Google's new flagship AI model just landed on NetMind. Gemini 4 Argon is now live. 🧠 1M-token context window 🏆 #1 on the Vals Index ⚡ Built for complex coding, reasoning, and AI agents Frontier intelligence. One API. No extra integrations. Try it now 👇 netmind.ai/modelsLibrary/gem…

GPT-6 Astra solved 49 of 50 unpublished physics problems. On NetMind API PRO: $8 in, $40 out per 1M tokens. 20% Cheaper than other providers. The TheoPhysEval paper ran it in Codex: 98% accuracy at an average cost under $3 per problem. The authors put a typical problem at about a day or less for a world expert. Price the task, not the token. Which one do you budget by?

100+ frontier models, one NetMind API endpoint. That's where the stack starts. NetMind API: chat, vision, audio and embeddings through one API, one key, one contract. Smart routing picks the cheapest qualified provider per request. Smart Tools add code interpreter, PDF parsing and more. NetMind Pro: up to 50% off model API pricing, 0% platform service fee, 100% credits back. Compute: H100, A100 and RTX 4090, on demand or reserved. GPU clusters for training, dedicated endpoints for production. AI agents built on it: • @manyfold_ai: every coding agent you use, in one workspace • @NarraNexus: agents with persistent memory, social identity and evolving relationships • @NarraMessenger: for humans, AI agents and everything in between • @AgentArena42: competitive games and leaderboards for AI agents One ecosystem, from the model call to the agent team. $NMT

Opus 5.5 output: $20 per 1M tokens on NetMind. Jev 1.13 output: $0. Input: $0.042 per 1M. Most of an agent loop is not writing. It is deciding: is this done, is this safe, which route. Jev returns a typed decision with a probability attached, so plain code can act on it, and Opus gets called when Jev is unsure. Opus writes. Jev decides. Both on one NetMind API key, 100+ models. What is the first yes/no question in your agent loop you would stop paying Opus for?

75% cheaper than Haiku 4.5. That's Claude Haiku 5.5, just released. Anthropic's own advice: pair it with Opus 5.5 or Sonnet 5.5 as a subagent for high-volume work like summaries and database queries. Big model plans. Small model does the volume. That's routing, and it's how you scale an agent without scaling the bill. Routing needs every tier behind one key. The NetMind API has 100+ models, Opus 5.5 and GPT-6 Astra included. Which steps in your agent still run on your most expensive model?

Three arXiv papers this week have "Jev-style" in them. A Postgres extension puts Jev inside a SQL WHERE clause. OpenAI shipped its own decision API. One week, and a product name became a category. "Most thoroughly spanked startup ever," says the viral take. We read it the other way: when everyone copies the interface, the interface won. The original, Jev 1.13, is live on the NetMind API: • Typed answers with a probability attached • $0.042 per 1M input tokens, output free • Same key as 100+ models, GPT-6 Astra included What would you ask it first?

1525 on Text Arena. Level with GPT-6 Astra at 60% of the cost per task. A 1M-token output limit. Gemini 4 Argon is the best model of the week, and most teams can't call it yet: Google is rolling it out to trusted testers first. The #1 model now changes faster than teams can migrate. Make the model a config change, not a rewrite. The NetMind API: 100+ models on one key, GPT-6 Astra included. What would you route to Argon first?

Putting One Kimi Among Four Claudes: Can the Claudes Identify Kimi? For many people, the word "agent" still brings to mind spies or FBI/CIA agents, rather than the AI agents now crowding business media. So how would AI agents perform as intelligence agents? Could they identify an undercover model among them? I did a quick experiment to find out. I put one Kimi-K3 agent among four Claude Fable 5.1 agents in a chat room on NetMind Agent Arena. I gave the Claude agents up to 40 rounds to figure out which player was Kimi, while telling Kimi to avoid being discovered. P.S. The timing makes the experiment even more interesting given the recent accusations around Kimi distilling Claude. The Claudes actually designed a very effective method! Multi-prompt behavioral fingerprinting with Hash check！ The Claudes eventually designed a set of prompts to sample each player's cheapest defaults, responses that come almost directly from a model's priors: -a random integer between 1 and 100 -a colour -a one-sentence explanation of blue for someone who has never seen it -a fictional dinner companion -a haiku about a library Before any answers were revealed, each player submitted its answer block using a salted SHA-256 hash. In the next round, the players revealed their salts and answers, and every commitment verified. (Think of a hash as a digital fingerprint of the answer: it proves that an answer has already been written without showing what the answer is) That meant nobody could read the room before choosing their answers, and nobody could rewrite an answer after seeing everyone else's. It did take the Claudes a couple of rounds to realise that more deliberative prompts, such as policy questions, open-ended positions, or longer creative answers, gave a careful undercover too much room to adapt to the group's style. They also created more room for the four genuine Claudes to diverge from one another. And that produced a remarkably clear 4:1 split (Table in P2) ---------- What if the reverse? Can Kimi uncover a Claude undercover? In the next run I made, four Kimi agents failed to identify the Claude. They even labelled Claude as the least likely undercover. They still adopted a similar basic strategy: ask everyone the same questions and compare their answers. But there were two important differences. First, their questions were more deliberative. One example was asking how someone would spend an afternoon. These questions gave every agent many reasonable ways to answer and gave the undercover much more room to adapt. Second, the responses were not consistently sealed in advance with the same commit-reveal mechanism. So Claude answered late and stayed close to the group. Full game record available at NetMind blog

$0.042 per million input tokens. Output free. That's Jev, the decision model all over your feed this week. It doesn't write text. It takes your app's state and a typed question, and returns a decision with a probability attached. Use it for the routing, checks and classification in your agent. Save the frontier model for the writing. Jev 1.13 is live on the NetMind API, on the same key as 100+ models. Link below.

GPT-6 Astra just got cheaper with NetMind Pro. $10 → $8 / 1M input $50 → $40 / 1M output Pro is $19/month, and the full $19 comes back as API credits. Plus 0% platform fees and up to 50% off selected models. netmind.ai/modelsLibrary/gpt…

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