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#Model release

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Oct 5

Oct 5Mon
  1. Liquid AIOfficialAI score37

    Liquid AI's d1 decision model adds vision, rivaling GPT-6.1 Sol at lower cost

    AILiquid AI released d1 with vision support, accepting images, text, or both as inputs. In tests on six real applications, d1 matched or beat GPT-6.1 Sol on four while costing 19x to 200x less than both GPT-6.1 Sol and Claude Opus 5.5. It returns probabilities for yes/no, choice, or score questions in one forward pass, with text decisions in 200 to 300 ms.

    Image from @liquidai's post
  2. Liquid AIOfficialAI score36

    Liquid AI's d1 model inspects parts from camera images with 85-97% accuracy

    AILiquid AI's vision-enabled decision model d1 inspects parts directly from camera images and is described as the best such model currently on the market. It reaches 85% to 97% accuracy across four VisA inspection tasks covering circuit boards, candles, cashews, and chewing gum. It understands each task from a short description without task-specific training.

    Video from @liquidai's post
  3. Liquid AI · new models on Hugging FaceOfficialAI score44

    LiquidAI releases d1-omni-600M, a 600M decision model for text, image and audio

    AILiquidAI has released d1-omni-600M on Hugging Face, a 587M-parameter model that answers named yes/no, choice and score questions over text, images or up to 30 seconds of speech in a single forward pass. It returns typed answers with zero output tokens by reading the model's distribution over options, and is built on LFM2.5-Encoder-350M with a 16,384-token context length. The model is not a chat model and does not generate text.

  4. Nous ResearchOfficialAI score38

    Upstage's Solar Mini 4 free on Nous Portal for two weeks

    AIUpstage's Solar Mini 4 is free on Nous Portal for the next two weeks. The model has 3B active parameters out of 35B total and a 512K context window. It scores 24 on the Artificial Analysis Intelligence Index, above models with roughly 10x the active parameters.

    Video from @NousResearch's post
  5. Liquid AI · new models on Hugging FaceOfficialAI score67

    Liquid AI releases d1-3B, a 3B multimodal decision model for edge deployment

    AILiquid AI has released d1-3B, a 3B parameter multimodal model post-trained to return calibrated, typed answers to yes/no, choice, and score questions in one forward pass. The source reports a Decision Index 0.2.1 score of 48.57, the highest among models under 10B in its table, and 8 ms per decision on an NVIDIA RTX 4090.

    Why it matters: The source gives benchmark scores against named peer models and edge latency figures across several hardware targets, helping readers judge fit for on-device decision pipelines.

Oct 4

Oct 4Sun
  1. Liquid AI BlogOfficialAI score70

    Liquid AI releases d1 decision model with image input support

    AILiquid AI introduces d1, its first decision model, now accepting both text and images. The company says d1 matches or beats GPT-6.1 Sol on four of six tested applications, at 19x to 200x lower cost and with faster answers on every task. d1 is available on the Liquid AI API and through Vercel and OpenRouter, with text-only support on those two platforms for now.

    Why it matters: The post gives benchmark comparisons against named models along with per-token pricing and latency figures, which makes the cost and speed tradeoff checkable.

  2. Tibor BlahoXAI score62

    OpenAI and Anthropic weekly roundup covers DevDay, Sonnet 5.5, and FTC probe

    AIOpenAI announced over 20 updates at DevDay 2026, including always-on agents on GPT-6 Astra and GPT-6.1 Sol, which arrived in the API and at a fifth of Astra's price. Anthropic launched Claude Sonnet 5.5, priced the same as Sonnet 5 but over 30 percent faster. Reuters reported an FTC probe into Anthropic, OpenAI and other labs over rogue AI agents.

    Video from @btibor91's post
  3. Tibor BlahoXAI score37

    OpenAI and Anthropic announce major updates, FTC probes labs over rogue agents

    AIOpenAI announced more than 20 updates at DevDay 2026, including always-on agents on GPT-6 Astra, GPT-6.1 Sol priced at a fifth of Astra's API cost, and a new $500/month Pro 500 plan. Anthropic launched Claude Sonnet 5.5 at $2/$10 per million tokens, 30%+ faster than Sonnet 5, with thinking always on. Reuters reported the FTC is probing OpenAI, Anthropic and other labs over rogue AI agents.

Oct 3

Oct 3Sat
  1. IndexTeam (Bilibili) · new models on Hugging FaceOfficialAI score22

    Index-Echo-S2ST-9B-FP4 released as NVFP4 quantized speech translation model

    AIIndexTeam released Index-Echo-S2ST-9B-FP4, an NVFP4 (W4A4) quantization of the Index-Echo-S2ST-9B speech-to-speech translation model, with only its text LLM backbone quantized. Perplexity rose from 3.8218 to 3.9650 (+3.75%) on a fixed corpus, while zh→en and en→zh outputs were semantically equivalent, and full FP4 speedup requires an NVIDIA Blackwell GPU.

  2. IndexTeam (Bilibili) · new models on Hugging FaceOfficialAI score27

    Index-Echo-S2ST-2B FP4 Quantized Speech-to-Speech Translation Model Released on Hugging Face

    AIIndexTeam released Index-Echo-S2ST-2B-FP4, an NVFP4 (W4A4) quantized version of the Index-Echo-S2ST-2B speech-to-speech translation model, with only the text LLM backbone quantized and the audio components kept in BF16. On a fixed corpus, perplexity rose from 5.9332 to 6.4980 (+9.52%), while zh->en and en->zh generations matched the original. Full FP4 acceleration requires an NVIDIA Blackwell GPU, and the model loads via compressed-tensors in vLLM or transformers.

  3. IndexTeam (Bilibili) · new models on Hugging FaceOfficialAI score20

    IndexTeam releases NVFP4 quantized Index-Echo-S2TT-9B speech translation model

    AIIndexTeam published an NVFP4 (W4A4) quantized version of its Index-Echo-S2TT-9B speech-to-text translation model, quantizing only the text LLM backbone while keeping the audio tower and other components in BF16. On an NVIDIA A100, perplexity rose from 3.4155 to 3.5113 (+2.81%), with zh->en and en->zh outputs semantically equivalent under greedy decoding. Full FP4 speedup requires an NVIDIA Blackwell GPU, while older GPUs get only memory reduction.

  4. IndexTeam (Bilibili) · new models on Hugging FaceOfficialAI score20

    IndexTeam releases NVFP4 quantized Index-Echo-S2TT-2B speech translation model

    AIIndexTeam has published an official NVFP4 (W4A4) quantized version of its Index-Echo-S2TT-2B speech-to-text translation model on Hugging Face. Only the text LLM backbone is quantized, while the audio tower, connector, and speech-synthesis components remain in BF16. Perplexity rises 5.80%, from 4.8772 to 5.1599, on a fixed corpus, and full FP4 speedup requires an NVIDIA Blackwell GPU.

  5. IndexTeam (Bilibili) · new models on Hugging FaceOfficialAI score22

    Index-Nailong-9B-FP4 NVFP4 quantized translation model released on Hugging Face

    AIIndexTeam released Index-Nailong-9B-FP4, an official NVFP4 (W4A4) quantization of the Index-Nailong-9B multilingual translation model, which covers 150 languages. In a validation on an NVIDIA A100 against the BF16 checkpoint, perplexity rose 3.10% (2.4339 to 2.5094), and zh-en and en-zh outputs were semantically equivalent. Full FP4 compute acceleration requires an NVIDIA Blackwell GPU, while older GPUs get memory savings only; the FP8 build is recommended for Hopper and Ampere.

  6. IndexTeam (Bilibili) · new models on Hugging FaceOfficialAI score29

    Index-Nailong-2B-FP4 Released as NVFP4 Quantized Translation Model

    AIIndexTeam has released Index-Nailong-2B-FP4, an official NVFP4 (W4A4) quantization of its Index-Nailong-2B multilingual translation model, which supports 150 languages. The checkpoint keeps lm_head, embeddings, and MoE router gates in BF16, and a perplexity test on a fixed corpus rose from 3.2806 to 3.4998 (+6.68%), while zh->en and en->zh outputs matched BF16 semantically. Full FP4 acceleration requires an NVIDIA Blackwell GPU; on Hopper or Ampere, vLLM provides only memory savings, so the FP8 build is recommended.

  7. IndexTeam (Bilibili) · new models on Hugging FaceOfficialAI score23

    Index-Homura-9B-FP4 released with NVFP4 quantization for translation model

    AIIndexTeam released Index-Homura-9B-FP4, an official NVFP4 (W4A4) quantization of the Index-Homura-9B translation model from the Index-Translate family. On a fixed corpus, perplexity rose from 2.5386 in BF16 to 2.6245, a 3.38% increase, and zh->en generations matched the original. Full FP4 compute acceleration requires an NVIDIA Blackwell GPU, while older GPUs get only weight-only memory savings and the FP8 build is recommended for them.

  8. IndexTeam (Bilibili) · new models on Hugging FaceOfficialAI score29

    Index-Homura-2B-FP4 released as NVFP4 quantized translation model

    AIIndexTeam released Index-Homura-2B-FP4, an official NVFP4 (W4A4) quantization of its Index-Homura-2B multilingual translation model, which supports 150 languages. The quantized checkpoint shows a 5.73% perplexity increase over the BF16 original (3.5011 to 3.7017) on a fixed corpus, and its zh-en and en-zh outputs are semantically equivalent under greedy decoding. Full FP4 acceleration requires an NVIDIA Blackwell GPU, while the source recommends the FP8 build for Hopper and Ampere hardware.

  9. CohereOfficialAI score13

    Cohere explains two data synthesis methods used in its model training

    AICohere says it refined training data using two approaches: Best-per-Language Forward Translation and Post-Edit Driven Preference Distillation. The post is part 4 of a thread and explains why these methods were chosen, but the source text does not give further detail.

    Video from @cohere's post

Oct 2

Oct 2Fri
  1. Replit ⠕OfficialAI score40

    Replit adds interactive charts, new models, and Jev integration

    AIReplit chat now generates interactive charts when users ask Replit Agent to visualize data. Users can also choose GPT-6.1 Sol from OpenAI or Claude Sonnet 5.5 from Anthropic when building with Agent, or stay in auto mode. Jev is available through Replit AI Integrations for classifying content, routing requests, and scoring leads without managing API keys.

    Video from @Replit's post
  2. Kilo (acq. by Anaconda)OfficialAI score36

    Ling 3.1 Flash is free in Kilo Code until October 13

    AIKilo Code is offering Ling 3.1 Flash for free until October 13, with the model served by Novita Labs. Ant Ling's background post describes the model as roughly 560B total parameters with about 25B active per token and a context window of up to 1M tokens. Ant Ling says it scores 1,673 Elo on GDPVal-AA v2.1, 75.16 on FrontierSWE, and 65.35 on HealthBench Professional, and plans to open-source it soon.