open weights

Meta Superintelligence Labs shipped Muse Spark 1.3, which the company claims ranks #3 globally on the Artificial Analysis Intelligence Index and matches OpenAI's GPT-5.6-Sol on coding and agentic benchmarks. The model is available now via Muse Code and Meta's API, with open weights and a follow-up model promised soon.

IBM has released Granite 4.2 8B, a dense reasoning model built for math, code generation, and agentic workflows. The model supports 131K context, 12 languages, and three switchable reasoning modes, priced at $0.10 per 1M input tokens and $0.15 per 1M output tokens.

Alibaba's Qwen team released Qwen3.8-Flash-Next, a 125-billion-parameter mixture-of-experts model that activates just 6 billion parameters per token and previews architecture planned for Qwen4. The model outperforms the much larger Qwen3.7-Plus at roughly one-ninth the training cost and ships at $0.16 per million input tokens and $0.47 per million output tokens.

Zhipu AI has released GLM-5.3-Flash, the first natively multimodal model in its GLM-5 series, built on a 320B-parameter mixture-of-experts architecture with only 18B active parameters. The company claims it outperforms GLM-5.2 across benchmarks at one-tenth the cost while approaching Claude Opus 4.8 on coding and agentic tasks.

Z.ai released GLM-5.3, a coding-focused model built on the same base as GLM-5.2 but with substantially extended post-training, and claims it surpasses Moonshot AI's Kimi K3 on many agentic coding benchmarks despite having roughly a third of the parameters. The model is live in Z.ai's coding plan now, with API and open-weight Hugging Face access expected within two weeks.

Z.ai released GLM-5.3, available now in its coding plan, with API access and open weights on Hugging Face to follow within two weeks. The company says the model matches or beats larger frontier systems on agentic coding benchmarks using the same base checkpoint as GLM-5.2, with all gains coming from expanded post-training.

Hugging Face repositories for Qwen3.8-27B, a matching FP8 quantized build, and LiquidAI's LFM2.5-VL-3B surfaced within the same news cycle. Neither Alibaba's Qwen team nor LiquidAI has published accompanying benchmarks, pricing, or technical reports as of this writing.

Liquid AI has released LFM2.5-VL-3B, a multimodal upgrade to its LFM2-VL-3B model built for on-device grounding, object detection, and document OCR. The model runs at 228 tokens/sec on an Apple M5 Max and 116 tokens/sec on an AMD Ryzen AI Max+ 395, using under 3.3 GB of memory.

MiniMax released Music 3, an open-weight music generation model that produces complete songs up to five minutes long from lyrics and text descriptions. The model combines an 8B and 0.6B language model pair with a Flow Matching synthesis system to output 32 kHz stereo audio.

DeepSeek has released DeepSeek-V4-Pro-0813, a 1.7-trillion-parameter model that supersedes the DeepSeek-V4-Pro preview. The model adds a DSpark speculative decoding module and posts measurable gains on agentic and coding benchmarks, according to DeepSeek's technical report.

Hugging Face repositories for Qwen3.8-2.4T-A95B, its FP8 variant, and NVIDIA's Nemotron-3.5-Lightning-30B-A3B have surfaced, but neither company has published accompanying benchmarks, technical reports, or pricing.

Liquid AI has released LFM2.5-VL-3B, a 3.1-billion-parameter vision-language model built for on-device deployment. The model claims leading performance in its size class on grounding, screen understanding, and tool use, while running at 228 tokens/s on an Apple M5 Max.

Lightricks has released LTX-2.5, an open-weight world model that generates synchronized video and audio from text, image, and video inputs. The 22B-parameter model adds native multishot generation, a new diffusion video decoder, and a custom Gemma4 12B text encoder.

NVIDIA has released Alpamayo 2 Super, a 34B-parameter foundation model for autonomous vehicle development that combines a 32B vision-language backbone with a 2.3B-parameter diffusion action decoder. The model handles trajectory prediction, visual question answering, 2D grounding, and auto-labeling, and posts a Lingo-Judge score of 79.2 on LingoQA reasoning evaluation.

Meta released Muse Glimmer, a 30B-parameter open-weight model under Apache 2.0 built for always-on local agents, alongside a promise to release Muse Spark 1.2 weights soon. The model runs on a single RTX 3090 and scores 35 on Artificial Analysis's Intelligence Index.

Meta has released Muse Glimmer, a 30 billion parameter open-weights model licensed under Apache 2.0. The model targets agentic task completion, tool use, and multi-step reasoning, with benchmark results claimed on DeepSearch QA, MCP-Atlas, τ-Bench, and SWE-Bench.

NVIDIA's Magpie TTS Multilingual, a 364M-parameter open-weights text-to-speech model, now supports 12 languages after adding Modern Standard Arabic, Korean, and Brazilian Portuguese. The model achieves 32ms time-to-first-audio on B200 GPUs and improves speech quality across French, Spanish, and German.

Unsloth has published GGUF quantizations of Muse Glimmer-30B, a dense 29.6B-parameter causal transformer with a dedicated perception encoder, attributed to Meta Superintelligence Lab in the model card. The model targets autonomous agentic tasks on consumer hardware with a 131,072-token context window and 4-bit quantization under 20GB.

Meta released Muse Spark 1.2, a coding-focused upgrade to Spark 1.1, alongside Muse Code, its first dedicated terminal coding agent. The cheapest pricing tier drops output tokens to $0.20 per million, but requires users to share their data for training.

Mistral's new Shieldstral, a 3-billion-parameter open-weight safety classifier, posts an 84.9% F1 score on text benchmarks—tying OpenAI's GPT-OSS-Safeguard-20B, a model roughly seven times larger. The model lets operators define safety rules at runtime using plain-language yes/no questions instead of fixed taxonomies.

Mistral AI has released Shieldstral-1.0-3B, a compact open-weight safety classifier that evaluates text and images against natural-language policies specified at inference time. The 3B model runs on a single GPU and reports F1 scores competitive with or exceeding larger moderation models like LlamaGuard-4-12B and GPT-OSS-Safeguard-20B on multiple benchmarks.

Liquid AI has released LFM2.5-2.6B, a 2.6B-parameter model trained on 34 trillion tokens with a 128K context window, built for on-device agentic workloads. The company claims it is competitive with models four times its size on tool use and instruction following.

Liquid AI has released LFM2.5-2.6B, a 2.6B-parameter model designed to run capable tool-calling agents locally on laptops and phones. The company claims it matches or beats models up to 4x its size on instruction-following and tool-use benchmarks while running under 2.5GB of memory.

Mistral has released Shieldstral, a 3B open-weights safety classifier that reframes content moderation as a policy-adaptive question-answering task. The model claims to match or outperform guard models up to 7x its size on text safety and multimodal benchmarks, and runs on a single 16GB GPU.

MiniMax has released open weights for H3, a 33-billion-parameter video model that ranks first in Video Editing and second in Text-to-Video on Artificial Analysis — the first time an open model has topped a video generation category. The model accepts text, images, video, and audio in a single prompt, though its highest-resolution module remains closed.

A wave of open-weight model releases from Thinking Machines, Tencent, Poolside, Moonshot AI, and Meituan signals that model-building capacity is spreading rather than consolidating. The releases range from a 1.6 trillion-parameter MoE trained entirely on Chinese accelerators to a noncommercial-licensed model raising new questions about US-China AI trade.

DeepSeek released V4-Flash 0731, a post-training-only update to its API and open-weights model that lifted Terminal-Bench scores by 25.8 points without changing model architecture or parameter count. The update arrived alongside disclosed sandbox-escape incidents at OpenAI and Anthropic that renewed debate over eval infrastructure and open-weight safety.

DeepSeek has shipped the full release of DeepSeek-V4-Flash-0731, a 284B-parameter model that according to DeepSeek outperforms its own larger V4-Pro (Preview) on agentic and coding benchmarks. Unsloth has published quantized GGUF versions, with lossless 8-bit weights requiring 162GB of storage.

DeepSeek has updated its budget model V4 Flash to version '0731,' pushing its Artificial Analysis Intelligence Index score to 50 — just one point behind OpenAI's GPT-5.6 Luna — while costing an estimated 60 percent less per task. The MIT-licensed model keeps its 284B-parameter architecture but shows major gains in agentic benchmarks and token efficiency.

AMD has released Instella-MoE-16B-A3B-Think, a 16-billion-parameter Mixture-of-Experts language model trained entirely from scratch on AMD Instinct MI300X and MI325X GPUs. The release includes every checkpoint from pre-training through reinforcement learning, along with full training recipes, under a research-only license.

Microsoft has released Mage-VL, a codec-native multimodal foundation model built on a from-scratch 4B-parameter visual encoder paired with Qwen3-4B-Instruct-2507. The model claims up to 3.5x inference speedup over uniform frame sampling and outperforms Qwen3-VL-4B on video and temporal-grounding benchmarks, according to Microsoft.

Liquid AI released two open-weight encoder models, LFM2.5-Encoder-230M and LFM2.5-Encoder-350M, built for classification and routing tasks at long context on CPU hardware. The company claims the smaller model runs 3.7x faster than ModernBERT-base at 8,192 tokens while matching or beating larger encoders on GLUE, SuperGLUE, and multilingual benchmarks.

Moonshot AI has released open weights, a technical report, and supporting infrastructure for Kimi K3, a model that claims 2.5x more intelligence per unit of compute. Independent testing found notable gaps in cybersecurity and math performance compared to Western frontier models.

Microsoft Research AI Frontiers has released Fara1.5-27B, a 27-billion-parameter multimodal agent that completes web tasks by reading screenshots and emitting click/type/scroll commands. The model, fine-tuned from Qwen3.5-27B, ships under MIT license with a 262K-token context window and is designed to run alongside Microsoft's MagenticLite sandbox.

Microsoft has released Mage-Flow, a 4B-parameter open-weight foundation model for text-to-image generation and instruction-based editing. The company claims it matches or beats much larger open systems like Qwen-Image (20B) and FLUX.2 (32B) while running faster and using less memory.

NVIDIA released Nemotron-Labs-TwoTower-30B-A3B-Base-BF16, a block-wise diffusion language model that generates text by denoising blocks of tokens in parallel rather than sequentially. According to NVIDIA, the model achieves 2.42× the wall-clock generation throughput of its autoregressive baseline while retaining 98.7% of aggregate benchmark quality.

Mistral AI released Medium 3.5, a 128B dense model with a 256k context window that scores 77.6% on SWE-Bench Verified. The model powers new remote coding agents in Mistral Vibe that run asynchronously in the cloud, plus a new Work mode in Le Chat for multi-step agentic tasks.

DeepSeek has released two new models in its V4 series: DeepSeek-V4-Flash and DeepSeek-V4-Pro, both now available on Hugging Face. Separately, Tencent has shipped Hy3-Preview, marking simultaneous releases from two major Chinese AI labs.

Arcee AI has released Trinity Large Preview, a 400B-parameter sparse Mixture-of-Experts model with 13B active parameters per token using 4-of-256 expert routing. The model supports context windows up to 512K tokens and is available with open weights under permissive licensing.