Model releases

AIMoonshot AI published Kimi-K2.7-Code, a coding-focused agentic model built on Kimi K2.6, with a 1T-parameter MoE architecture and 32B activated parameters. The model card reports about 30% fewer thinking tokens than K2.6 and benchmark results against GPT-5.5 and Claude Opus 4.8, with weights and code released under a Modified MIT License.

Why it matters: The model card gives benchmark comparisons against GPT-5.5 and Claude Opus 4.8 on coding and agentic tasks, useful for judging its position among current coding models.

AIXiaomi and TileRT released MiMo-V2.5-Pro-UltraSpeed, reporting decode speeds above 1000 tokens/s on a 1-trillion-parameter model using a single standard 8-GPU node. The API is priced at 3x MiMo-V2.5-Pro and is available by application only from June 9 to June 23, 2026. The speedup relies on FP4 quantization of MoE Experts, DFlash speculative decoding with an average coding acceptance length of 6.30, and TileRT compute kernels.

Why it matters: The post traces how FP4 quantization, DFlash speculative decoding, and TileRT kernels combine to reach 1000+ tokens/s on a single 8-GPU node, which is useful for teams weighing inference throughput.

AICohere and Cohere Labs released North Mini Code 1.0, an open-weights 30B-A3B mixture-of-experts model for code generation and agentic terminal tasks, under Apache 2.0. The model has 256K context and 64K max output, and is trained for tool use. Its benchmark table lists Terminal-Bench v2 at 36.0, SWE-Bench Verified at 67.6, and LiveCodeBench v6 at 70.3, below Qwen3.6 on several tasks.

Why it matters: The card lists benchmark results against Qwen3.6, Gemma4, and other models, showing where North Mini Code trails on some coding and agentic tasks.

AIMiniMax published MiniMax-M3-MXFP8, an MXFP8 quantized variant of its native multimodal M3 model with 1M context, about 428B total parameters and about 23B activated parameters. M3 adds MiniMax Sparse Attention, which the source says yields 9× prefill and 15× decode speedups over M2 at 1M context. The model supports three thinking modes (enabled, adaptive, disabled) via the thinking parameter and can be served with SGLang, vLLM, or Transformers.

Why it matters: The release pairs sparse attention for 1M-token contexts with reported prefill and decode speedups over M2, useful for judging long-context serving costs.

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.

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.

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.

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 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 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.

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.

Why it matters: The post confirms Gemini 3.5 Flash's global availability and points to the blog for details, the main new information for developers.

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.

Why it matters: The post pairs parameter and pretraining cost reductions with benchmark results against named frontier models, letting readers weigh efficiency against capability.

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.

Why it matters: The quoted post gives concrete targets for the update, factuality and hallucination reduction, useful for judging whether the default ChatGPT model changed in practice.

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.

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.

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.