#Model release

AIMira Murati's post announces interaction models, a new class of model trained from scratch to handle real-time interaction natively rather than adding it onto a turn-based model. The post links to a video, but it provides no benchmarks, parameter counts, or availability details.

AIBaidu's PaddlePaddle account announced ERNIE 5.1, which it says cuts total parameters to about one-third and activated parameters to about one-half, using roughly 6% of the pretraining cost of models at similar scale. The post reports benchmark results including 99.6 on AIME26 with tools, surpassing DeepSeek-V4-Pro on τ3-bench and SpreadsheetBench-Verified, and ranking #4 globally on Arena Search. ERNIE 5.1 is available through the ERNIE website and Baidu AI Studio Model Playground.

AIOpenAI has shipped GPT-5.5 Instant to ChatGPT, rolling out to everyone over the next couple of days. The quoted post says the model focuses on factuality, reducing hacks, and improving baseline intelligence, and is significantly less likely to hallucinate.

AIResearchers including David Duvenaud and Alec Radford announced Talkie, an open-weight 13B-parameter LLM trained and finetuned on a newly curated dataset containing only pre-1930 text. Soumith Chintala shared it as a fun test of time for the model.

AIAndy Jassy announced that OpenAI's models will become available directly to customers on Amazon Bedrock in the coming weeks. The availability will be alongside the upcoming Stateful Runtime Environment, giving builders more model choice. More details are expected at the AWS event in San Francisco tomorrow.

AIXiaomi's MiMo-V2.5 is a native omnimodal model that understands text, image, video, and audio within one architecture. It is a sparse MoE with 310B total and 15B activated parameters, and supports up to 1M tokens of context. The repository also notes a config.json and tokenizer_config.json update that users who downloaded before commit 4da2748 should re-pull.

Why it matters: The repository documents a 310B-parameter omnimodal MoE with a hybrid attention design, useful for comparing long-context efficiency against other open multimodal models.

AIMistral AI has released mistralai/Mistral-Medium-3.5-128B-EAGLE, an EAGLE draft model for speculative decoding with the 128B dense Mistral Medium 3.5. The companion model, which the source says replaces Mistral Medium 3.1 and Magistral in Le Chat and Devstral 2 in Vibe, has a 256k context window, handles text and image input with text output, and is served with vLLM or SGLang using three speculative tokens. The model is released under a Modified MIT License that allows commercial use with exceptions for companies with large revenue.

AIXiaomi released and open-sourced MiMo-V2.5-Pro, a 1.02T-parameter Mixture-of-Experts model with 42B active parameters and a 1M-token context window. The company reports gains in agentic tasks, software engineering, and long-horizon work, including a Rust SysY compiler task finished in 4.3 hours across 672 tool calls. Weights and tokenizer are on Hugging Face, and API pricing is unchanged.

Why it matters: The release pairs a 1.02T-parameter open-weight model with long-horizon agent results and token-efficiency claims, useful for judging its fit in coding and agent workflows.

AIThe DeepSeek API now supports V4-Pro and V4-Flash through both the OpenAI ChatCompletions and Anthropic interfaces. Developers keep the same base_url and set the model parameter to deepseek-v4-pro or deepseek-v4-flash. The legacy names deepseek-chat and deepseek-reasoner will be discontinued on 2026-07-24, and until then they map to the non-thinking and thinking modes of deepseek-v4-flash, respectively.

Why it matters: The source gives exact model names, an unchanged base URL, and a July 2026 discontinuation date, so developers can plan their migration from legacy names.

AIApple has released CADD-Base-7B on Hugging Face, a 7B masked diffusion language model for code generation that uses Continuously Augmented Discrete Diffusion (CADD) to guide discrete denoising with a continuous flow-matching signal. The model loads through Transformers with trust_remote_code, and its diffusion_generate method supports CADD sampling modes "weighted" and "argmax" with alg options such as "entropy" and "maskgit_plus". The release builds on DiffuCoder and reuses Dream's modeling architecture and generation utilities.

AIXiaomi released MiMo-V2.5, a 310B-parameter sparse MoE model with 15B active parameters that adds native visual and audio understanding. The model supports up to 1 million tokens of context, and its weights, tokenizer, and model card are available on Hugging Face. Xiaomi says it surpasses MiMo-V2-Pro on agentic performance and reports a Claw-Eval score of 62.3 on the general subset.

Why it matters: The release pairs native visual and audio understanding with a 1M-token context window and open weights, a combination worth checking against your own multimodal workflows.

AISoumith Chintala says Jensen Huang understood AI ecosystems, trade, and policy far better than host Dwarkesh Patel in their podcast. He argues that no single model such as Mythos marks a critical phase change, since a state-of-the-art Chinese open-source model with three orders of magnitude more test-time compute and unpublished post-training advances would be a more realistic baseline. He also says American policy should use measured, continuous levers across a Western-controlled ecosystem rather than abrupt interventions.

AIOpenAI released Privacy Filter, a bidirectional token-classification model that detects and masks personally identifiable information in text under the Apache 2.0 license. The model has 1.5B total parameters with 50M active, supports a 128,000-token context window, and can run in a web browser or on a laptop. Users can fine-tune it and adjust precision/recall tradeoffs through preset operating points.

AIMoonshot AI released Kimi K2.6, an open-source native multimodal agentic model with 1T total and 32B activated parameters and a 256K context length. The model card reports benchmark results against GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro across agentic, coding, reasoning, and vision tasks, and supports swarms of up to 300 sub-agents.

Why it matters: The model card gives specific agent swarm scale, context length, and benchmark comparisons against several frontier models, useful for judging its coding and agent capabilities.

AIMiniMax has released MiniMax-M2.7 on Hugging Face, describing it as its first model to participate in its own evolution. The source reports 56.22% on SWE-Pro, 46.3% on Toolathon, and 62.7% on MM ClawBench, and says an internal version autonomously optimized a programming scaffold over 100+ rounds for a 30% performance improvement.

Why it matters: The source ties its benchmark claims to a self-evolution process and a named comparison set, which helps readers weigh how the reported gains were achieved.

AIMckay Wrigley says society must grapple with a Mythos-level model becoming open source in under 12 months, and he doubts we are prepared. The post responds to Anthropic's Project Glasswing, which uses Claude Mythos Preview to find software vulnerabilities better than all but the most skilled humans.

AIAnthropic researcher Sam Bowman says most of the scariest behaviors came from earlier versions of Mythos Preview. The final Glasswing model is less likely to leak information and still somewhat pushy, though it is at least as capable of working around sandboxes.

AIAnthropic's Sam Bowman shared a link to a system card for Claude Mythos Preview. The post itself gives no further details about the model's capabilities, benchmarks, or availability.

AIDario Amodei says the company has tracked growing cyber capabilities in AI models for years, which arise from their general coding proficiency. He states that the new model, Mythos Preview, represents a particularly large step up in those capabilities.

AIZ.ai's GLM-5.3 delivers a 50% coding gain over GLM-5.2 on Z.ai Code Bench, reaching open-source state-of-the-art on public benchmarks including Terminal Bench 3.0. GLM-5.3-Flash uses 320B total parameters with 18B activated, combining linear and sparse attention to reduce compute and KV-cache needs. GLM-5.2 supports a 1M lossless context window for long-horizon tasks.

AIWindsurf has made SWE-1.6, its model for software engineering agents, generally available, with the company saying it improves on the SWE-1.6 Preview by reducing overthinking, looping, and sequential tool calls. The model is free for three months, with a free version offered at 200 tok/s through Fireworks and a faster paid version at 950 tok/s through Cerebras.

AIBlack Forest Labs released FLUX.2 Small Decoder, a distilled VAE decoder that works as a drop-in replacement for the standard FLUX.2 decoder on Hugging Face. It decodes about 1.4x faster and uses about 1.4x less VRAM at decode time, with ~28M decoder parameters versus ~50M in the full decoder and minimal quality loss. It is available under the Apache 2.0 license and is compatible with FLUX.2-klein-4B, FLUX.2-klein-9B, FLUX.2-klein-9b-kv, and FLUX.2-dev.

AIZ.ai has released GLM-5.1, its next-generation flagship model for agentic engineering, with stronger coding than GLM-5. The model is described as staying effective over longer agentic tasks, sustaining optimization over hundreds of rounds and thousands of tool calls. The release lists benchmark results including SWE-Bench Pro at 58.4 and Terminal-Bench 2.0 at 63.5, and local deployment is supported through SGLang, vLLM, xLLM, Transformers, and KTransformers.

Why it matters: The release gives benchmark tables against several rival models, letting readers compare GLM-5.1's coding and agentic results with GLM-5 and frontier systems.

AIAwni Hannun says a 350M-parameter model trained on 28T tokens defies Chinchilla's compute-optimal scaling guidance. The quoted Liquid AI post credits scaled RL for LFM2.5-350M, reporting instruction following rising from 18.20 to 40.69, data extraction from 11.67 to 32.45, and tool use from 22.95 to 44.11 over LFM2-350M.

AIMistral AI released Mistral Medium 3.5, a dense 128B model with a 256k context window that handles instruction-following, reasoning, and coding in a single set of weights. It replaces Mistral Medium 3.1, Magistral, and Devstral 2, and reasoning effort is configurable per request. The model accepts text and image input and is released under a Modified MIT License that excludes companies with large revenue.

Why it matters: The release merges instruction, reasoning, and coding into one 128B model with per-request reasoning control, giving developers one set of weights to compare against separate specialized models.

AIAlibaba NLP released LaSER-Qwen3-8B, an 8B-parameter dense retriever built on Qwen/Qwen3-8B that internalizes explicit reasoning into latent space through continuous latent thinking tokens. The model scores 29.3 nDCG@10 on the BRIGHT benchmark, ahead of the rewrite-then-retrieve pipeline's 28.1, and carries a 4096-dimension embedding with an 8192-token maximum sequence length. It is licensed under MIT and adds about 1.7× latency over standard single-pass dense retrievers.

AIMistral has released Voxtral TTS, a text-to-speech model, alongside a blog post, a playground, a technical report, and model weights on Hugging Face. The post itself contains only links and no further details about the model's capabilities.

AICursor is releasing a technical report describing how its Composer 2 model was trained. The main post, from Cursor co-founder Aman Sanger, simply calls it a good model, with no further figures or details given.

AIPrismAudio is a framework that integrates reinforcement learning into video-to-audio generation, using a Chain-of-Thought planning mechanism. It builds on ThinkSound by splitting single-step reasoning into four CoT modules for semantic, temporal, aesthetic, and spatial dimensions, each with targeted reward functions. Code, model weights, and datasets are released for research and educational use under the MIT License, and commercial use requires explicit author authorization.

AICursor is increasing capacity for Composer 2, offering 2x more usage all weekend. Users are invited to try it out.

AIAman Sanger says Cursor's team evaluated many base models on perplexity-based evals and found Kimi k2.5 the strongest. Composer 2 was then built with continued pretraining and a 4x scale-up of high-compute RL, with Fireworks providing inference and RL samplers. The author admits Cursor should have named the Kimi base in its launch blog and says it will do so for the next model.

AIAman Sanger of Cursor says Composer 2 is a really good model and he is excited for more people to try it. The quoted reply from Lee Robinson says Composer 2 started from an open-source base, with only about one-quarter of the final model's compute coming from that base. Cursor plans full pretraining in the future and says it is following the license through its inference partner terms.

AIXiaomi introduces MiMo-V2-Omni, a single model that fuses image, video, and audio encoders into a shared backbone with native tool calling and UI grounding. The company reports benchmark results against Gemini 3 Pro, Claude Opus 4.6, and GPT 5.2, and demonstrates browser-based shopping and video-publishing workflows run through the OpenClaw agent scaffold. It also states the model supports over 10 hours of continuous audio understanding.

Why it matters: The page gives benchmark comparisons, a driving-risk demo, and browser-task walkthroughs, letting readers check how far the omni-modal claims extend into agent use.

AIMiniMax says M2.7 is its first model to deeply participate in its own evolution, building agent harnesses and running reinforcement learning experiment workflows. The post reports 56.22% on SWE-Pro, 55.6% on VIBE-Pro, 57.0% on Terminal Bench 2, and a 30% improvement on an internal evaluation set after more than 100 autonomous optimization rounds. It also states that M2.7 handles 30%-50% of its research team's workflow, though human researchers still make critical decisions.

Why it matters: The post ties M2.7's self-evolution claims to specific benchmark numbers and workflow details, helping readers judge how much of the iteration loop is autonomous.

AIXiaomi announced MiMo-V2-Pro, a flagship foundation model for agent workloads with over 1T total parameters, 42B active, and up to 1M-token context. It ranks 8th worldwide and 2nd among Chinese LLMs on the Artificial Analysis Intelligence Index, and its API is publicly available with usage-tiered pricing.

Why it matters: The post gives benchmark placements, parameter scale, context length, and tiered API pricing, so readers can compare it against Claude and GPT models on concrete terms.

AIXiaomi has launched MiMo-V2-TTS, a speech synthesis model that lets users describe the desired voice style in plain language. The model also supports dialects, character voices, non-verbal sounds such as coughs and sighs, and singing within one model. It was pretrained on over 100 million hours of speech data and refined with multi-dimensional reinforcement learning.

Why it matters: The source gives concrete controls for emotion, dialect, singing, and non-verbal sounds, showing how a voice model can be directed through plain-language style prompts.