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Sunday

Saturday

  1. ZhidongxiSelected

    DeepSeek V4.1-Flash updated to V4.1-Flash-1017 and opens search and fetch APIs

    DeepSeek V4.1-Flash has been updated to version V4.1-Flash-1017; the official web, App and API have been updated in sync, and the model weights are available for download on HuggingFace and ModelScope. In the DeepSWE v1.1 test, the new model's max tier achieved a single-task pass rate of 77.0%, 2.8 percentage points higher than the previous version, and the average cost per task dropped from about 3.3 yuan to about 1.3 yuan. DeepSeek also opened to the public the Search and Fetch APIs of its self-developed search engine; both interfaces are priced at 5 yuan per thousand calls, and this part of access via server-side tool calls will be charged per call starting from 00:00 Beijing time on November 1.

    DeepSeek V4.1-Flash has been updated to version V4.1-Flash-1017; the official web, App and API have been updated in sync, and the model weights are available for download on HuggingFace and ModelScope. In the DeepSWE v1.1 test, the new model's max tier achieved a single-task pass rate of 77.0%, 2.8 percentage points higher than the previous version, and the average cost per task dropped from about 3.3 yuan to about 1.3 yuan. DeepSeek also opened to the public the Search and Fetch APIs of its sel

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    #hot-signal#Model releases#Open-source ecosystem#English text is being prepared.

Thursday

  1. Shao Meng

    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.

    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,

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    #hot-signal#Model releases#Open-source ecosystem#Agents

Wednesday

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Wednesday

  2. 36Kr · AISelected

    Google Gemini 4 Argon is fully open to developers worldwide

    On October 13, US local time, Google Gemini 4 Argon opened to developers and enterprises worldwide, callable via platforms such as the Gemini API, Google AI Studio, and Google Antigravity; Gemini Pro and Ultra subscribers can also experience it in the Gemini app and AI Mode in Search. Google also disclosed several application cases, including using Argon to refactor a Chromium legacy code directory of about 6 million lines, raising Gemma 4 E2B inference throughput on a single A100 from about 37,900 tokens per second to over 108,000, and building a Real2Sim simulation environment from real robot teleoperation videos. Limited-time API pricing is 2 USD per million input tokens and 10 USD for output, rising to 4 USD and 20 USD respectively from December 1; the model offers a unified 1 million token context window.

    On October 13, US local time, Google Gemini 4 Argon opened to developers and enterprises worldwide, callable via platforms such as the Gemini API, Google AI Studio, and Google Antigravity; Gemini Pro and Ultra subscribers can also experience it in the Gemini app and AI Mode in Search. Google also disclosed several application cases, including using Argon to refactor a Chromium legacy code directory of about 6 million lines, raising Gemma 4 E2B inference throughput on a single A100 from about 37,

    Matched fieldsSummary、Body

    #hot-signal#Model releases#Google#Multimodal

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