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Models2026-09-22 · source-backed
Artificial Analysis ranked it 46 on the Intelligence Index, #1 of 114, ahead of Kimi K3 at 44 and GLM-5.3 at 45, with leading closed models at 53. Natively omnimodal MoE, 1M context, weights on Hugging Face, $0.435 per million input and $0.87 per million output. The companion MiMo-V2.6-Distill-Qwen-9B distills the frontier model into a Qwen 9B body and ships with a research package reported to contain more than 7,000 RL tasks covering vulnerability reproduction and knowledge work, explicitly framed for small-lab RL research rather than production. First frontier-class open weights from a phone maker rather than one of the six established Chinese labs. (Latent Space, r/LocalLLaMA)
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xAI released Grok 4.7 on September 21 on a new 2.1-trillion-parameter base, a 40% jump over Grok 4.6's 1.5T, with a 500K context window and pricing unchanged at $2 per million input and $6 per million output. Published scores: 46.3% on CursorBench 4.0 (up from 40.4%), 71.0% on...
Xiaomi released MiMo-V2.5-Pro, a 1.02 trillion parameter mixture-of-experts model (42B active) with 1M token context, fully MIT licensed. In benchmarks, it achieves 63.8% success on agentic tasks using 40-60% fewer tokens than Claude Opus 4.6 or GPT-5.4 for comparable results....
This is a supply-chain fact, and most people are still treating it as a geopolitics argument. Sequoia published "America's Open-Model Paradox" on July 24 with the number that reframes the whole conversation: Qwen's share of open-model fine-tunes went from 1% in January 2024 to...
Mira Murati's lab finally shipped a full LLM, and it's Apache 2.0. Inkling is 975B total parameters with 41B active in a MoE configuration, multimodal on input (text, image, audio) and text out, trained on 45 trillion tokens. The context number is the fun part: 1M tokens in th...
Google DeepMind released Gemma 4 on April 2 with four model sizes (E2B, E4B, 26B MoE, 31B Dense) under Apache 2.0. Multimodal (text, vision, audio). 256K context. Native thinking and tool-calling optimized for agentic workflows. Day-zero ecosystem support across vLLM, llama.cp...
Moonshot AI dropped Kimi K2.7-Code on Hugging Face on June 12. The specs are loud: 1T-parameter MoE with 32B active across 384 experts, a 256K context window, Modified MIT license, tuned for long-horizon agentic software engineering (MarkTechPost). Moonshot reports +21.8% on K...
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