Superintelligence Plots

OpenRouter’s daily token data show MiniMax’s anonymous Space Bunny Alpha climbing from 1.5% of all tokens on 2026-09-23 to 29.5% on 2026-10-04, 56.7 trillion tokens in total (matching OpenRouter’s own reveal figure), before it left the platform on 2026-10-05. DeepSeek’s combined share averaged 22.1% during Space Bunny’s run (2026-09-24 to 2026-10-04), jumped from 21.0% to 31.1% the day it left, and averaged 29.1% from 2026-10-06 to 2026-10-19; the timing fits users of the free stealth model moving to DeepSeek, though the aggregate data cannot show where individual users went. It matters for two stories in SI News’s MiniMax M3.1 Flash item and its report on the U.S. DeepSeek ban: the relaunched M3.1 Flash took 4.3% of tokens on its first (free) day, and DeepSeek, now barred from federal civilian agency systems, was still OpenRouter’s largest model developer at 31.5% of tokens on 2026-10-19.

Sources: OpenRouter rankings (daily tokens per model from the rankings-daily dataset, CC BY 4.0; Source: OpenRouter (openrouter.ai/rankings), as of 2026-10-20T08:29:15Z), retrieved via the SF·SIX usage snapshot (SF·SIX most-used models page) · OpenRouter, Space Bunny Alpha model page (released 2026-09-23, revealed as MiniMax M3.1 Flash) · MiniMax, M3.1 Flash launch post · DHS order on DeepSeek, Federal Register 2026-21447
Original stories: SI News: Anthropic 5% shares into public benefit fund | MiniMax model M3.1 Flash with 1M context | Hybrid Reasoning Agents · SI News: U.S. bans DeepSeek from federal civilian agency systems
Notes: Shares are each developer’s tokens divided by all OpenRouter tokens that day (top-50 models plus the aggregated long-tail row), UTC days. DeepSeek includes all of its listed models. Token counts come from each provider’s own tokenizer, so cross-developer shares are approximate, and the data cover OpenRouter traffic only, not global use. Space Bunny Alpha and the 2026-10-19 M3.1 Flash listing were free to use.

Across 19 models released between 2024-05-13 and 2026-04-07 in METR’s Time Horizon 1.1 data, the task length a model completes with 80% predicted success is a median 5.3x shorter (range 4.0x to 10.3x) than the length it completes with 50% success. The most recent point, an early Claude Mythos Preview, reaches 17.4 human-expert hours at 50% but only 3.1 hours at 80%, and METR cautions that estimates above 16 hours are unreliable. The gap matters because attackers can tolerate frequent failure: in the AI-agent hacking campaign covered in today’s SI News brief, Reuters reports that the agents attempted more than 1,200 sites and compromised more than 100, while businesses deploying agents generally need the reliable end of the curve.

Sources: METR, Task-Completion Time Horizons of Frontier AI Models (Time Horizon 1.1 data file; p50 and p80 horizon estimates and release dates) · Reuters (republished by RTÉ) · NorForge, Inside Cairn
Original story: SI News: Agents that hack, Anthropic’s public-benefit fund, Disney’s Character.AI hire
Note: METR’s horizon measures how long the tasks take human experts, not how long an agent runs. Points are point estimates; METR’s confidence intervals are wide (Claude Mythos Preview at 50%: 8.5 to 55 hours).