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Kimi K2.5 (Agent Swarm, beats GPT-5) now on RouterLab (Swiss hosting)

Hacker News

Kimi K2.5 (Agent Swarm, beats GPT-5) now on RouterLab (Swiss hosting)

Hi HN! Moonshot AI released Kimi K2.5 today, and we integrated it on RouterLab within hours. Why this matters: *Open source beats proprietary:* • Kimi K2.5: 50.2% on HLE (Humanity's Last Exam) • GPT-5: 41.7% • Claude 4.5: 32.0% First time an open-source model beats GPT-5 on expert-level reasoning. *Agent Swarm architecture:* • Orchestrates up to 100 parallel agents • 1,500 simultaneous tool calls • 4.5x faster than sequential execution • Autonomous task decomposition Example: "Analyze 50 competitors and create a report" → Creates 50 research agents → Parallel execution → Compiled report in minutes *Technical specs:* • 1T parameters (32B activated) • 384 experts MoE • INT4 quantization native • 256k context window • Open weights on Hugging Face *Benchmarks:* • HLE (reasoning): 50.2% (GPT-5: 41.7%) • BrowseComp (web nav): 60.2% (GPT-5: 54.9%) • SWE-Bench (coding): 71.3% • VideoMMMU: 86.6% *Pricing:* • $0.60/$3.00 per 1M tokens • 5x cheaper than GPT-5 We're a Swiss company (Eyelo SA, founded 1983) and integrated Kimi K2.5 on RouterLab with Swiss/German hosting for GDPR compliance. OpenAI-compatible API, migration is 2 lines of code: ```python client = OpenAI( base_url=" https://routerlab.ch/v1 ", api_key="your-key" ) I wrote a technical analysis: https://medium.com/@comeback01/kimi-k2-5-the-agent-swarm-rev... Try it: https://routerlab.ch/blog/kimi-k2-5 (14-day free trial) Happy to answer technical questions about Agent Swarm, MoE architecture, or deployment!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: para, compatible · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released
46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: video, para · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, calls · Missing: plus, platform, intuitive
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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