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#Coding

All collected and published items, ordered by time. Picks are a selected subset.
Models

New models and versions, open weights, and changes in capability and pricing, with their evaluations.

Today10/17Sat
  1. 量子位87

    Google releases Gemini 4 Argon with temporary half-price API access

    Google is rolling out Gemini 4 Argon globally to developers, enterprises, and paid subscribers, emphasizing coding, professional knowledge work, multimodal understanding, and sustained task execution. API pricing is $2 per million input tokens and $10 per million output tokens through November 30, rising to $4 and $20 on December 1. Google reports that Argon worked unattended for 11 hours on ADK optimizations, with engineers merging two changes; Ethan Mollick described improvements but noted remaining weaknesses in complex multi-agent tasks.

10/16Fri
  1. Hugging Face Blog83

    Reflection releases Beam, a 501B open model for coding and agentic work

    Reflection released Beam on Hugging Face, a 501B MoE model with 23B active parameters per token, a 1M-token context window, and an Apache 2.0 license. The text-only model targets coding and agentic work, with BF16, FP8, and NVFP4 checkpoints and day-0 support in transformers, vLLM, and SGLang. Hugging Face provides deployment and post-training recipes alongside its own task evaluations and demos.

    Why it matters: The deployment guidance distinguishes active computation from total weight memory, helping teams assess hardware requirements for running and adapting a sparse model with a long context window.

  2. Artificial Analysis · X55

    RTX Spark laptop completes Artificial Analysis local agent workload in 18.7 minutes

    NVIDIA's RTX Spark N1X puts up to 128 GB of unified memory and a Blackwell GPU in a Windows laptop. In Artificial Analysis's first AA-AgentPerf-Local results, an RTX Spark laptop completes its local agent workload on Qwen3.8 27B in 18.7 minutes. Artificial Analysis describes RTX Spark's N1X chip as essentially the GB10, linking to https://t.co/520A4lOOdB

  3. TechCrunch · AI78

    Meta launches Muse Spark Open, an open-weight AI model designed for Macs

    Meta has launched Muse Spark Open, a natively multimodal open-weight model with 816 billion parameters and a 1 million token context window, built for complex coding tasks and agentic workflows. It is designed to run locally on Macs, though Engadget reports it requires at least 1 terabyte of RAM. Meta claims superior performance over other popular open-weight models in its evaluations and is making the model available now through multiple API platforms.

    The same story is shown in the picks as 《Meta releases Muse Spark Open, an open-weight multimodal mixture-of-experts model》

  4. The Decoder77

    Meta releases Muse Spark Open for coding and agentic tasks

    Meta has released Muse Spark Open, an open-weight model with 816 billion parameters (42 billion active), up to one million tokens of context, and text, image, video, and audio support. Meta reports leads over Qwen 3.8, GLM 5.3, and Kimi K3 in several benchmark categories, while trailing some competitors in coding. Weights are on Hugging Face; use requires the label "Built with Muse Spark Open," and API providers earning more than $20 million annually need a separate agreement with Meta.

    The same story is shown in the picks as 《Meta releases Muse Spark Open, an open-weight multimodal mixture-of-experts model》

  5. NVIDIA AI · X85

    Meta releases Muse Spark Open for coding and agentic work

    Meta announced Muse Spark Open, describing it as the most capable US open-weight model to date, built for coding, agentic work, and multi-step reasoning. NVIDIA AI says the 816B model was trained on NVIDIA GB300 NVL72 and has a 1M-token context window for working across large codebases. Meta also produced NVFP4 checkpoints optimized for Blackwell Ultra. https://t.co/yAtoxNeYJm https://t.co/HJUKdC1n3D

    The same story is shown in the picks as 《Meta releases Muse Spark Open, an open-weight multimodal mixture-of-experts model》

  6. 宝玉 · X84

    Meta releases Muse Spark Open with downloadable weights and conditional commercial use

    Baoyu reports that Meta released Muse Spark Open on October 15, an open-weight model with 816B total parameters, 42B active parameters, and a 1M token context window. It accepts text, images, video, and audio and outputs text; the Muse Model License permits free commercial use subject to attribution, usage restrictions, and a separate agreement for qualifying API businesses. The model card lists minimum memory requirements of 700GB for NVFP4 and 2TB for BF16, while Meta describes optimization for two Mac Studio machines with 512GB unified memory each.

    The same story is shown in the picks as 《Meta releases Muse Spark Open, an open-weight multimodal mixture-of-experts model》

  8. Ollama · X79

    Meta releases Muse Spark Open, with Ollama availability coming shortly

    Meta announced Muse Spark Open, calling it the most capable US open-weight model to date and describing it as built for coding and agentic work. Meta lists 816B total / 42B active parameters, a 1M token context window, and native text, image, video, and audio support. Ollama welcomed the release and said the model will be available on Ollama shortly. https://twitter.com/AIatMeta/status/2110779754274292212

    The same story is shown in the picks as 《Meta releases Muse Spark Open, an open-weight multimodal mixture-of-experts model》

  9. AI at Meta · X81

    Meta releases Muse Spark Open for coding and agentic work

    Today Meta is releasing Muse Spark Open, which it calls the most capable US open-weight model to date. Built for coding and agentic work, Meta says it excels at long-horizon tasks, complex software engineering, and multi-step reasoning. Key model specs include 816B total / 42B active parameters, a 1M token context window, and native multimodality across text, image, video, and audio.

    The same story is shown in the picks as 《Meta releases Muse Spark Open, an open-weight multimodal mixture-of-experts model》

  10. Hugging Face Blog87

    Meta releases Muse Spark Open, an open-weight multimodal mixture-of-experts model

    Meta’s Muse Spark Open is an open-weight mixture-of-experts model with approximately 816 billion parameters, around 42 billion active per token, and a context window of up to one million tokens. Trained for agentic coding, it accepts text, images, audio, and video and generates text. Hugging Face provides inference and deployment recipes, reports its own quantization tests, and demonstrates attention-layer LoRA fine-tuning with TRL while keeping the experts frozen.

    Why it matters: The deployment recipes clarify how sparse expert activation reduces per-token computation while retaining substantial memory requirements, helping developers assess hardware needs across checkpoint formats and serving frameworks.

10/15Thu
10/14Wed
  1. Artificial Analysis · X46

    Claude Sonnet 5.5 Scores 68 on the Coding Agent Index at Max Effort

    In Claude Code, Sonnet 5.5 scores 68 on the Coding Agent Index at max effort and 63 at xhigh. The xhigh setting costs $3.33 per task versus $14.19 at max, a 77% reduction for 5.5 fewer Index points. Max generates 4.8× as many output tokens and takes 3.4× as many turns, while improving all three benchmark scores; its largest gain is on Terminal-Bench 4.0, from 58.1% to 66.2%.

  2. The Verge · AI82

    Google releases Gemini 4 Argon with competitive pricing and claimed benchmark gains

    Google launched Gemini 4 Argon, claiming improved reasoning and benchmark performance against GPT-6 Astra, Claude Fable 5.1, and Claude Opus 5.5. It is rolling out today to Pro and Ultra subscribers in the Gemini app and AI mode in Search, and Ultra subscribers on Antigravity; access for Plus subscribers and free users remains unclear. Introductory pricing is $2 per million input tokens and $10 per million output tokens, doubling at the end of November, though lower token rates do not necessarily mean lower task costs.

    The same story is shown in the picks as 《Gemini 4 Argon is now available worldwide as Google's most capable frontier model》

  3. Sundar Pichai · X37

    Some great use cases from Googlers building with Gemini 4 Argon 🧵 It has a much stronger grasp of coding and multi-step tool use - starting from a raw Pixel CAD file in Google Antigravity, Argon created this exploded-view animation, lighting, camera choreography, and audio through Blender MCP. It ran 1,000 rounds of iterative loops to check its own renders and refine the code along the way.

    Some great use cases from Googlers building with Gemini 4 Argon 🧵 It has a much stronger grasp of coding and multi-step tool use - starting from a raw Pixel CAD file in Google Antigravity, Argon created this exploded-view animation, lighting, camera choreography, and audio through Blender MCP. It ran 1,000 rounds of iterative loops to check its own renders and refine the code along the way.

  4. 宝玉 · X85

    Google releases Gemini 4 Argon with paid access and introductory API pricing

    Baoyu reports that Google released Gemini 4 Argon on October 13 for complex, multistep tasks, citing Sundar Pichai’s announcement of global paid availability. Access includes the paid Gemini API and platform, Ultra subscriptions in Google Antigravity, and Pro or Ultra subscriptions in the Gemini app and Search. Introductory API pricing is $2 per million input tokens and $10 per million output tokens, including thinking tokens, through November 30; prices double December 1. Input, thinking, and output share a 1,048,576-token limit.

    Why it matters: The shared budget for input, thinking, and output makes context allocation consequential for long tasks, while repeated input accounting leaves a specific billing detail unresolved.

  6. Google DeepMind94

    Google DeepMind launches Gemini 4 Argon for complex reasoning and agentic tasks

    Google DeepMind has launched Gemini 4 Argon globally through its API and selected Google products, with access depending on subscription or service tier. Introductory pricing is $2 per million input tokens and $10 per million output tokens, with cached input tokens at 95% off and a 1M unified input-output token limit through the Gemini API. Google reports improvements across coding, professional workflows, scientific research, and robotics, alongside stronger agent safeguards and prompt injection resistance.

    Why it matters: Google’s reported gains in code submissions and reductions in rollbacks connect Argon’s engineering capabilities to delivery speed and code stability in internal workflows.

  7. Logan Kilpatrick · X86

    Google rolls out Gemini 4 Argon across Gemini API, Gemini App, and Antigravity

    Logan Kilpatrick announces that Gemini 4 Argon is rolling out globally today in the Gemini API, to Pro + Ultra users in Gemini App, and to Ultra subscribers in Antigravity. He says over 100,000 people at Google tested the model over the last month, with cyber defenders in the fairwind program and early access customers receiving it over the last 2 weeks. He highlights writing, knowledge, persistence for coding, and 1M token output when the model needs to work more.

    The same story is shown in the picks as 《Gemini 4 Argon is now available worldwide as Google's most capable frontier model》

  9. Demis Hassabis · X83

    Gemini 4 Argon rolls out to Google Ultra and Pro subscribers

    Gemini 4 Argon is rolling out today to Google Ultra and Pro subscribers in @GeminiApp and Search, and to Ultra subscribers in @antigravity. Demis Hassabis praises its knowledge work and coding capabilities, sharing a wizard chess game they asked it to create as an example of its physics understanding and 3D modeling capabilities that reminds him of Battlechess on the Amiga. More information and full availability: https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-argon/

    The same story is shown in the picks as 《Gemini 4 Argon is now available worldwide as Google's most capable frontier model》

10/13Tue
  1. Latent Space84

    Meta launches Muse Wonder and Muse Wonder Full frontier models

    Meta Superintelligence Labs released Muse Wonder and Muse Wonder Full, its second-generation model family for personal and professional agentic work. Latent Space reports that Wonder is available through the Meta Model API and Muse Code, while Full is limited to Muse Code subscribers. Artificial Analysis gives Wonder Full and Wonder Intelligence Index v4.3 scores of 56 and 52, respectively, versus Opus 5.5’s 58; Wonder supports a 1M context window and text, image, video, and audio inputs, with text-only output.

    Why it matters: The benchmark breakdown compares Wonder variants with competing models on agentic performance and token usage, showing how their relative standing changes across tasks rather than a single ranking.

  2. The Decoder83

    Meta releases Muse Wonder models with gains in coding and agent benchmarks

    Meta released Muse Wonder and Muse Wonder Full, scoring 52 and 56 on the Artificial Analysis Intelligence Index v4.3, versus 48 for Muse Spark 1.3. Both support a one-million-token context window and text, image, video, and audio inputs, with stronger coding and agent results but weaker factual knowledge than leading competitors. Muse Wonder is available through Meta's API and Muse Code at higher prices than its predecessor; Muse Wonder Full is currently limited to Muse Code users.

    The same story is shown in the picks as 《Meta releases frontier models Muse Wonder and Muse Wonder Full》

  3. Alexandr Wang · X82

    Meta releases Muse Wonder and Muse Wonder Full with higher benchmark scores

    Meta has released Muse Wonder and Muse Wonder Full, according to Artificial Analysis in the post quoted by Alexandr Wang. At max reasoning effort, they score 52 and 56 on the Artificial Analysis Intelligence Index v4.3, compared with Muse Spark 1.3’s 48, following a release gap of 6 weeks. Muse Wonder is available through Meta’s first-party API and Muse Code, while Muse Wonder Full is limited to Muse Code users; both have 1M-token context windows.

    Why it matters: Artificial Analysis’s comparison highlights a tradeoff between Muse Wonder’s improved benchmark performance and its higher cost per task relative to Muse Spark 1.3 despite lower output token usage.

  4. TechCrunch · AI83

    Meta releases frontier models Muse Wonder and Muse Wonder Full

    Meta’s Superintelligence Lab released Muse Wonder and Muse Wonder Full on Monday, larger models described as more capable than Muse Spark. Meta reports benchmark scores comparable to Fable 5.1 and GPT-6 Astra and emphasizes information retention through “active memory.” Wonder costs $4.00 per million input tokens and $20.00 per million output tokens through the Meta Model API; Wonder Full is available only to Muse Code subscribers. Both models will power the Muse agent.

    Why it matters: The split between API access for Wonder and subscriber access for Wonder Full gives developers a concrete basis for comparing integration options within Meta’s new model lineup.

  5. Artificial Analysis · X81

    Meta releases Muse Wonder and Muse Wonder Full with higher Artificial Analysis scores

    Meta has released Muse Wonder and Muse Wonder Full, which Artificial Analysis reports score 52 and 56 on its Intelligence Index v4.3 at max reasoning effort, versus 48 for Muse Spark 1.3 (max). Muse Wonder Full improves across all ten evaluations, with the largest gains in agentic evaluations. Muse Wonder is available through Meta’s first-party API and Muse Code at launch, while Muse Wonder Full is limited to Muse Code users; Muse Wonder costs $4.52 per Intelligence Index task.

    The same story is shown in the picks as 《Meta releases Muse Wonder and Muse Wonder Full with higher benchmark scores》

10/12Mon
  1. 量子位47

    Zhipu’s GLM-5.3-Highspeed Model Reportedly Reaches 490 Tokens per Second

    Zhipu has added the GLM-5.3-Highspeed model to Zcode, where it is being gradually rolled out to subscribers at random. A user reported a generation speed of 490 tokens per second; the article says the model is currently available for testing in Zcode 3.15.1. The reported speed has not been independently confirmed, and the author says they could not access the model.

10/8Thu
  1. Latent Space82

    Anthropic releases Claude Haiku 5.5 at GPT-6 Luna pricing

    Anthropic released Claude Haiku 5.5, the first Haiku-series update in about a year. Its pricing matches OpenAI's GPT-6 Luna. On the same day, Anthropic also cut prices for Sonnet 5.5 and its subscription plans.

    Why it matters: The report brings together Haiku 5.5 pricing, independent evaluations, and token-usage data to help assess cost tradeoffs for small models in agent workflows.

  2. 量子位80

    Anthropic releases Claude Haiku 5.5, matching GPT-6 Luna on benchmarks while cutting prices by 90%

    Anthropic released Claude Haiku 5.5. Its official benchmark scores exceed GPT-6 Luna across the board, making it the new small-model gatekeeper, replacing DeepSeek V4.1 Flash and GLM-5.3-Flash.

    Why it matters: The source provides Haiku 5.5 benchmark scores, tiered pricing, and a migration checklist, helping readers judge which execution-layer tasks it can take on.

10/7Wed
  1. Ars Technica · AI76

    Mistral releases 1-trillion-parameter Mistral Large 4, codenamed Le Chonk

    Mistral released its 1-trillion-parameter Mistral Large 4, codenamed Le Chonk. Anyone can use and customize it for free during the preview; the final version is due by the end of the month. The model is for general tasks, with additional optimization for coding, cyber defense, and fields such as manufacturing, finance, and electrical engineering. Mistral says Le Chonk is the strongest open-weight model outside China so far and comes close to some closed models. The company emphasized that it was trained from scratch rather than distilled.

    Why it matters: Mistral is using a 1-trillion-parameter open-weight model to target specialized areas such as coding and cyber defense, offering a view of how the gap between open and closed models is changing.

10/6Tue
  1. Mistral AI80

    Mistral launches public preview of Mistral Large 4, a 1-trillion-parameter multimodal model

    Mistral released a public preview of Mistral Large 4, a natively multimodal model with 1 trillion parameters and 49 billion active parameters. Its weights will be opened at the end of the month.

    Why it matters: Mistral outlined the open-weight release schedule for its 1-trillion-parameter multimodal model and its investment in European compute, allowing comparison with mainstream open models.

  4. AWS Machine Learning Blog62

    GLM 5.3 launches on Amazon Bedrock

    Zhipu AI (Z.ai)'s GLM 5.3 is now available on Amazon Bedrock. The 753B-parameter mixture-of-experts model targets coding and long-horizon agent tasks and is offered through a fully managed API with cross-region inference, prompt caching, and service tiers. It is currently available to eligible enterprise customers.

    Why it matters: The source describes how to access GLM 5.3 on Bedrock and use prompt caching, helping readers assess the cost impact of managed inference for coding and long-horizon agent workflows.

  5. Ollama · X65

    Reflection AI announces Beam, a 501B-parameter agentic open model

    Reflection AI introduced Beam, described as a highly efficient agentic open model with 501B total parameters and 23B active parameters. The company says it advances the Western open frontier on coding and agentic tasks and was trained end-to-end from scratch, with full weights to be released this month. Ollama said more US models are coming to its platform.

    Why it matters: Reflection AI's Beam is presented as an efficient agentic open model with 501B total and 23B active parameters, giving readers a concrete comparison point for open-weight coding and agentic models.

10/1Thu
  2. Google DeepMind88

    Google DeepMind releases frontier model Gemini 4 Argon

    Google DeepMind released frontier model Gemini 4 Argon, initially making it available through the Fairwind Program to trusted cybersecurity defenders before gradually expanding access. Pricing starts at $2 per million input tokens and $10 per million output tokens.

    Why it matters: Gemini 4 Argon raises the maximum output to 1M tokens and reports multiple benchmark results and internal deployment data, offering a view of its limits on long-running workflows.

9/30Wed