Open Weight Models

Summary: DeepSeek V4.1 Flash in 30 Seconds DeepSeek-V4.1-Flash shipped on September 10, 2026. License is MIT, weights are on Hugging Face. 552B backbone parameters, but only 8B active while reading input and 16B while generating output. That asymmetry is the whole point of the model. New architecture: Causal Encoder-Decoder (CED). 40 layers, 20 causal encoder plus 20 decoder. The decoder’s global KV cache is projected from the encoder’s final hidden states instead of from each decoder layer’s own. Result: a global KV cache of 890 bytes per token. One quarter of V4-Flash, and 1/437 of DeepSeek-V1. Natively multimodal: images go through DeepSeek-ViT, a vision encoder trained from scratch, from the very start of language-model pre-training. Pre-training corpus is 45T tokens. Beats Claude Opus 5 on several agentic benchmarks: 90.6 vs 89.1 on Terminal-Bench 2.1, 74.2 vs 74.0 on DeepSWE v1.1, 54.8 vs 50.3 on AutomationBench. Reasoning effort is a continuous 1-100 dial, not an on/off thinking toggle. API pricing is $0.15 in / $0.60 out per million tokens off-peak, double that at peak. Roughly one twentieth of Claude Opus 5’s output price. DeepSeek is retiring V4 Pro: from September 14, 2026, deepseek-v4-pro requests get routed to V4.1 Flash and billed at Flash rates. Everyone in the open-weight race this year is chasing the same two numbers: active parameters and KV cache. The first decides what each token costs, the second decides how much memory a long context eats. GLM-5.3-Flash answered with hybrid attention, Qwen3.8-Flash-Next with 6B active parameters. ...

Summary: DeepSeek V4 Pro in 30 Seconds DeepSeek V4 Pro 0813 went generally available on August 13, 2026. No press release, no blog post, just one line in a changelog. 1.7 trillion parameters, mixture-of-experts architecture, 1M token context, 384K token output ceiling. The weights are on Hugging Face under the MIT license. You can download them, modify them and ship them in a commercial product. Official scores are bold: 87.9 on Terminal Bench 2.1, 62.7 on DeepSWE. Independent testing is more restrained: 53 on the Artificial Analysis Intelligence Index. Pricing is $0.435 in and $0.87 out per million tokens. From August 16 the off-peak rate is half of that. The catch: its smaller sibling V4 Flash scores 52 on the same index at one third of the price. You are paying triple for one point. Model launches have settled into a ritual: a teaser video, a wall of benchmark charts, an excited founder post. DeepSeek skipped all of it. DeepSeek V4 Pro 0813 went generally available on August 13, 2026 with no announcement at all. One line landed in the API changelog and the weights appeared on Hugging Face. ...

Summary: Muse Glimmer in 30 Seconds Muse Glimmer is a 30 billion parameter agentic model released by Meta Superintelligence Labs on August 10, 2026. The weights are open under Apache 2.0. No cloud needed. Four-bit quantization shrinks the language model to under 20 GB, so it runs on a single consumer GPU with 24 GB or 32 GB of memory. It reads text and images, and returns text. Default context window is 128K tokens, and the model supports more. It clears its size class on agentic tests: 75.5 on MCP Atlas (Gemma4-31B 54.2, Qwen3.6-27B 62.5). On math, 94.7 on AIME 2026. The bundled DFlash drafter speeds generation up 3.1x on an RTX 5090 and 1.8x on an M5 Max. It is distilled from Muse Spark, Meta’s cloud model, so the big model’s agentic behavior is transferred into a small one. On the same day Zuckerberg announced that the weights for Muse Spark 1.2, Meta’s flagship model, will be opened too. No date, just “soon.” An AI agent normally means an API key, an internet connection and a bill that ticks up with every request. Meta just tried the opposite. ...

Summary: Qwen3.8-Max in 30 Seconds Qwen3.8-Max is Alibaba’s new flagship model, made generally available on August 2, 2026. 2.4 trillion parameters, 95 billion active (MoE architecture). It reads text, images and video, and returns text. Context window is in the 1 million token class. On one task it ran 125 hours (about 5 days) with no human input, rebuilding an experiment from a machine learning paper from scratch, confirming its six findings, then inventing a method that beats the paper. It beats Claude Opus 4.8 on most coding and agent tests, trades blows with Claude Fable 5 and GPT-5.6 Sol, and falls behind on some. API pricing is $2 input / $6 output per million tokens. Repeated input costs $0.25. This is the first time Alibaba has open-weighted a Max-class model. The weights landed on Hugging Face on August 12, 2026, though under Alibaba’s own Qwen3.8-Max license rather than Apache 2.0. Two days later, on August 14, Qwen3.8-27B followed: a dense 27B model under Apache 2.0 that fits on a single GPU. Ask an AI to “rebuild the experiment in this paper, then improve on it” and it normally stalls after a few turns, waiting for you to step in and steer. ...

TL;DR: Kimi K3 in 30 Seconds Kimi K3 is the new AI model Chinese company Moonshot AI announced on July 16, 2026; at 2.8 trillion parameters, it is the largest open source model released so far. In a single 48-hour run it designed a working chip, finished an astrophysics study in 2 hours instead of 2 weeks, and cut a teaser video from 56 raw clips. It reads 1 million tokens at once: roughly like reading the entire Lord of the Rings trilogy in one sitting and remembering all of it. On coding it plays in the same league as the strongest Claude and ChatGPT models, and beats them on some tests. It falls behind on hard knowledge questions. You can try it in the Kimi app and on kimi.com. The model files go public on July 27, 2026. Ask an AI to design a chip for you and what happens? It probably writes you a nice article about how chips are designed. ...