Not All AI Tokens Are Equal: A Real Guide to Cutting Costs

A model's per-token price does not determine actual cost; Kimi K2 at roughly half the per-token price of GPT-5.1 class models still lands close to the same cost per completed task at about 95 cents versus $1.04, according to artificial analysis benchmarks. Splitting coding workflows into plan, execute, and review stages and assigning different models to each stage can cut costs substantially while maintaining quality, and using two different models to review code catches more bugs than a single model reviewing its own work.

Not All AI Tokens Are Equal: A Real Guide to Cutting Costs Why cheaper models like Kimi K2 don't always cost less in practice, and how splitting AI tasks across models by price and skill saves real money. Why cheaper AI models don’t always mean cheaper AI bills The listed price per token for an AI model tells you almost nothing about what a task will actually cost. A model priced at half the rate of a competitor can end up costing the same, or more, once you account for how many tokens it actually burns to solve a problem. The real metric that matters is cost per completed task, which depends on both price per token and how efficiently a model uses those tokens its token density . Getting AI costs under control means comparing tasks completed per dollar, not sticker price, and assigning different models to different stages of a workflow based on what each one is actually good at. TL;DR Token price and task cost are different things : a model charging half as much per token can require twice as many tokens to solve the same problem, erasing the apparent discount. Token density , meaning how much useful work a model squeezes into each token, varies a lot between labs and changes which model is actually cheaper for a given job.- Benchmarks from artificial analysis show models like Kimi K2 and GPT-class frontier models landing close to the same cost per completed task despite very different per-token pricing. - Splitting a coding job into plan, execute, and review stages and assigning a different model to each stage cuts cost substantially while keeping quality high. Output tokens cost far more than input tokens , so the execution stage the one that generates the most output is where a cheaper, faster model saves the most money.- Using two different models to review the same code tends to catch more bugs than having one model review its own work, because different models make different kinds of mistakes. - The bigger story is a pricing battle between closed-source labs OpenAI, Anthropic and open-source models like Kimi , and which side wins will shape how AI tokens are priced for everyone. Remy doesn't build the plumbing. It inherits it. Other agents wire up auth, databases, models, and integrations from scratch every time you ask them to build something. Remy ships with all of it from MindStudio — so every cycle goes into the app you actually want. What is a token, and why does it matter for cost? A token is the basic unit a large language model reads and generates, roughly comparable to a word or word fragment. Short common words are usually a single token. Longer or less common words get split into pieces. Every input you send and every output the model produces gets counted this way, and providers charge separately for input tokens and output tokens, usually with output priced several times higher. Model providers publish per-million-token prices. As of the models discussed here, OpenAI’s GPT-5.1 class model runs around $5 per million input tokens and $30 per million output tokens. Anthropic’s Claude Opus-class model prices higher still. Kimi K2, an open-source model out of China, came in around $3 per million input tokens and $15 per million output tokens, about half the price of the GPT model on paper. The mistake is stopping the analysis there. A token isn’t a fixed unit of work. It’s a unit of language. How much reasoning, planning, or code a model packs into that unit varies by architecture and training, which is what people mean by token density. How much do token density differences actually cost you? Data from artificial analysis benchmarking shows the gap closing once you measure cost per completed task rather than cost per token. Kimi K2 averaged about 95 cents per completed task on their benchmark, compared to roughly $1.04 for the GPT-5.1 class model, an Anthropic Opus-class model came in noticeably higher, around $2.75 per task. Looking at cost to complete an entire benchmark suite told a similar story: Kimi and the GPT model landed close together around $2,700 and $2,800 respectively , while the Anthropic model came in around $5,600. The explanation is token density. Kimi K2’s per-token price is roughly half that of the GPT model, but it tends to take roughly twice as many tokens to solve the same task. Half the price times twice the tokens nets out to roughly the same total cost. So the “cheaper” model isn’t necessarily cheaper once a real task is completed. This is the core reason comparing sticker prices between models is misleading. The right comparison is dollars spent per task solved, and ideally on tasks similar to what you actually run. Speed adds another dimension. If a lower-priced model takes noticeably longer to reach the same answer, you’re also trading your own time for the token savings, which may or may not be worth it depending on how time-sensitive the task is. What does a multi-model plan-execute-review workflow look like? Since different models excel at different parts of a task, and since output tokens are the expensive part of any job, the most cost-effective approach is to split a complex task into stages and assign each stage to the model best and cheapest suited for it. A practical version of this workflow, applied to a coding task, looks like this: - ✕a coding agent - ✕no-code - ✕vibe coding - ✕a faster Cursor The one that tells the coding agents what to build. Planning stage : Give the hardest, highest-level thinking reading the codebase, spotting edge cases, writing a spec to the strongest frontier model you have access to, such as Claude Opus or GPT-5.1. This stage reads a lot of input the whole codebase but produces relatively little output a plan , so it’s input-heavy and comparatively cheap even with an expensive model. Execution stage : Hand the plan to a cheaper, faster model that’s still solid at writing code, such as Grok or a lower-cost coding-focused model. This stage is output-heavy since it’s generating all the actual code, and output tokens are the most expensive kind. Since output volume dominates cost here, using a cheaper model at this stage is where the real savings come from. Review stage : Send the finished code to a different frontier model than the one that wrote the plan, and have it check the implementation against the spec. This stage is input-heavy again reading the code and spec with light output bug reports , so cost stays manageable even with a premium model. In a worked example using this structure, running the entire task through a single expensive frontier model cost around $81. Running it entirely through a cheaper frontier model cost around $46.50. Splitting the same task across the plan-execute-review workflow brought the cost down to around $25.55, while arguably improving output quality since each stage used the model best suited to it. Does using two different models to review code actually catch more bugs? Evidence suggests yes. Different models tend to fail in different, somewhat predictable ways. One model might be more prone to missing behavior specified in a task, while another is more prone to misreading semantic intent. When one model authors a piece of code and a different model reviews it, the reviewer catches issues the original model is less likely to catch reviewing itself, simply because it approaches the problem differently. This is the same logic behind the plan-execute-review split: the value isn’t just in saving money, it’s in getting a second, structurally different perspective on the same problem. Why is there a pricing battle between open-source and closed-source AI models? Closed-source frontier labs like OpenAI and Anthropic sell access to their models as their core business, and they set prices to protect healthy margins on every token sold. Because only they and their infrastructure partners can serve their models, they have less competitive pressure to cut prices aggressively, though competition between the two labs still pushes prices down somewhat. Open-source models like Kimi K2 work differently. Because the weights are public, any cloud provider or data center operator can run and serve the model. That creates direct competition among hyperscalers to offer the lowest possible price for serving the same open model, which drives inference costs down over time regardless of what the original lab charges. If open-source models continue closing the capability gap with closed-source frontier models, token prices broadly should trend downward, since anyone can undercut anyone else on serving the same weights. That shifts where the profit sits in the AI industry, away from raw token margins and toward chipmakers, data center operators, and the application layer, since cheaper tokens tend to mean people use more of them. Frequently Asked Questions Is Kimi K2 cheaper than GPT-5.1 for coding tasks? Remy doesn't write the code. It manages the agents who do. Remy runs the project. The specialists do the work. You work with the PM, not the implementers. On a per-token basis, yes, roughly half the price. But because Kimi K2 tends to use about twice as many tokens to complete comparable tasks, the actual cost per completed task ends up close to GPT-5.1’s, based on artificial analysis benchmark data. What’s the difference between input tokens and output tokens in pricing? Input tokens are what you send to the model your prompt, codebase, context . Output tokens are what the model generates in response. Output tokens are priced significantly higher, often several times the input rate, so tasks that generate a lot of output like writing code are more expensive per token than tasks that mostly involve reading. Why use a cheap model for code execution instead of a frontier model? The execution stage of a coding task generates the most output tokens, which are the most expensive kind. Since writing code once a clear plan exists is a relatively contained task, cheaper and faster models handle it well, and putting the savings there has the biggest impact on total cost. Does using multiple models for planning, execution, and review actually improve quality, not just cost? Based on comparisons of models reviewing each other’s code, yes. Different models make different kinds of mistakes, so a second model reviewing a first model’s work tends to catch bugs the original model would miss reviewing itself. Will open-source AI models make token prices drop for everyone? If open-source models keep closing the gap with closed-source frontier models, broader pricing should trend downward, since any provider can serve the same open weights and compete on price. Closed-source labs face less of that pressure since only they can serve their own models.