#Model release

AIMiniMax has released MiniMax-M3, a native multimodal model with a 1M-token context window, roughly 428B total parameters, and about 23B activated parameters. The model introduces MiniMax Sparse Attention, which the source says delivers 9× prefill and 15× decode speedups over M2 at 1M context. M3 supports enabled, adaptive, and disabled reasoning modes through the thinking parameter, and weights are available on Hugging Face.

Why it matters: The source gives concrete attention-efficiency figures and three reasoning modes, which helps readers judge long-context cost against deployment choices.

AIByteDance has open-sourced the inference code and model weights of the Bernini Renderer (Bernini-R), a DiT-based renderer paired with an MLLM-based semantic planner for video generation and editing. A diffusers-format version, ByteDance/Bernini-R-Diffusers, bundles the Wan2.2 base components with the Bernini-R transformer weights for direct loading, and the framework requires a CUDA GPU with PyTorch 2.5.1+cu124.

AIMiniMax released M3, an open-weight model with a 1M-token context window, native image and video input, and desktop operation support. The post credits a new sparse attention architecture, MSA, for long-context gains, reporting over 9x prefilling and over 15x decoding speedups and 59.0% on SWE-Bench Pro. The API and MiniMax Code are available now, with the technical report and open weights promised within 10 days.

Why it matters: The post pairs a new sparse attention design with benchmark figures and a 1M-token context window, letting readers judge the architecture's practical effect on long-context work.

AIXiaomi MiMo has published a blog detailing full-pipeline inference optimizations for the MiMo-V2.5 series. The post highlights how the team pushed hybrid sliding window attention (SWA) efficiency to its limit. The full write-up is available at

AIXiaomi's MiMo-V2.5 is now available in OpenCode at no cost for a limited time. The OpenCode announcement lists a 1M context window and support for reasoning, text, and image inputs.

AIPaddlePaddle has released PaddleOCR-VL 1.6, which sets a new state-of-the-art score of 96.33% on OmniDocBench for text, formula, and table recognition. It ranks first on OmniDocBench v1.5 and Real5-OmniDocBench, with gains in table, classic text, rare character, seal, spotting, and chart recognition. The version is fully compatible with the v1.5 architecture, requiring no migration.

AIJeremy Howard says Xiaomi's MiMo v2.5 model appears to offer very good value, with per-prompt cost shown after each answer. His test context includes more than 40k tokens of tool descriptions, a system prompt, and skills.

AIMiniMax says its M2 series could not output the name Ma Jiaqi, a failure it traced to post-training data that rarely included the token. Its tests found the input embedding stayed stable while the lm_head weights for low-frequency tokens drifted during SFT. A synthetic full-vocabulary repetition dataset restored generation for affected tokens and reduced Japanese-to-Russian confusion from 47% to 1%.

Why it matters: The post traces a community-noticed token failure through tokenizer, embedding, and lm_head tests, showing how post-training data coverage can cause low-frequency token drift.

AIFunAudioLLM has released Fun-ASR-Nano-2512-hf, a Hugging Face Transformers-compatible version of its end-to-end speech recognition model, which supports Chinese, English, and Japanese. The Chinese coverage includes 7 dialect groups and 26 regional accents, and a separate Fun-ASR-MLT-Nano-2512 checkpoint handles 31-language recognition. Developers can run the model natively in Transformers 5.17.0 without custom model code or trust_remote_code=True.

AIDeepSeek has made its discount on DeepSeek-V4-Pro permanent, after previously extending it until May 31, 2026, 15:59 UTC. The company is encouraging developers to build with the model.

AIAn OpenAI model disproved Erdős's longstanding planar unit distance conjecture, which Paul Erdős posed in 1946, by discovering a new family of constructions that performs better than the square grids mathematicians had long assumed. Mark Chen says the proof draws on algebraic number theory and describes it as the first time AI has autonomously solved a prominent open problem central to a field of mathematics.

Why it matters: The post names the specific open problem and the approach used, giving readers a concrete case of AI producing a research proof in mathematics.

AIStability AI has launched Stable Audio 3.0 for experimentation. The Small and Medium models are available on Hugging Face, while the Large model is accessible through the Stability AI API or self-hosting with an enterprise license.

AIStability AI released Stable Audio 3.0, a family of four audio models trained on fully licensed data. Three of them, Small SFX, Small and Medium, have open weights on Hugging Face, while Large is available through the Stability AI API and enterprise self-hosting. Outputs can be distributed and commercialized under the Stability AI Community License, and organizations with more than $1M in annual revenue can use the Enterprise License.

Why it matters: The source specifies which models are open-weight, their licensing terms, and clip-length limits, which matters for anyone deciding whether to build on them.

AIGoogle demonstrated Gemini 3.5 Flash in the Gemini app creating an interactive circuit helper that outputs a step-by-step physical build guide and a working simulation. The demo targets students learning complex subjects visually through hands-on interaction. Gemini 3.5 Flash is available globally starting today.

AIGoogle's Gemini 3.5 Flash outperforms Gemini 3.1 Pro on Terminal-Bench 2.1 (76.2%), GDPval-AA (1656 Elo), and MCP Atlas (83.6%). The post also claims it is 4x faster than other frontier models, or 12x in Antigravity, and reports 83.6% on MMMU-Pro for multimodal performance.

Why it matters: The post gives specific benchmark scores against Gemini 3.1 Pro, letting readers compare coding, agentic, and multimodal results directly.

AIGoogle is rolling out Gemini 3.5 Flash globally for consumers in the Gemini app and Search AI Mode. It is also available to developers through the Gemini API, Google Antigravity, and Google AI Studio, and to businesses on the Gemini Enterprise Agent Platform.

Why it matters: The post shows where each Gemini 3.5 Flash access path goes, from consumer apps to developer and enterprise platforms, which helps readers pick the right entry point.

AIAt Google I/O, Google introduced Gemini 3.5 Flash, which the author says has become part of the daily research cycle. The author says a team of agents in Antigravity 2.0 recreated the original AlphaZero paper and built a playable web version from two prompts, coding the reinforcement learning pipeline in JAX/Flax and training a ResNet model via self-play on multi-TPU pods.

AIGoogle's Gemini 3.5 Flash is available today globally, according to Oriol Vinyals. The post invites developers to build agents and apps with it and links to Google's blog for more details.

AIGemini 3.5 Flash produces fully tactile, interactive HTML/SVG hardware simulations in a single shot when given complex agents and tasks. The output includes bump-mapped metals, spring physics, and procedural audio, according to the post.

AIGoogle's Gemini 3.5 Flash reaches 76.2% on Terminal-Bench 2.1, 1656 Elo on GDPval-AA, and 83.6% on MCP Atlas, showing strong performance on multi-step agentic tasks. The model is positioned as an ultra-fast reasoning engine for powering AI agents.

AIComposer 2.5 is now the most-chosen model in Cursor, according to Cursor co-founder Michael Truell. To celebrate, Cursor is giving users 10x usage for the rest of the day.

AIMichael Truell of Cursor says Composer 2.5 is a significant step up from Composer 2. He adds that this is only the start of work with SpaceXAI, with more improvements expected soon. Cursor's announcement describes the model as more intelligent, better at long-running tasks, and more reliable at complex instructions, with doubled included usage for the next week.

AIShanghai AI Laboratory's Intern Large Models introduces Intern-S2-Preview, a 35B scientific multimodal foundation model, and says it matches the trillion-scale Intern-S1-Pro on core scientific tasks. The post says it is the first open-source model with material crystal structure generation and strong general capabilities, with shared-weight MTP plus KL loss improving acceptance rate and speed. It is already supported by vLLM and SGLang, with weights on Hugging Face and ModelScope.

AIThinking Machines says its team produced 12 versions, plus many subversions, and filled 137 pages of training run logs in recent months. The post credits human-human collaboration with improving human-AI collaboration. The linked blog describes an AI designed to talk, listen, watch, and think with people in real time.

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.