Say goodbye to tokenmaxxing and hello to the calm of productivity
At the Stanford AI Summit last weekend, Arvind KC, Chief People Officer at OpenAI, asked the audience a question: How many of you find that AI has made you more productive? In response, almost every hand in the auditorium went up.
But then he asked a follow-up: *And how many of you are working less? *Only a couple of hands.
Many conversations at the summit came around to one thing: it’s not just the AI-native startups pushing the envelope that are starting to see real ROI from AI workflows and toolsets; a wide range of sectors, including healthcare and fintech, have leaned in and are starting to see results. The problem is now how to optimize humans-in-the-loop to get the most out of AI workflows and agentic engineering without losing our focus.
Many of us are working more because we’re simply so excited about the potential for this new technology, but we still need to keep costs low while maintaining flexibility in the stack.
Lately, AI builders have been swinging between two extremes:
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Tokenmaxxing: Stuffing every possible piece of context, RAG output, and history into a prompt to maximize context.📉
Tokenminimizing: Aggressively pruning prompts, dropping vowels, and obsessing over system instructions to save a fraction of a cent.
But a new trend is emerging among developers on Kilo. We’re calling it Tokenrelaxxing and I have a feeling it’s going to stick around a lot longer than the summer holiday.
The Art of Tokenrelaxxing
Tokenrelaxxing is the realization that you no longer need to manually sweat over every single token. In fact, you don’t even need to constantly try to optimize the best time to re-set your subscription allowance. It’s the shift away from hyper-optimizing inputs and back to what actually matters: productivity and shipping code.
How? By leaning on the latest models, and leaning into Auto Modes on a daily basis. The industry is playing around with a lot of different forms of auto-routing, and Kilo has been there since the beginning with optimization techniques based on how real developers use different models tied to different agents or modes.
Instead of manually playing cost-basis arbitrage across a dozen different API endpoints, Tokenrelaxxing lets Kilo’s Auto Model dynamically handle the heavy lifting. The engine optimizes your requests in real-time based on the ultimate developer formula: Quality × Optimized Speed × Cost.
The New Math of Frontier Models
A little while ago, in a post on * tokenmaxxing without breaking the bank*, I wrote about how to push context windows without maxing out your AI budgets. The response was fantastic, but if there’s one thing the AI development space guarantees, it’s that the meta shifts fast.
If you look back at the cost comparisons in my previous Tokenmaxxing post, the landscape has already fundamentally changed. In just the past few weeks alone, frontier labs have increased efficiency, even for high-reasoning models, while continuing to improve capabilities. At the same time, we’re seeing models like Kimi K3, Grok 4.5, and the newest generation of Qwen models completely redefine the price-to-performance ratio. They are making everything vastly more powerful and profoundly more affordable at the same time. At the same time as OpenAI releasing a suite of GPT models with major cost improvements and Anthropic unleashing Opus 5 with Fable-like intelligence, we tested every new model and found that MoonshotAI’s Kimi K3 and xAI’s Grok 4.5 could build the same database as Claude Opus 5 at 1/25th the price.
It’s truly an embarrassment of riches.
Compared to my earlier post on tokenmaxxing, tokenrelaxxing with Kilo can save you an additional 40-50% simply by assigning the best models to the tasks and modes at hand. It’s not rocket science. We’re just assigning the best model to the job within your chosen mode, and sometimes that’s still a frontier model like Sol, Luna or Terra, as OpenAI improved overall efficiency for 54% better token usage in agentic coding with this recently updated model family.
When frontier-level intelligence is this cheap and fast, micromanaging your token usage is actually a net negative on your engineering velocity. The time you spend shaving 50 tokens off a prompt is time you aren’t spending building features your users actually want.
Let Auto Mode Do the Work
With Kilo, Tokenrelaxxing becomes your default state. Instead of guessing which model fits the task, Kilo classifies the session intent in real-time and routes your request based on live benchmark data.
Here are the Auto Model routing tiers making Tokenrelaxxing possible:
Auto Efficient (The Game-Changer): This tier uses live session classification for smarter spend. According to Kilo’s latest stats, Auto Efficient delivers71% of published frontier completion at a massive 72% lower cost on KiloBench. It delivers near-frontier coding performance for pennies.Frontier: For when the work is incredibly complex and you need maximum capability. It automatically routes to the latest and most capable paid models.Balanced: Highly capable paid routing that avoids the absolute premium prices of frontier models, finding the sweet spot for everyday development.Free: Routes your requests to the best available free and experimental models providers offer at no cost.
Stop stressing and start tokenrelaxxing. You don’t have to choose between going broke or losing quality anymore.
Breathe in. Breathe out. And turn on your chosen auto model in the Kilo model picker, wherever you like to prompt — the CLI, VS Code extension, cloud agents, JetBrains extension and beyond.