcased its growing appeal among active traders. Avalanche’s Open Interest dominance, however, reflected superior market depth and capital durability.

This left traders watching whether Sei could convert address growth into lasting liquidity. The next phase depended on whether usage translated into deeper derivatives positioning.

Related Questions

QWhich network briefly overtook Sei in perpetual DEX volume in early 2026, and what was the percentage change in its 7-day Perp Volume?

AAvalanche briefly overtook Sei in perpetual DEX volume. Its 7-day Perp Volume was $3.19 million, down 34.66%.

QHow did Sei Network reassert its lead in Perp Volume after the brief shift, and by how much did its volume increase?

ASei Network reasserted its lead by adding roughly $23 million in Perp Volume within 48 hours, restoring its control over the leaderboard.

QAccording to Token Terminal data, how many monthly Active Addresses did Sei Network and Avalanche have, respectively?

ASei Network recorded 7.6 million monthly Active Addresses, while Avalanche logged 1.6 million.

QWhat was the Open Interest (OI) for Avalanche and Sei at the time of the article, and what does this disparity indicate?

AAvalanche's Open Interest was $443 million, compared to Sei's $74 million. This disparity highlighted Avalanche's deeper liquidity and market confidence.

QWhat key metric did the article suggest Sei must improve to challenge Avalanche's market position?

AThe article suggested that Sei must turn its growing user base into real financial strength and liquidity, specifically by converting its address growth into lasting liquidity and deeper derivatives positioning.

Related Reads

Near Returns to the AI Stage: Transformation into a Public Chain Due to 'Payroll Difficulties,' Agent and Privacy Emerge as New Growth Narratives

NEAR Returns to AI Origins: From Payroll Struggles to Blockchain, Now Focusing on AI Agents and Privacy NEAR Protocol's journey began not with grand blockchain ambitions, but from a practical hurdle: its AI startup founders, including Transformer paper co-author Illia Polosukhin, couldn't efficiently pay international developers in 2017. This led them to pivot and build a high-performance, scalable blockchain. After years navigating various crypto narratives like sharding and cross-chain interoperability, NEAR is now leveraging its AI roots to re-enter the AI arena. A key driver is its "NEAR Intents" layer, which abstracts complex cross-chain transactions. Users simply state their goal (e.g., swap BTC for ETH), and a solver network finds the optimal route. This system has processed over $20B in cross-chain volume, generating significant fee revenue. A major growth area is private transactions via "Confidential Intents/Swaps," which hide trade details until settlement to protect against MEV and front-running. Remarkably, private swaps recently accounted for over 40% of NEAR's transaction volume, highlighting strong demand but also potential regulatory scrutiny. With its AI-founder pedigree, NEAR is positioning itself at the intersection of blockchain, AI agents, and privacy, aiming to become infrastructure for the emerging agent economy while navigating the challenges of its rapid adoption.

marsbit2h ago

Near Returns to the AI Stage: Transformation into a Public Chain Due to 'Payroll Difficulties,' Agent and Privacy Emerge as New Growth Narratives

marsbit2h ago

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

In recent discussions, Vitalik Buterin has frequently emphasized the concept of "CROPS," a framework defining core values for Ethereum's development. CROPS stands for Censorship Resistance, Capture Resistance, Open Source, Privacy, and Security. Initially outlined in the Ethereum Foundation's "EF Mandate," it represents a commitment to user sovereignty, ensuring that the network resists external control, remains open, protects privacy, and prioritizes security. The relevance of CROPS extends beyond Ethereum's foundational principles, becoming crucial in the context of AI integration. As AI agents begin handling wallet operations and automated transactions, the risk increases that users may cede control over their digital assets, privacy, and intentions to centralized AI service providers. A "CROPS AI" would therefore emphasize local execution where possible, privacy-preserving remote model calls (e.g., using zero-knowledge proofs), and transparent, verifiable processes to maintain user agency. Vitalik highlights a significant convergence between "CROPS Ethereum access layer" and "CROPS AI." Both address the same fundamental challenge: how users can access powerful services—be it blockchain data via RPCs or AI models—without exposing sensitive information or relinquishing ultimate control. This intersection points toward a future digital entry point that is more private, secure, and user-controlled. Ultimately, CROPS is not merely an abstract ideal but a practical guidepost. It steers development—from protocol resilience and wallet design to AI agent safety—towards a future where users retain self-sovereignty even as digital systems grow more complex and powerful. In an era of accelerating AI adoption, these "slow variables" of censorship resistance, openness, privacy, and security may define Ethereum's enduring value.

marsbit2h ago

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

marsbit2h ago

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

Silicon Valley investor and "Godfather of Startups" Steve Hoffman warns that combining Web3 with AI is likely a trap, not a promising venture. In an interview, Hoffman argues that while AI is a foundational technology touching all industries, Web3 adds complexity, friction, and regulatory risk without solving mainstream consumer or business needs. He advises founders to focus on deep, specialized applications where startups can out-iterate giants, rather than on generic features easily replicated by large tech companies. Hoffman observes that Silicon Valley will lead foundational AI research, while China excels at rapid, large-scale application and commercialization, particularly in robotics. He stresses that AI-driven autonomous agents capable of collaborative, multi-step tasks are 2-4 years away, which will cause significant job displacement. The solution is not to slow AI but to redesign business models around human-AI collaboration and reform social systems like education and retraining. For startups, Hoffman recommends focusing on vertical, expertise-heavy domains to build defensibility. He sees major opportunities in AI fraud detection and cybersecurity. Key founder mindsets include systemic thinking over feature-focus, relentless customer centricity, building adaptive teams, and deeply understanding AI's capabilities and limits. Hoffman is also leading a non-profit initiative to establish university centers aimed at training future leaders in responsible, human-value-aligned AI innovation.

marsbit3h ago

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

marsbit3h ago

Token Inefficient, Economy Tokenless

The article "Tokens Aren't Economical, Economics Aren't Tokenized" analyzes a pivotal shift in the AI industry from a technology-driven narrative to one dominated by capital efficiency. It highlights two concurrent trends: a severe capital shortage due to the exorbitant and recurring costs of compute (e.g., OpenAI's high burn rate) and a wave of corporate spin-offs where major tech companies are separating their AI units (like Kuaishou's Kling and Baidu's Kunlunxin). The core argument is that AI's "anti-internet" business model, where user growth increases costs rather than profits, has created a disconnect between high valuations and actual cash flow. Spin-offs address this by allowing AI assets to be valued independently. Within a parent company, they are seen as cost centers, but as standalone entities, they are priced based on their growth potential and scarcity in the primary market, leading to massive valuation premiums (e.g., Kling's estimated value tripling post-spin-off). The industry is at an inflection point, moving from "model worship" to "value realization." The competition is evolving from a pure compute (GPU) race to a broader focus on systemic efficiency and full-stack engineering (involving CPUs and orchestration) to achieve viable commercialization. The year 2026 is framed as a critical moment where the industry must definitively answer how to economically translate AI capability into tangible business value, reshaping the sector's future power structure.

marsbit3h ago

Token Inefficient, Economy Tokenless

marsbit3h ago

Trading

Spot
Futures

Hot Articles

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of S (S) are presented below.

活动图片