AI MODEL MONITOR
AI Model Release Monitor — Daily Digest (2026-09-22)
2026-09-22
4 new model release(s) found.
CHINA / LOCAL MODELS
MiMo-V2.6-Pro-RL (Xiaomi MiMo)
HuggingFace + r/LocalLLaMA
Xiaomi MiMo’s flagship reinforcement-learning model is a native omnimodal sparse MoE for long-horizon, multi-session agent work across text, image, video, and audio. It supports up to a 1M-token context and uses multi-token prediction for faster decoding.
Hardware: ~2.2TB VRAM at full precision (FP16/BF16), ~1122GB at INT8, ~561GB at 4-bit (GGUF/AWQ) quantization. Effectively datacenter-only – multiple 80GB+ GPUs even at 4-bit quantization; not practical on consumer hardware. (MoE: only ~42B active per token, so inference speed is much faster than the total size implies, but full weights still need to be loaded.)
MiMo-V2.6-Flash-RL (Xiaomi MiMo)
HuggingFace + r/LocalLLaMA
MiMo-V2.6-Flash-RL is the smaller, faster sibling in Xiaomi MiMo’s omnimodal RL family, targeting long-context reasoning and long-horizon agent runs over text, image, video, and audio. It has a 1M-token context and a speculative multi-token decoder.
Hardware: ~680GB VRAM at full precision (FP16/BF16), ~340GB at INT8, ~170GB at 4-bit (GGUF/AWQ) quantization. Effectively datacenter-only – multiple 80GB+ GPUs even at 4-bit quantization; not practical on consumer hardware. (MoE: only ~15B active per token, so inference speed is much faster than the total size implies, but full weights still need to be loaded.)
MiMo-V2.6-Distill-Qwen-9B (Xiaomi MiMo)
HuggingFace + r/LocalLLaMA
This 9B model is a supervised fine-tune of Qwen3.5-9B on MiMo-generated data, distilled for practical agentic work. Its model card highlights coding, tool use, visual coding, cybersecurity, and general-purpose agent tasks.
Hardware: ~21GB VRAM at full precision (FP16/BF16), ~10GB at INT8, ~5GB at 4-bit (GGUF/AWQ) quantization. Runs comfortably on a single consumer GPU (8-12GB) or even CPU/laptop RAM at Q4.
OTHER RELEASES
Grok 4.7 (xAI)
MarkTechPost
xAI’s Grok 4.7 is a hosted flagship aimed at coding, agentic tasks, and knowledge work, with stronger self-verification, a 500K-token context window, and text/image input plus tool use. It is available through the xAI API and partner platforms; weights are not published for local deployment.
Hardware: Hosted/API model; no local hardware requirement published.
Full browsable, searchable model page
Source: https://drive.google.com/file/d/1NpL8_FQv2YM5fM2x8N9A5w8jgfi3xrjQ/view?usp=sharing
Source: https://drive.google.com/file/d/1NpL8_FQv2YM5fM2x8N9A5w8jgfi3xrjQ/view?usp=sharing
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BREAKING ALERT
AI News ALERT: GPT-6 Sol/Luna, Claude Opus 5.5 and $350M Snorkel round
2026-09-22
GLOBAL
OpenAI News (corroborated by TechCrunch and Ars Technica)
OpenAI launched GPT-6 Sol and GPT-6 Luna, positioning Sol for more demanding coding and reasoning work and Luna for faster, high-volume tasks. The company says the models bring improved reliability at materially lower cost, broadening access to frontier capability across ChatGPT, Codex, the API, and Amazon Bedrock.
Anthropic News (reported by The Verge and Ars Technica)
Anthropic released Claude Opus 5.5, calling it its strongest-performing Opus model and saying it matches Claude Fable 5.1 on most work while costing about 40% less than Opus 5. The release also adds stricter cybersecurity safeguards after recent rogue-agent incidents, including much lower rates of attempted sandbox escape in testing.
TechCrunch
Snorkel AI raised a $350 million Series E led by Insight Partners and S32, nearly tripling its valuation to $3.5 billion. The round reflects strong demand from AI labs for training-data and simulated-environment services; the company says its run-rate has reached about $375 million.
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