One week on Arc Mainnet. 624M USDC in circulation. $6.8B USDC transferred. Day one was just the beginning.

Introducing Xiaomi MiMo-V2.6 — Pro & Flash. Frontier intelligence, all the modalities, built in public. 🔹 Two omnimodal models, advancing through scaled reinforcement learning 🔹 Pro performs on par with Claude Opus 5 and GPT-5.6 Sol across most agent benchmarks 🔹 Pro scores 46 on the Artificial Analysis Intelligence Index — the highest among open-source models 🔹 Stronger coding, computer use, 3D reasoning and creative capabilities 🔹 Open model weights, technical report, RL environments and training code Blog：mimo.xiaomi.com/mimo-v2-6

1/ Jev, a decision model by @typesafeai, sparked a burst of projects and discussion. We tested it using Ori Eval against popular LLMs on OpenRouter at judging. Jev was >5x faster than the next fastest model, and even its slowest requests beat every other model's median.

80% of all Robinhood Stock Token volume is traded on Uniswap That translates to $10B (and counting)

Arc Mainnet is live. Arc launches as the Economic OS for the internet: an open platform for global markets, real-time value movement, tokenized assets, and agentic economic activity. Arc is more than a blockchain. It launches as a full-stack financial platform with assets, applications, interoperability, developer infrastructure, and Circle platform services live from day one. Arc delivers USDC as native gas, deterministic sub-second finality, EVM compatibility, and institutional validators. It integrates with Arc Studio, App Kits, Arc Portal, Circle Agent Stack, CCTP, Gateway, CPN, and StableFX. A complete economic platform at genesis. Arc launches with infrastructure for: → Agentic economic workflows → Lending and borrowing → Trading and liquidity → Onchain FX → Payments and settlement → Tokenized assets → Exchanges, wallets, custody, compliance, data, and developer tooling 190+ institutional and ecosystem builders are building across Arc.

The ARC token has been minted. Circle is the first publicly traded company to mint a network token for a new blockchain. This is a technical milestone in the Arc roadmap as the network explores a future transition from Proof of Authority toward Proof of Stake. The minting of ARC does not represent any commitment to a public launch of the token. ARC is not live, tradeable, available for public use, or active for staking, governance, fees, or utility. The Arc network remains Proof of Authority today. Future ARC functionality and activation remain subject to change. Official ARC contract: explorer.arc.io/token/0xA12C…

Calling all Architects. See you soon.

Exciting news: DeepSeek-V4.1-Flash (Max) by @deepseek_ai just landed in Agent Arena at #3 among open models! With +4.87% net improvement and a median cost per task of $0.07 it reshaped the Pareto frontier. Among the top 3 open models, DeepSeek-V4.1-Flash (Max) has the lowest median cost per task. Its +4.87% net improvement is within 0.09 percentage points of Hy4 preview (ranked #2) at 68% lower cost, and within 1.52 percentage points of Kimi K3 (Max) (ranked #1) at 91% lower cost. - Kimi K3 (Max): +6.39% | $0.77/task - Hy4 preview: +4.96% | $0.22/task - DeepSeek-V4.1-Flash (Max): +4.87% | $0.07/task See the full Pareto Frontier below. DeepSeek-V4.1-Flash (Max) is ranked #12 overall, and by signal landed #4 Confirmed Success with +13.75%! Congrats to the @deepseek_ai team on this release!

GPT-6 Astra vs Kimi K3. I'm comparing these because the gap between US models and Chinese models is already getting small. If US AI labs slow down, they will be the ones most affected by it. > Astra did it in one shot 6m 20s and $8. > Kimi K3 got almost the same result, but I had to use 2 follow-up prompts. Total: 12 min and $6.35. The results are almost identical. If US labs slow down, China takes the AI race. They will ship Astra-level open-weight models. China is already good at this. Open weights become the default. That's a nightmare for US labs.

Nearly a year of Code Arena: WebDev progress compressed into 15 seconds. Each line follows the highest-scoring model from top labs over time, showing the pace of improvement across the ecosystem. In just the last year, the model in the leading spot increased score by +340 pts and the number of frontier labs competing for the top spot expanded from 6 to 10. @AnthropicAI has dominated throughout the year. Although standout releases have jumped to the top spot, most notably the Chinese open-source model Kimi K3 from @Kimi_Moonshot in July. Today, GPT-6 Astra by @OpenAI leads with 1796 pts, followed by @claudeai Fable 5.1 at 1764 pts. The next closest lab is 103 pts away, @Alibaba_Qwen with 1685 pts. Code Arena: WebDev ranks models through head-to-head user preference on real front-end web development tasks. These votes drive the leaderboard that is tracking the frontier.

DeepSeek-V4.1-Flash by @deepseek_ai just landed ~#14 overall in Code Arena: WebDev with 1620 pts (AutoEval)! Among open models, DeepSeek-V4.1-Flash landed at ~#4 within 11 pts of Qwen3.8-Flash-Next. This release is a significant improvement compared to DeepSeek-V4 variants: +38 pts vs. V4-Flash (High) at #20 (1582 pts) +40 pts vs. V4-Pro (High) #21 (1580 pts) DeepSeek-V4.1-Flash is within 5 pts of the models ranked #11–#13, while costing substantially less (priced per million input/output tokens): ~#14 DeepSeek-V4.1-Flash: $0.30/ $1.20 #13 Hy4 Preview: $0.83/ $2.50 #12 Grok-4.6 High: $2/ $6 #11 Muse Spark 1.3 xHigh: $1.25/ $4.25 Note: this is an early AutoEval score, in which a Reward Model trained on Arena’s human preference data casts automatic votes in place of live votes. We’ll continue to see how scores converge as more live human votes come in. Congrats to the @deepseek_ai team on this contribution to the open source ecosystem!

🚀 Introducing DeepSeek-V4.1-Flash: smarter, faster, more efficient. 🔹 Introducing the smallest model in our new architecture family, with native visual understanding. 🔹 Designed for greater capability, faster inference, higher throughput, and scaling to larger models. 1/6

We benchmarked DeepSeek V4.1 Flash by @deepseek_ai . It reached 98% of GPT-6 Astra’s score at 1.4% of the cost on everyday design tasks based on user requests. Every model except Astra scored lower AND cost more. Are open models overtaking closed ones? Full results below ↘️