Open models have gotten really, really good. So why are so many companies still sticking with closed ones? DeepSeek, Qwen, GLM and Kimi can already compete with (and often beat) the last closed models most companies use in production, like Sonnet, Haiku, Terra, Luna or Gemini. And they can do it at a fraction of the cost. Yet plenty of teams are still hesitant. We've been thinking about why, so we wrote a piece on it. A few things worth looking at: → Price per token doesn't tell you much. What matters is how much you pay for a task that actually gets done right. DeepSeek V4.1 Flash's latest update brought that cost down 54%, without changing the price. → DeepSeek isn't just opening its models. It's also releasing the inference engine and harness. That's a pretty big deal when you consider how much of this used to be locked inside the labs. → "We don't want our data going to China." Fair concern. But using a model trained in China doesn't mean your data goes there. It depends on where you run it and who's operating the infrastructure. → "We don't want to manage GPUs." You don't need to. Just like you don't need to run your own database servers to use PostgreSQL. Or your cloud. We're still treating open models as if quality were the main obstacle. For a lot of real-world use cases, it isn't anymore. The questions companies need to answer are where the models run, who operates them, and how much control you have over the whole thing. And those are choices companies can already make today. Because with open models you have control. Full piece: helmcode.com/posts/open-vs-c…

🐳 @deepseek_ai V4.1 Flash 1017 is now on Helmcode too. Same size, same architecture. DeepSeek focused on getting more out of what the model already had, and the gains are impressive. From DeepSeek's model card: → Terminal-Bench 4.0: 31.2 → 42.3 → ProgramBench: 20.3 → 29.1 → Automation-Bench: 54.8 → 60.5 → DeepSWE v1.1: 74.2 → 77.0 Artificial Analysis points the same way. Its Intelligence Index goes from 39 to 45, and the average cost per evaluated task drops from $0.49 to $0.26 on its test suite. Around 47% less. We also wanted to see what it does with a real project, so we ran two tests on design and video! 1/ Signal / Noise. Same creative brief for DeepSeek V4.1 Flash 1017 and Claude Opus 5.5: design and build a website from scratch. Both got to a good result. Pretty impressive advance from DeepSeek, to be honest. You have a video comparing both solutions. 2/ A motion graphics piece about what happens when machines begin to dream. Built entirely in code, with no video generation model. Opus 5.5 level on video too for a piece of cost. Improvement from 1017 is savage! Haven't tried it? Give it a full project, let it use tools and check what it built, how long it took and what it consumed. You can use it with flat rate, zero-prompt retention and EU servers at Helmcode :) Full write-up and the video: helmcode.com/posts/deepseek-… Both websites, screen by screen: helmcode.com/design-challeng…

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🐳 NEW DeepSeek V4.1 Flash 1017 is now live on nan.builders! Impresive post-training/update from one of oure most loved models. Improved for coding, agentic workflows and design with better reasoning. - 1M-token context window - Multimodal: text and image - Thinking mode Soon on Helmcode Enterprise plans too!

Tomorrow at AWS Community Day Spain, @crstiax explains how to size an LLM deployment on your own infra. Weights are the easy part. KV cache and batching decide whether a GPU serves 2 users or 20. And the secret sauce about what we are doing at Helmcode. Sat 17 Oct, 16:10, Santiago de Compostela @awscommspain @AWSCommunity

Which open model should you use instead of Claude, GPT or Gemini? Clients ask us this every week, so we built an equivalence table: which open model to test first if you’re using Opus, Haiku or Sol today. Benchmark rankings alone aren’t a source of truth, so we compared closed and open models by class and by workload. What you’ll find in the article: helmcode.com/posts/open-weig… An equivalence table: closed → open Benchmark numbers and where the models are close The best open model for each workload Routing as an architecture to save time, money and resources In short, after these last few weeks, this is our starting point. Not gospel, just guidance: Reasoning and knowledge work → MiMo V2.6 Pro Frontier coding and agents → GLM-5.3 Agentic coding with a better balance → GLM-5.3 Flash Tools and automation → DeepSeek V4.1 Flash High volume, basic RAG → Qwen3.6 / Qwen3.8-Flash-Next Long context → Kimi K3 Opus and Astra still win on certain problems, but we’re keeping an eye on the next open releases 👀 The setup that works best for us is routing each request to the smallest model that solves it well, and escalating to a frontier model only when it’s needed. In six months some of these names will have changed. The method won’t: take a few hundred real tasks, run them blind against your current model and two or three candidates, and measure cost per completed task. Benchmark your own workload before deciding anything.

Last week we ran our first conference. Here’s a short recap of Kernel Panic, one week later. We filled the Auditorio Casa del Lector at Matadero, Madrid. Every seat taken, people standing at the back, and nobody left early. Four talks on running open AI at scale in production, with links to their slides: → David Yunta @YuntaDavid and Luis Valerio (Inditex Tech): Kumoss, the Inditex Tech platform for building infrastructure with AI, made for developers inditextech.github.io/kumoss… → Sergio Paniego @SergioPaniego (@huggingface): Training a coding agent through a harness you did not write huggingface.co/spaces/FineEn… → Sergio Guijarro (@Cloudflare ): How Cloudflare rolled out agents to everyone in the company kernelpanic.sguijarrodepoo.c… → Cristian Córdova @barckcode (@helmcode ): How to scale inference for 1,000 users helmcode.com/slides/kernel-p… To everyone who came, thank you. You showed up, you asked hard questions and you stayed for the conversations afterwards. That’s the whole point of these events. To Hugging Face, Inditex Tech and Cloudflare: thank you for building this with us. Pictures at kernelpanic.helmcode.com See you at the next one. Because there will be a next one, and it’ll be bigger :)

🕹️ Your AI agents shouldn’t stop working because you closed your laptop. NaN Workspaces are live for your personal subscription. We’ve been building the best open-model AI subscription out there at nan.builders: good models, billions of tokens, a few of them unlimited. The missing piece was somewhere to actually run your agents. Until now, that meant renting a VPS, setting it up yourself and babysitting it. A Workspace is your own Linux machine in the cloud, with Hermes, Pi and gentle-shell preinstalled. Open it, start an agent, close the lid. It keeps running. And it’s the same session everywhere. Kick off a long task from your desk, check on it from your phone on the train, pick it back up at home. Nothing to reconnect, nothing to redeploy. Leave a scraping agent running overnight, read the results over coffee. Let a coding agent grind through a refactor while you’re in a meeting. Run a research pipeline all weekend without your laptop open. Your first Workspace is included with NaN Premium. On the regular NaN plan, add one from €4.99/month. nan.builders/docs/workspaces

You can use DeepSeek without sending a single token to China. Same for GLM and Qwen. We download the weights, deploy them on our own infrastructure, and serve them without a single request going through DeepSeek, Z.ai or Alibaba’s APIs. At Helmcode, we run them on European infrastructure: → prompts and responses are processed in the EU → the model developer never receives your data → we don’t store prompts or responses → no international data transfer for inference → you can sign a DPA with a European provider We've created a page explaining every detail helmcode.com/security/open-m… And there’s another big part: cost. - Running 10B tokens/month through Claude Opus 5.5 via API is around $72,000 at list price. - Our Growth plan is €1,299/month and includes up to 15B tokens across our open models. It means running a strong model in production no longer has to mean sending your data to a US or Chinese provider and paying frontier-model prices for every token. Your data is yours.