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WWallstreetcn19:47Model releases

AI score69

iFlytek releases cloud-device integrated Spark X2.6 and on-device Spark X2.6-4B

On October 22, iFlytek released the cloud-device integrated iFlytek Spark X2.6 at the 1024 Developer Festival, and simultaneously launched the on-device model Spark X2.6-4B; the system routes tasks to pure on-device, pure cloud, or cloud-device collaborative execution. Spark X2.6 uses a MoE architecture with 293B total parameters and 30B activated parameters, with the context window expanded to 1 million Tokens. The official website shows X2.6 at a limited-time 40% discount, with input and output prices of 0.9 yuan and 3.6 yuan per million Tokens, respectively. Liu Qingfeng expects that in the future about 60% of scenarios can be completed on-device, 30% will use cloud-device collaboration, and 10% will require cloud large models. The company says the share of tasks that can be completed independently on-device has risen from 12.8% to more than 40%.

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WWired AI19:00Model releases

AI score71

Hugging Face Releases Open-Source DNA Model Carbon-X, Runs Locally on a Laptop

Hugging Face on Thursday released the open-source DNA understanding model Carbon-X, which can run locally on a laptop, letting users ask in natural language how DNA changes affect gene activity and health. The model was developed in collaboration with researchers at Zhongguancun Academy and the University of Naples Federico II; it extends the vocabulary of an existing language model to read DNA in six-letter units and was further trained on 300 billion tokens of DNA sequences, English text, and samples pairing DNA with biological descriptions. The model has 14B parameters and can process sequences of about 50,000 base pairs at a time; it cannot upload a complete human genome. The developers also note that the prediction accuracy has not yet been validated through wet-lab experiments.

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BBusiness Insider Tech18:49Model releases

AI score71

Hugging Face Releases Open-Source Model Carbon-X, Which Can Analyze and Generate DNA Sequences Using Natural Language

Hugging Face has released the open-source model Carbon-X, combining DNA analysis and generation with natural language so that researchers can ask questions directly and receive predictions and explanations. The model is based on Qwen3-14B and was trained on genomic sequences and natural language from humans, other mammals, plants, and fungi, with viruses explicitly excluded from the training data. Researchers can run it locally, avoiding the sharing of sensitive patient data with external AI companies, and can also generate new DNA sequences to assist gene therapy research.

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IEnglish text is being prepared.18:42Model releases

AI score62

Ant Digital Technologies Releases Decision Model LingDT-Decision-1.0, Cutting Token Consumption by Over 90%

Ant Digital Technologies has released its decision model LingDT-Decision-1.0, purpose-built for rapid industrial decision-making, offering 4B and 27B versions. It is now live on the Ant Digital Technologies DTMaaS platform with a limited-time free trial. The model natively supports multiple-choice and sequential decision-making (DAG), and outperforms decision models such as Microsoft-Decision-1 and Jev 1.13 across 23 open-source evaluation benchmarks, with the 27B version achieving the highest accuracy among models of the same parameter size. Tests by Ant Digital Technologies in customer service, risk control, finance and other scenarios show that, when handling the same tasks, it can reduce Token consumption by over 90% compared with large language models and improve response time by 10x.

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AAYi AI Notes17:25Model releases

AI score73

MemTensor Open-Sources Native Memory Model MemT-1.0, Available in 36B and 288B Tiers

The MemTensor team has open-sourced MemT-1.0, a model built on a native memory architecture, offering two size tiers—36B Nano and 288B Flash—and has released a 66-page technical report. The model connects a Native Memory Block in parallel alongside the standard Transformer Block, uses an erase gate, write gate, and forget gate to achieve token-level dynamic state updates, and extends memory capacity through a Mixture-of-Storage mechanism. According to the figures in the article, under a 256K history, VRAM drops from 5.06 GiB to 40 MiB, and first-token latency remains at 104–112 ms between 512 and 256K.

Quoted postAYi · @AYi_AInotes

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QQbitAI16:55Model releases

AI score66

China Telecom Xingchen Releases and Open-Sources Intent Understanding Model Xing4.0-29B-Intent

The Xingchen General Artificial Intelligence Laboratory of China Telecom Artificial Intelligence Technology Co., Ltd. has released and open-sourced the dedicated intent understanding model Xing4.0-29B-Intent, simultaneously opening 2.753 million training corpus entries, model weights, and inference images. The model is fine-tuned based on Xing4.0-29B-A4B and adopts a unified schema-driven semantic parsing framework, supporting both intent recognition and parameter extraction. It outputs field-constrained JSON, with an average output of a few dozen tokens, and the full result response latency can reach the hundreds-of-milliseconds level. The model has been applied in conversational intent recognition and intelligent ticket routing for Telecom's ten-thousand-line customer service, as well as spoken command parsing for smart home speakers. The code is open under the Apache 2.0 license, and the dataset and weights will be released simultaneously on Hugging Face.

#Model releases#Open-source ecosystem#Reasoning

MMarkTechPost15:52Model releases

AI score62

IBM Releases Granite Speech 5.0 2B Multilingual Speech Transcription and Translation Model

IBM has released the open-source speech language model Granite Speech 5.0 2B under the Apache 2.0 license. It supports speech-to-text in 6 languages and also offers speech translation capabilities. On the Open ASR Leaderboard, the model ranks second among open-weight models with an average English WER of 5.68%, behind only Qwen3-ASR-1.7B at 5.40%. Compared with 4.1 2B, it adds language ID prompting, keyword biasing of up to 2000 words, and on-demand punctuation and capitalization, and can run on Transformers, vLLM, llama.cpp, and mlx-audio.

#Model releases#Speech#Open-source ecosystem

IEnglish text is being prepared.15:42Model releases

AI score68

iFlytek Spark X2.6 cloud-device integrated large model released, achieving breakthroughs in coding and agent capabilities based on fully domestic computing power

iFlytek released the iFlytek Spark X2.6 cloud-device integrated large model at the 1024 Developer Festival, claiming breakthroughs in coding and agent capabilities based on fully domestic computing power for the first time. The model adopts a 293B-A30B parameter MoE architecture, expands context length to 1M, and supports custom security policies by task scenario. The simultaneously released Spark X2.6-4B open-source on-device large model improves coding, agent, long-text and multilingual capabilities, adds three tiers of reasoning modes, and offers free reasoning for a limited time; the X2.6 series is now available for trial on the iFlytek Spark official website.

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IIT Home · Artificial Intelligence15:42Model releases

AI score63

iFlytek Releases SparkAudio 1.0, a Spark Speech Foundation Model

At the 1024 Developer Festival, iFlytek announced the debut of SparkAudio 1.0, a Spark speech foundation model that integrates capabilities such as machine translation, speech recognition, and semantic understanding, and claims outstanding performance in real-time speaker separation, environmental understanding, audio understanding, and emotion recognition. The model has been fully launched in iFlytek Input Method 16.0, iFlytek Hearing, iFlytek Office Notebook, and iFlytek Translator, supporting arbitrary switching between Chinese and English and real-time speaker recognition. iFlytek also released the iFlytek AI Recording Ring, equipped with a 4 + 1 ring microphone array and Wi-Fi 6 transmission, claiming a comprehensive recognition accuracy rate of over 96% and that transcribing 2 hours of recording takes only 2 minutes.

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#Model releases#Speech#Multimodal

Mirendil Releases Mirendil-Base Code Foundation Model, Pretraining Completed in 3.5 Months

Mirendil has released its code foundation model Mirendil-Base, pretrained from scratch by one researcher leading a group of Agents over 3.5 months at a cost of under $10 million, producing a model with 100–500 billion parameters. It used only public data—no distillation, no synthetic data, and no purchased data—and more than 99% of the code was written by Agents. On code prediction loss it outperforms GLM-5.3, DeepSeek-V4-Pro-Base, and Nemotron-550B, but falls short of Kimi-K3. The model does not release its weights, has no API, and has no task evaluations such as SWE-bench yet; the author views this as more of an efficiency win for a senior researcher plus Agents, and the method relies on third-party Coding Agents and is easy to replicate.

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PPhoenix Technology12:44Model releases

AI score62

ElevenLabs Releases Eleven Realtime v1 Mini and Max Real-Time Speech Models

On October 20, ElevenLabs released the Eleven Realtime series of real-time speech models, including v1 Mini for natural conversation and v1 Max for customer service and multi-step tasks, both supporting more than 90 languages and 17,000 voices. According to Artificial Analysis testing, Max achieved 98.8% accuracy on the Big Bench Audio speech reasoning benchmark, ranking third, with an average time to first audio response of 0.57 seconds, while Mini achieved 75.9% accuracy and 0.62-second response time. Both models can be used through the ElevenAgents platform or the Realtime API compatible with the OpenAI Realtime specification, and currently only support WebSocket connections; in the author's hands-on test, when building an assistant with Architect, the Chinese accent was noticeable, responses tended to be long, and latency was relatively high.

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TThe Elec11:31Model releases

AI score57

Kakao Open-Sources Kanana-2-vision-encoder Vision Encoders in 426 Million and 98 Million Parameter Versions

Kakao has open-sourced two versions of its self-developed image recognition model Kanana-2-vision-encoder: a medium version with 426 million parameters and a tiny version with 98 million parameters, both now available on Hugging Face. Kakao says that across 42 benchmarks spanning nine major categories, including general visual question answering, English-Korean OCR, document understanding and object grounding, the two models achieved the highest performance compared with Google SigLIP 2, Meta Perception Encoder, Apple AIMv2 and Alibaba Qwen3.5-ViT. The tiny version outperforms competitors with more than 400 million parameters on some benchmarks, with particularly notable gaps in Korean recognition, document understanding and object grounding. During training, the use of the self-developed Kanana-2 language model improved training efficiency by about 4x, and architectural improvements for high-resolution image processing reduced latency by more than 60%; the models are released under the Kanana Open License, permitting research, development and commercial use.

#Model releases#Open-source ecosystem#Multimodal

TTesting Catalog10:46Model releases

AI score68

Recursive Releases Flint, Its First Open-Weight Coding Model

Recursive has released Flint, its first open-weight coding model, under the Apache 2.0 license. It is a 40B-parameter MoE model aimed at local coding scenarios. Recursive says it reaches over 220 tokens per second on a 64GB RTX Spark, 2x faster than comparable models. Its automated research system, Eureka Machine, also generated custom GPU code that sped up Flint on the RTX Spark by 55% without changing the weights.

Quoted postRichard Socher · @RichardSocher

Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.

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DDongseok Ma NLP10:46Model releases

AI score58

Base Labs Releases mammoth-1 and elephant-1 Decision Models and Open-Sources Weights

Base Labs has released two decision models, mammoth-1 and elephant-1, stating that mammoth-1 is the world's top-ranked decision model and elephant-1 is the best model under 15B and surpasses Jev. Base Labs has open-sourced all model weights and provided a technical report detailing its training and data methods.

Quoted postBase Labs · @baselabs

Today we release mammoth-1 and elephant-1! We post-trained mammoth-1, the #1 ranked best decision model in the world, and elephant-1, which is the best model under 15B (and beats Jev). We're open-sourcing all model weights and provide a technical report detailing all our training and data methods.

#Model releases#Open-source ecosystem

SShao Meng10:46Model releases

AI score75

Recursive releases its first model Flint: local coding MoE, 125+ tokens/s on a MacBook

Recursive releases its first model Flint, a MoE with 40 billion total parameters and 2.5 billion activated parameters per token, aimed at local Agent coding, with weights open-sourced under Apache 2.0. With short context, it exceeds 125 tokens per second on an M4 Pro MacBook Pro and over 150 on a 64GB RTX Spark, and supports llama.cpp, Ollama, LM Studio, vLLM, SGLang, and Unsloth. Nearly every stage of its R&D involved the automated research system Eureka Machine, including architecture search, quantizing the output layer to 4 bit with offline distillation, and fusing Metal kernels for M4/M5.

Quoted postRichard Socher · @RichardSocher

Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.

Same story · 9 reports

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OOpenRouter10:46First-partyModel releases

AI score62

Recursive's first open-source model Flint launches on OpenRouter, a 40B MoE built specifically for coding agents

Recursive's first model Flint has launched on OpenRouter. It is an open-weight 40B MoE model (about 2.5B active parameters), built by the company's automated research system Eureka Machine, aimed at coding agents, and supports 262K context, reasoning, and tool calling. Recursive reports that it scores 54.9% on Terminal-Bench 2.1, 89.7% on SWE-bench Pro v2, and 74.2% on SWE-bench Verified, and is free to use during its launch period.

Quoted postRichard Socher · @RichardSocher

Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.

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RRohan Paul10:46Model releases

AI score76

Recursive releases Flint, a 40B MoE coding model, at 125+ tokens/s on a MacBook Pro

Recursive has released its first model, Flint, a 40B mixture-of-experts coding model under the Apache 2.0 license, with weights now open on Hugging Face, built from scratch in under 4 months by the automated research system Eureka Machine. Flint generates code at 125+ tokens/s on a MacBook Pro and reaches 150+ tokens/s on an RTX Spark. On a 48GB MacBook, its average quality is about 81%, compared with about 85% for Qwen3.8 27B, but in 128K-token long sessions it is about 7 times faster than the latter.

Quoted postRichard Socher · @RichardSocher

Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.

Same story · 9 reports

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AAyi AI Notes10:43Model releases

AI score67

MemT-1.0, an open-source native-memory large model, cuts 256K memory usage to a constant 40M

MemT-1.0 is open-sourced, connecting a native memory module in parallel within the Transformer to turn memory into a state in the forward computation flow, releasing up to 288B weights and a 66-page technical report. The authors claim that under a 256K ultra-long history, memory overhead drops from 5.06G to a constant 40M, KV cache is reduced by more than 120 times, and first-token latency stays stable at just over 100 milliseconds. The team also wrote more than a thousand code-correction experiences into the internal memory state, which do not occupy prompt tokens, and the pass rate on a standard code test set increased by 8% for three consecutive runs.

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MMarkTechPost07:45Model releases

AI score70

Recursive Releases Open-Weight Coding Model Flint, a 40B MoE with Only 2.5B Active Parameters

Recursive Superintelligence has released its first model, Flint, a 40B MoE model aimed at coding agents with roughly 2.5B active parameters per token; the weights are released under the Apache 2.0 license on Hugging Face. The model card lists a context of 262,144 tokens, with 4-bit weights at about 23 GB, runnable on a 48 GB Apple Silicon Mac or a 48 GB NVIDIA GPU. The company self-reports SWE-bench Verified at 74.2% and IFEval at 95.1%, while Terminal-Bench 2.1 stands at 54.9%, below dense Qwen 3.8's 73.0%. Recursive says its automated research system Eureka Machine contributed to the design of the architecture, quantization, and kernels.

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TTechCrunch AI04:47Model releases

AI score69

Recursive releases laptop-class coding model Flint, says it was built mainly with AI involvement

AI startup Recursive has released its first model, Flint, a coding model that runs offline on a laptop and is freely downloadable on Hugging Face under the Apache 2.0 license; the company says its development process was mainly carried out with the involvement of its in-house Eureka Machine. Flint is an MoE model that activates only 2.5B of its 40B parameters at a time, generates at about 131 tokens/sec on a MacBook Pro with an M4 Pro, and outperforms other models under 50B parameters on SWE-Bench Verified. Recursive says Flint requires more guidance than large paid coding assistants and recommends breaking large tasks into small steps.

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