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AI Tracker Bot@aitrackerbot·
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Meituan ships LongCat-2.5, a 1.6T-parameter (about 48B active) native multimodal agentic model, with API pricing of $0.30/$1.20 per 1M tokens

On Nov 2 Meituan's LongCat team listed LongCat-2.5 in its API platform change log as the full release of September 25's LongCat-2.5-Preview. The official technical blog says it has 1.6 trillion total parameters with about 48 billion activated per token, builds on LongCat-2.0 with stronger native visual understanding and agentic coding, and was trained and served with W4A4 inference on domestic AI accelerators. Vendor-reported scores include 88.4 on Terminal Bench v2.1, 38.5 on Terminal Bench v4.0, 91.9 on BrowseComp and 83.1 on MCP Atlas. The API speaks both OpenAI and Anthropic formats with a 1M-token context and up to 128K output tokens, at a limited-time price of $0.30 per 1M uncached input tokens, $0.006 cached input and $1.20 output.

Meituan ships LongCat-2.5, a 1.6T-parameter (about 48B active) native multimodal agentic model, with API pricing of $0.30/$1.20 per 1M tokens

Key Takeaways

  • Scale: a 1.6-trillion-parameter MoE with about 48B active per token, a 1M-token context and up to 128K output tokens (official blog, API docs)
  • Code agents (vendor-reported): Terminal Bench v2.1 88.4 (Claude Opus 5 89.1, GPT-5.6-Sol 88.8), Terminal Bench v4.0 38.5 (GPT-5.6-Sol 37.3, Claude Opus 5 53.9), DeepSWE v1.1 64.6
  • General agents (vendor-reported): BrowseComp 91.9, MCP Atlas 83.1, AutomationBench v1.0.6 51.8, HLE with tools 63.1; AutomationBench jumps from 15.8 on LongCat-2.0
  • Pricing: limited-time $0.30 per 1M uncached input tokens, $0.006 cached input and $1.20 output (pricing page)
  • Engineering: W4A4 inference raises prefill throughput 43.3% and decode throughput 42.77% over a W8A8 baseline; FSDP gives 86% higher per-device throughput than DDP in long-sequence SFT
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Linked Models & Agents:
⚡ LongCat-2.5⚡ LongCat-2.5-Preview⚡ LongCat-2.0⚡ Claude Opus 5⚡ GPT-5.6 Sol⚡ DeepSeek-V4.1-Flash🤖 Claude Code🤖 Codex🤖 Hermes Agent🤖 OpenCode🤖 Cline🌐 Meituan
#LongCat-2.5#Meituan#MoE#Multimodal#Agents#Pricing
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SenseTime@SenseTime_AI·
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SenseTime open-sources SenseNova-Loop-9B-Preview, a 9B looped Transformer with about 14.6B inference parameters for long-context and agentic work

On Oct 30, SenseTime released SenseNova-Loop-9B-Preview (Apache 2.0) on its official X account and Hugging Face. It runs layers 4 to 27 of a 32-layer decoder twice with the same weights, so each forward pass executes 56 layer computations while storing about 9B parameters, about 14.6B at inference, and it uses two-stream mHC (manifold-constrained hyper-connections) to manage the residual streams. It takes text and images, with training sequences up to 262,144 tokens. SenseTime charts show it beating Qwen3.5 and Qwen3.6 peers on BIRD-Dev, WorkBuddy Bench, and other agent and data tasks, while it still trails on Terminal Bench 2.1; the technical report is not out yet. The official launch post drew about 910 likes.

SenseTime open-sources SenseNova-Loop-9B-Preview, a 9B looped Transformer with about 14.6B inference parameters for long-context and agentic work

Key Takeaways

  • Structure: 32 distinct decoder layers, with layers 4 to 27 looped twice on shared weights for 56 layer computations per forward pass; about 9B stored and about 14.6B inference parameters (model card)
  • Vendor-reported scores: BIRD-Dev 71.38 (Qwen3.5-27B 69.84), WorkBuddy Bench 66.89 (Qwen3.6-35B-A3B 61.98), AutomationBench 13.72 (Qwen3.5-27B 10.17)
  • Weak spots from the same vendor chart: Terminal Bench 2.1 at 40.45, below Qwen3.6-27B at 47.19; tau3-Banking at 18.56, slightly below Qwen3.5-27B at 19.59
  • Training context up to 262,144 tokens; hybrid attention uses three linear-attention layers plus one full-attention layer per four-layer group; MTP speedup is available, DSpark and DFlash2 are still training
  • Apache 2.0, requires trust_remote_code=True; official examples cover Transformers 5.18.0, a 4-GPU LightLLM setup, and an SGLang PR, and the technical report is still to come
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Linked Models & Agents:
⚡ SenseNova-Loop-9B-Preview⚡ Qwen3.5-27B⚡ Qwen3.6-35B-A3B🌐 SenseTime
#SenseNova#SenseTime#looped transformer#mHC#open weights#Agent#Multimodal
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OpenRouter@OpenRouter·
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OpenRouter ships Model Router Benchmarks: 7 routers vs 6 suites on quality, speed, and cost

On 2 Oct 2026 OpenRouter shipped Model Router Benchmarks: seven model routers (Auto, Jev, NVIDIA Switchyard, Unbiased Pareto, Sakana Fugu, and variants) compared on GPQA Diamond, Tau3 Banking, MMMU Pro Vision, SWE Atlas QA, Deep SWE, and Terminal Bench 2.1. The Router Index defaults to quality 60% / time 20% / cost 20%. openrouter/pareto-code and Fusion are not on this board.

OpenRouter ships Model Router Benchmarks: 7 routers vs 6 suites on quality, speed, and cost

Key Takeaways

  • Announced 2026-10-02 22:08 UTC; blog by Brian Thomas dated 10/2/2026; live page openrouter.ai/benchmarks/routers
  • Seven router slugs in the public stats feed: openrouter/auto, typesafe/jev-router, nvidia/switchyard, unbiased/pareto, sakana/fugu-max, sakana/fugu-ultra, sakana/fugu-ultra-v2. pareto-code and Fusion were not benchmarked
  • Router Index default is quality 60% / time 20% / cost 20% on a 0-10 scale; it is not raw accuracy
  • Stats API 2026-10-04 SGT, Deep SWE n=113: Switchyard Flash+Opus 5.5 and Opus-only both 83/113 (~$279 / $285); Flash+GPT-6 Sol 74/113 at ~$55
  • Terminal Bench 2.1 n=89: GPT-6 Astra 77/89 ($66.50), Opus 5.5 74/89 ($24.00); one Pareto run 71/89 ($27.20)
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Linked Models & Agents:
⚡ openrouter/auto⚡ typesafe/jev-router⚡ nvidia/switchyard⚡ unbiased/pareto⚡ sakana/fugu-ultra-v2⚡ openrouter/pareto-code🌐 OpenRouter🌐 NVIDIA🌐 Sakana🌐 TypeSafe🌐 Unbiased
#Openrouter#Model router#Benchmark#Jev#Switchyard#Pareto#Fugu#Auto router#Coding agents
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