Coinbase finalizes MATIC to POL swap as Polygon migration hits 99% completion

ambcryptoPublicado a 2025-10-10Actualizado a 2025-10-10

Key Takeaway

Why is Coinbase delisting MATIC?

Polygon has fully transitioned to its new ecosystem token, POL, marking the end of MATIC’s lifecycle.

What’s next for holders?

All MATIC on Coinbase will automatically convert to POL at a 1:1 ratio from 14 October, completing Polygon’s migration.


Coinbase has announced that it will permanently disable Polygon [MATIC] trading on 14 October 2025. The move completes the network’s year-long migration to the Polygon Ecosystem Token [POL]. According to the exchange, all remaining MATIC balances on its platform will be automatically converted to POL at a 1:1 ratio.

Coinbase finalizes MATIC phase-out

The exchange will also pause MATIC send-and-receive functions between 14 and 18 October. This provides users with a window to transfer their tokens to self-custody wallets before the conversion begins.

This move makes Coinbase one of the last major exchanges to implement the token swap, following a series of phased transitions across the industry. 

The delisting signals the end of MATIC’s run as Polygon’s primary asset, officially handing the reins to POL as the ecosystem’s new governance and staking token.

Polygon’s near-complete migration

Polygon Labs confirmed on 3 September 2025 that 99% of all MATIC on its network had successfully migrated to POL. This highlights the level of transition since the official upgrade in September 2024.

The shift to POL forms the backbone of the Polygon 2.0 roadmap, which unifies all Polygon chains under a single ecosystem, enabling cross-chain coordination through zero-knowledge proofs and modular staking.

Polygon’s recent Rio upgrade, dubbed “payments-focused rehaul,” rolled out this week, further strengthening this framework.

The upgrade introduces validator staking improvements, cross-chain interoperability tools, and enhanced scalability features, setting the foundation for broader adoption across DeFi and enterprise applications.

Market struggles despite technical milestones

Despite steady progress on the network side, POL’s price has yet to reflect the ecosystem’s growth. 

Data from TradingView shows that POL has declined roughly 40.5% since the MATIC-to-POL transition began in September 2024.

Polygon (POL) price trendPolygon (POL) price trend

Source: TradingView

At the time of writing, POL trades at $0.2273, down 3.7% in the last 24 hours. The token’s Relative Strength Index (RSI) at 41.6 suggests muted buying pressure, with traders remaining cautious amid broader market uncertainty.

The selloff follows a period of consolidation that began in late August, during which the token struggled to maintain momentum despite increased validator participation and expanding ecosystem support.

Share

Lecturas Relacionadas

Robotic GPT-3 Moment Shakes Silicon Valley: Zero Lines of Code, Learns Instantly, with Investments from Jensen Huang and Fei-Fei Li

Generalist AI's newly released robot foundation model GEN-1.5 is being hailed as the "GPT-3 moment" for embodied AI. The model demonstrates remarkable one-shot and few-shot learning capabilities in physical manipulation tasks. By watching a single 3-12 second demonstration video (physical prompting) without any training or code, the robot can attempt the task with a 59% average success rate across 10 tasks. With just 5 minutes of demonstration data and minimal fine-tuning (10 gradient steps), the success rate jumps to 83%. The most significant breakthrough is the emergence of spontaneous, improvisational problem-solving abilities not present in the training data. For instance, after learning to sweep blocks with a brush, the robot can adapt to use a banana similarly or switch to a completely new "scoop-and-pour" strategy when given a dustpan. Other emergent behaviors include removing obstacles, correcting errors, and spontaneously sorting objects. These capabilities stem from over 8 months of large-scale pre-training on physical interaction data, suggesting the existence of a Scaling Law for embodied intelligence—where model generalization improves with more data and training time. This approach drastically reduces the cost and expertise needed to teach robots new skills, potentially democratizing robot programming. The release coincides with a surge in the humanoid robotics sector, marked by events like the World Robot Conference and significant investments. While the demonstrated tasks are still relatively simple, the scaling trend and emergent behaviors point toward a future where general-purpose robot "brains" could be easily adapted to various hardware "bodies," reshaping the industry's competitive landscape.

marsbitHace 58 min(s)

Robotic GPT-3 Moment Shakes Silicon Valley: Zero Lines of Code, Learns Instantly, with Investments from Jensen Huang and Fei-Fei Li

marsbitHace 58 min(s)

Trading

Spot
活动图片