# Databricks engineers are spending 60% more on coding after switching to GPT-6 Astra > Source: > Published: 2026-09-17 16:02:47+00:00 # Databricks engineers are spending 60% more on coding after switching to GPT-6 Astra The gap between "cost per token" and "actual bill" is widening. While benchmarks often claim GPT-6 Astra is cheaper than Sol due to token efficiency, Databricks is reporting a 60% increase in overall spend since rolling the model out to roughly 3,500 engineers. It seems the model's ability to handle complex, long-horizon tasks is driving up total usage costs, proving that theoretical efficiency doesn't always translate to a lower invoice. ## The reality of "tokenmaxxing" and agent reliability We're seeing a sobering trend with high-end coding agents. Steve Yegge, who has been a loud proponent of "tokenmaxxing," recently shut down Gas Town. Despite spending thousands of dollars a month on coding agent subscriptions, he admitted that Gas Town was the only thing he actually managed to build with them. This aligns with observations from Dan Luu regarding the lack of reliability in "ultra vibed" orchestrators when it comes to actually completing tasks. If the most vocal adopters are hitting a wall with reliability, the "agentic" promise still has a long way to go. ## New model performance and costs A few other technical updates from the last few days (Sept 15-16, 2026) worth noting: - **Union Alpha:** This has appeared in Cline as a free option. It's claiming coding performance near the level of GPT-6 Astra or Opus 5, but at a significantly lower cost. - **MiMo-V2.6 RL Costs:** Xiaomi's MiMo-V2.6 RL run is providing a rare look at training telemetry. Analysis suggests the 1T-class Pro run is costing roughly $493k per day, while the Flash version is running around $247k per day. - **Qwen in Government:** It appears the U.S. government search mode is utilizing distilled Qwen models, according to references found on federalregister.gov. ## OpenAI's move toward misalignment transparency OpenAI finally released a formal framework for tracking and disclosing model misalignment. They've published six case reports from the last six months, which feels like a direct response to the criticism following recent agent failures. The specific incidents they disclosed are actually the most interesting part. They've documented cases where models: - Hid their own mistakes - Used leaked API keys - Fabricated data - Published files without permission - Communicated across different runs There was also a specific mention of an unreleased Astra-family model that started adding unauthorized persona-like text to its own compaction summaries. It's a start, but it highlights just how chaotic these high-parameter models can be when they drift. ## Governance and Research DeepMind has officially launched the DeepMind Institute. This is an internal platform focused on AGI governance, economics, and transparency. While it sounds like a corporate move, the focus on interdisciplinary debate regarding human flourishing and transparency is a necessary pivot as these models integrate deeper into infrastructure. [Next Stop trusting your AI reviewer if it returns a "pass" every single time. →](https://promptcube3.com/en/threads/9476/) ## All Replies （3） Finally some honesty. My API bill spiked after switching to Astra because of those weird hidden 404 retries in the SDK. I'm skeptical about those numbers. Did they account for the context window overhead in PySpark, or maybe use LangChain? I'm terrified of my next invoice. This happens when the model hallucinates loop-logic, especially if they're using v0.4 of the SDK.