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#open-weights

Everything tagged open-weights, across every stream.

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CVCommunity Views #Alibaba#community#local-ai#open-weights#qwen#reddit

r/LocalLLaMA cheers a Qwen statement that future Qwen 4 and Qwen 5 models will stay open source

A window into how much the open-weights crowd depends on Alibaba's Qwen, and how far it trusts a corporate 'forever' promise.

The post answers rumors from the previous few days that Qwen4 would be the last open-weight Qwen series and that later models would be available only through an API. It shares an image of a statement it attributes to Qwen head Liu Dayiheng, saying future Qwen 4.0 and Qwen 5.0 models will continue to be open source, that AI's value should not belong to only a few people, and that the series has passed 3 billion downloads, 460 open-source models and 300,000 derivative models. We could not find a separate report of the Qwen 4/5 wording; 36Kr reported very similar remarks, and the same figures, from Liu's first appearance as Qwen's leader at Alibaba's conference in Hangzhou on September 22.

It was the top thread of our sweep relative to community size: about 4,800 upvotes and 500 comments in a 329,000-member community. The most-upvoted replies celebrate the pledge; others praise how capable Qwen's models are for their size. Skeptics point out that a company's 'forever' promise lasts only as long as its current management and business conditions, and a few add that it holds only as long as China's government allows it.

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CVCommunity Views #ai-coding#community#glm#open-weights#reddit#revolut#self-hosting

r/LocalLLaMA on Revolut cutting its AI coding costs about 90% by self-hosting an open model instead of Claude

A rare inside look at the numbers behind a large company moving its engineers from a paid AI service to a model it runs itself.

The Register reports that Revolut says it cut its AI coding inference costs by roughly 90 percent in under a year by making self-hosted models its default rather than relying mainly on commercial services such as Claude. Speaking at the AI Engineer conference in New York, ops and infrastructure lead David Abitbol said the fintech rolled AI coding tools out to about 2,000 engineers, adopted Z.ai's open-weights GLM-5.1 and now serves GLM-5.3 on 56 Nvidia B200 GPUs. Self-hosting cost about $0.30 per million tokens, roughly a tenth of Codex or Claude Code, and he said cheap tokens let engineers hand the AI debugging and ticket work, which is where the biggest gains came from. He also called very long context windows "a scam" in practice.

About 1,900 upvotes and 340 comments in a 329,000-member community. The top reply says this is why running models locally is not a waste of time; another argues that self-hosting means a company knows exactly which model it is running, while a cloud provider can change it without notice. One commenter who says they know people at Revolut describes its engineering culture as cautious.

Cascadic Analysis 1
UndertowDelayed Consequence / False Success Problem

What hidden risk could pull against this, even if the news is good?

The savings are measured right away: about $0.30 per million tokens, roughly a tenth of Codex or Claude Code, and the biggest gains came from handing AI debugging and ticket work. The quality of that work shows up on a slower clock, in code that ships and tickets that come back. A 90% cost cut is real; whether the cheaper model's fixes hold up is a separate question the cost figure can't answer, and one worth tracking alongside it.

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MVMaker Views #ai-coding#cybersecurity#europe#Hacker-News#llm#mistral#model-release#open-weights

Mistral releases the weights of Mistral Large 4, its 1-trillion-parameter flagship

Mistral's post has the benchmark charts, the reasoning modes and the license terms; the thread argues over what 'open' means here.

Mistral released the weights of Mistral Large 4 (nicknamed "Le Chonk"), a 1-trillion-parameter multimodal model that entered public preview earlier in October. The company says three more weeks of reinforcement learning since the preview improved coding and defensive cybersecurity: it reports leading open-weight scores on Cognition's FrontierCode 2.0 (19.7%) and Terminal-Bench 4.0 (41%), and 51% on the Artificial Analysis Cyber Index, with a 96.6% refusal rate on malicious cyber prompts. These are vendor-reported results. It adds low, medium and high reasoning modes and says the model was trained from scratch on European infrastructure.

Weights are on Hugging Face under the "Mistral Ecosystem Open-Weight license," a modified MIT license: free to use, modify and commercialize, except that model-as-a-service providers need a separate commercial license. API pricing is $0.70 per million input tokens and $2.10 per million output tokens.

On Hacker News, a commenter noted the price is roughly half the preview's, another argued the license makes it "weights available" rather than open source, and several said its main draw is European provenance.

Cascadic Analysis 2
UndertowBenchmark scores can overstate real-world reliability

What hidden risk could pull against this, even if the news is good?

Most of the headline numbers come from Mistral's own release page, and the 96.6% refusal rate is an average over public test sets of malicious prompts (JailbreakBench, StrongREJECT, AgentHarm). Known prompts are what a determined user is least likely to send. Mistral itself says it sees "potential for uplift" toward real attacks and is adjusting its guardrails.

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UndertowAutonomous cyber capability scales attackers enormously

What hidden risk could pull against this, even if the news is good?

Mistral keeps its specialized cyber variant behind vetting and contracts, but the general model, at 51% on a vulnerability finding-and-patching index, ships as open weights that the license lets anyone modify and fine-tune. Its release page says built-in guardrails separate defensive from offensive tasks, without saying how they hold up once the weights are self-hosted. A downloaded model can't be recalled if that line proves thin.

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AIAI #China#kimi#model-release#moonshot-ai#open-weights

Moonshot's Kimi K3.1 Pro nears the top closed models on coding, but its open release is deliberately weaker at hacking

Moonshot's post has the demos and benchmarks; VKTR explains why the company stripped cyber training data before the open-weight release.

Chinese AI lab Moonshot AI launched Kimi K3.1 Pro and a faster Highspeed version on its apps and API, with full weights due by Nov. 4. It calls K3.1 Pro its most capable 3T-class open-weight model, with a 1-million-token context window. Moonshot reports 65.7% on Terminal-Bench 4.0, ahead of OpenAI's GPT-6 Astra (58.2%) and just behind Anthropic's Claude Opus 5.5 (66.4%), plus strong results on 3D/CAD and financial-document tasks.

VKTR reports that Moonshot removed cyber-related data from two training stages for the open release, which cut its success on an internal exploit-development benchmark from 90.5% to 81% (K3 scored 33.3%). Moonshot says it aims "not to release open weights that would extend the absolute frontier of cyber capability" and plans to raise the cyber capability in open weights gradually. Several of the cited benchmarks are Moonshot's own and not independently verified.

Cascadic Analysis 3
UndertowAutonomous cyber capability scales attackers enormously

What hidden risk could pull against this, even if the news is good?

Moonshot deserves credit for deliberately holding back, but the numbers show how high the floor is: the open release still solves 81% of its internal exploit-development tasks, against 33.3% for K3, the previous version. Moonshot also says it plans to raise the cyber capability in open weights gradually. Downloaded weights can't be recalled, so each step sets a permanent floor for anyone.

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UndertowPrivilege escalation + credential propagation

What hidden risk could pull against this, even if the news is good?

K3.1 Pro arrives with connectors to Gmail, Google Calendar, Drive, Notion and Obsidian, coding connectors to GitHub, Cloudflare, Vercel and Supabase, plus computer-use and browser plugins. Each connection is a token the agent holds, and deployment and code-hosting accounts reach much further than one chat. Enterprise admins must switch it on manually, which is the right default: grant the fewest connections a task needs.

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UndertowBenchmark scores can overstate real-world reliability

What hidden risk could pull against this, even if the news is good?

Several of the comparisons, including the exploit-development suite, are Moonshot's internal evaluations and not independently verified. The model trails badly on some others (31.5% on Program Bench against Claude Opus 5.5's 65.0%), so a near-tie on Terminal-Bench doesn't mean it is near the top for every job.

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MVMaker Views #benchmarks#deepseek#Hacker-News#llm#open-weights

DeepSeek releases V4.1-Flash-1017, an MIT-licensed "minor update" with sizable benchmark gains

The model card has the full benchmark table against GLM-5.3, GPT-5.6 Sol and Opus 5.

DeepSeek published DeepSeek-V4.1-Flash-1017 on Hugging Face, calling it a minor update to V4.1-Flash that brings substantial improvements in reasoning, coding, agentic tasks and visual benchmarks while cutting task time and output length. Its table shows, for example, Terminal-Bench 4.0 rising from 31.2 to 42.3 and DeepSWE v1.1 from 74.2 to 77.0, ahead of GPT-5.6 Sol on several tests and behind Opus 5 on others. The model has a 1M-token context window, weights are MIT-licensed, and DeepSeek released deepseek-recipe, Rust libraries with Python bindings for its prompt format.

On Hacker News, users said the model feels better than its benchmarks because of speed, visible reasoning and fewer refusals; others posted informal head-to-head tests and cautioned that single runs prove little. Some joked about calling big gains a minor update; a side debate asked whether US labs want regulation that would hurt open models.

Cascadic Analysis 2
UndertowBenchmark scores can overstate real-world reliability

What hidden risk could pull against this, even if the news is good?

The gains, such as Terminal-Bench 4.0 from 31.2 to 42.3, come from DeepSeek's own table on its model card. Hacker News users posted head-to-head tests of their own, and others cautioned that single runs prove little; how the model holds up on a team's own tasks still has to be measured there.

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UndertowLoss of meaningful human control as capability and autonomy increase

What hidden risk could pull against this, even if the news is good?

The weights are MIT-licensed and downloadable, and some Hacker News users liked the model partly for its fewer refusals. Once the weights are out, how the model is fine-tuned, what it refuses and where it runs is up to whoever downloaded it; DeepSeek can't recall or patch copies already in circulation.

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AIAI #frontier-models#meta#open-weights

Meta ships its first frontier model, Muse Wonder, then open-sources Muse Spark

Meta's own posts and TechCrunch's report show both halves of its strategy in one week: a paid frontier model and a big open-weight one.

Meta's Superintelligence Lab released Muse Wonder and Muse Wonder Full on Monday, its first attempt at a frontier model. By Meta's own testing they score comparably to Fable 5.1 and GPT-6 Astra, and Meta highlights "active memory", recall of what a user has told the model over weeks or months. Wonder costs $4 per million input tokens and $20 per million output tokens through Meta's API; Wonder Full is only for Muse Code subscribers.

On Thursday Meta also released the weights for Muse Spark Open, an 816-billion-parameter (42 billion active) multimodal model with a 1-million-token context window, which it calls the most capable US open-weight model so far. It is offered through hosting providers and on Hugging Face, and Meta says it runs on two Apple M5 Ultra Mac Studios.

Cascadic Analysis 3
UndertowBenchmark scores can overstate real-world reliability

What hidden risk could pull against this, even if the news is good?

The comparisons to Fable 5.1 and GPT-6 Astra, and the lead on MemoryBench, come from Meta's own testing in its model card. Memory over "days, weeks, months" is exactly the kind of skill a benchmark run can't fully show, because it only fails after real use has piled up.

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UndertowPersistent memory creates a new poisoning surface

What hidden risk could pull against this, even if the news is good?

Meta's headline feature is "active memory": the model keeps what you tell it and uses it later. Anything that gets into that memory, including a planted instruction or a wrong fact from a web page, can keep shaping answers long after the conversation that put it there is forgotten.

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UndertowLoss of meaningful human control as capability and autonomy increase

What hidden risk could pull against this, even if the news is good?

Muse Spark Open's weights are now downloadable and run on two Mac Studios. Once an open-weight model is out, nobody, including Meta, can recall it, patch it, or see how it is being used or modified.

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MVMaker Views #AI#Hacker-News#maker#open-weights

Mistral previews Large 4, a 1-trillion-parameter open-weight model

Mistral's post makes the case for open weights in security work, where a provider's refusal can stall incident response.

Mistral opened a public API preview of Mistral Large 4, a natively multimodal model with 1 trillion parameters, 52 billion of them active. The company says the weights will be released by the end of October; until then it is red-teaming the model with cybersecurity firms, vetted partners and state authorities. Mistral says it trained the model on 3,800 NVIDIA Grace Blackwell GPUs in its own European data centers and pitches it on AI sovereignty. Its performance comparisons are the company's own.

Cascadic Analysis 3
UndertowAutonomous cyber capability scales attackers enormously

What hidden risk could pull against this, even if the news is good?

Mistral pitches Large 4 as one of the strongest models for cybersecurity and is giving some partners a version with reduced moderation and expanded cyber capabilities. Once open weights ship at the end of the month, the same capability is available to anyone who downloads them, attackers included, with no provider able to switch it off.

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UndertowBenchmark scores can overstate real-world reliability

What hidden risk could pull against this, even if the news is good?

The claims of beating other open models come from Mistral's own comparisons, and it says more benchmarks and architecture details will follow later. Until independent tests exist, "state of the art among open models" is the company's description, not a measured fact.

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UndertowDelayed Consequence / False Success Problem

What hidden risk could pull against this, even if the news is good?

Red-teaming happens during a weeks-long preview, then the weights are released for good. Problems found after that point can't be patched in every copy that's already been downloaded.

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  • Mixture of experts

    After exploring Introducing Mistral Large 4, the technique behind "52 billion active" is Mixture of experts.

    A machine-learning technique that splits a problem among multiple expert networks, each handling its own region of the problem space. That's why only part of a large model runs for any given input.

    Wikipedia · Swim · 30 min

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MVMaker Views #AI#Hacker-News#MiMo#open-weights#Xiaomi

MiMo v2.6

Xiaomi’s model got builders talking not only about performance but about unusually transparent training and what 'open' should mean.

Xiaomi released MiMo-V2.6, a new open-weight model family that scored strongly against other open models while offering relatively inexpensive inference.

Why HN cared

The thread repeatedly praised Xiaomi’s transparency around training, including a live reinforcement-learning dashboard and detailed methodology.

That led to a deeper argument over what counts as open AI: open weights alone, or weights plus training data, code, methodology and reproducibility.

HN snapshot: about 1.1k points and ~480 comments.

Cascadic Analysis 2
UndertowWe don't completely understand why frontier models behave as they do

What hidden risk could pull against this, even if the news is good?

Transparent training details are valuable, but openness about ingredients is not the same as interpretability of learned behavior. Even a well-documented model can remain difficult to reason about in novel situations.

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UndertowAutonomous cyber capability scales attackers enormously

What hidden risk could pull against this, even if the news is good?

High-performing open or widely available models also diffuse capability. That democratizes useful development, but it can also lower the cost of automated phishing, reconnaissance and exploit adaptation.

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  • MiMo-V2.6 release page

    After exploring MiMo, the MiMo homepage covers the whole family; the release itself is here: MiMo-V2.6 release page.

    Xiaomi's own page for the v2.6 release, the page the Hacker News thread was reacting to.

    Xiaomi · Wade · 5 min

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  • The Open Source AI Definition 1.0

    The thread argued over what 'open' should mean for AI. This is the Open Source Initiative's answer, which asks for detailed training-data information, not just weights.

    Open Source Initiative · Swim · 30 min

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