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.

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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!

🚀 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 ↘️

GLM-5.3 achieves 60 on the Artificial Analysis Intelligence Index, on par with Kimi K3 and up 7 points from GLM-5.2. Once the weights are released it will be tied as the leading open weights model @Zai_org has just launched GLM-5.3, which ties Kimi K3 (60) for the most intelligent open weights models on the Artificial Analysis Intelligence Index (assuming weights are released as per Z AI’s guidance). Between Kimi K3 and GLM-5.3, the open weights frontier is closer than ever to the proprietary frontier. GLM-5.3 keeps GLM-5.2's size at 753B total parameters and 40B active parameters. The Z AI team has shared that GLM-5.3’s weights are expected to be released within the next week. Key Takeaways: ➤ GLM-5.3 sees the largest gain in agentic capabilities, placing it amongst frontier models. The model's Elo rating in GDPval-AA v2, our real-world agentic knowledge work evaluation, rises from 1524 to 1770, a 246-point jump. That places GLM-5.3 second among all models, behind only Claude Opus 5 (1855), and surpassing the previous open weights leader on this evaluation, Kimi K3 (1668), by more than 100 points. ➤ GLM-5.3 is less token efficient than its predecessor. Across the Artificial Analysis Intelligence Index v4.1, GLM-5.3 uses roughly 18,700 output tokens per task, up about 20% from GLM-5.2 (15,700) and 27% more than Kimi K3 (14,700). This would have implications for cost of deployment. ➤ At $0.68 per Intelligence Index task, GLM-5.3 costs 1.5x GLM-5.2's $0.44, but is still cheaper than other models in the same intelligence tier. GLM-5.3 is 19% cheaper per task compared to Kimi K3 ($0.84) and 45% cheaper than GPT-5.6 Sol ($1.23). GLM-5.3’s increase in cost is partially driven by a 20% token usage increase compared to its predecessor. ➤ GLM-5.3 makes an improvement in real-world knowledge accuracy, scoring 14 on AA-Omniscience up from 4 for GLM-5.2. GLM-5.3 is now the second-best open weights model on AA-Omniscience, behind only Kimi K3 (20). A more detailed breakdown of where improvements are made reflects a genuine gain in accuracy as opposed simply more cautious abstention, as accuracy rate (24% to 34%) and attempt rate (46% to 55%) both increased. However, GLM-5.3’s hallucination rate regressed up slightly, from 26% to 30%. Additional model details: ➤ Size: 753B total parameters, 40B active (MoE), unchanged from GLM-5.2 ➤ Context window: 1M tokens ➤ Pricing: $1.40 per 1M input tokens and $4.40 per 1M output tokens. An 81% cache hit discount is applied ($0.26 per 1M cached input tokens) ➤ License: MIT ➤ Accessibility: GLM-5.3 is currently accessibly through Z AI’s first party API. The Z AI team has shared they expect to release the weights shortly Check out the full analysis of GLM-5.3 on Artificial Analysis: artificialanalysis.ai/

DeepSeek-V4-Flash-Vision-Exp is now live on the DeepSeek API Platform! 🚀 🔹 This experimental multimodal model matches DeepSeek-V4-Flash on text capabilities—including agents, reasoning, and world knowledge. 🔹 On multimodal agent benchmarks, V4-Flash-Vision-Exp makes a major leap over V4-Flash, bringing multimodal agent performance close to Opus-4.8. Try it with model='deepseek-v4-flash-vision-exp'. DeepSeek Harness 0.1.1 was released today with out-of-the-box support for the new model. 1/n

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