What Happens When the Cost of Intelligence Drops 100x

What Happens When the Cost of Intelligence Drops 100x Progress in large language models is usually reported as what the best model can now do that no model could do before. That is the direction that produces headlines, and it has indeed been truly incredible. Each step up at the top of the range lets a model handle a kind of task that was previously out of reach, whether that is fixing a bug that spans a whole codebase or, lately, making progress on outstanding mathematical problems that had not been solved by anyone. There is a second direction of progress that gets less attention, which is how cheaply a given level of capability can be bought. A great deal of useful work does not require the smartest model available, but a model that is good enough, applied many thousands of times. Reading every scientific paper on a topic, checking every contract in an archive for a particular clause, or summarizing every thread in a large discussion forum are tasks of this kind. For these, the question is not whether a model exists that can do the job, but whether it can do the job ten thousand times within a budget. The ceiling unlocks new kinds of tasks; the floor unlocks volume. When you pick a model for an application you are trading off how capable it is against how much each call costs. For agentic coding I have focused almost entirely on capability, with the general sense that the improved quality of the work is worth the money, even when far cheaper models exist that are reasonably capable. My attention was recently drawn to the cost of the floor. We are measuring how often datasets shared on the DANDI Archive are reused in later publications, which means reading on the order of ten thousand candidate papers with a model and asking of each one whether it actually reused the data. At today’s prices a full pass over the corpus costs a little over a hundred dollars with a model whose capability was at the frontier in the spring. At the prices of this past March, the same pass with the same level of capability would have cost several thousand dollars, and a year ago that capability was not available at any price. That change in the floor is what turned the analysis from a thing we could do on a sample into a viable project. I have been surprised by the progress across the cost spectrum, particularly how intelligent cheap models have become. Artificial Analysis has been benchmarking intelligence and price across hundreds of models for a couple of years, and enough of that data is accessible to reconstruct the tradeoff. In particular, this plot shows the intelligence index vs. the cost per task, providing a realistic cost estimate for different levels of model capability. The top line is what they define as the “Pareto line,” the most capable models at a given price point. This line describes the true frontier of LLMs. I pulled data from artificialanalysis.ai and looked at how the Pareto frontier has moved as new models have been released. I think it is worthwhile to take a beat to review this progress and make some predictions for the next few months. The short version: the level of intelligence that cost $1.22 per task in February costs $0.022 today, a 56x drop in under six months, and the rate of decline is accelerating. At the measured pace, a 100x drop for a given capability level takes about a year, and the question worth asking is not whether that happens but what it changes. The Artificial Analysis Intelligence Index The capability axis throughout this post is the Artificial Analysis Intelligence Index, so it is worth being clear about what that number is. The current version, v4.1.1, is a weighted average over nine evaluations grouped into four categories: agentic tasks at 34%, coding at 24%, scientific reasoning at 24%, and general capability at 18%. The weighting reflects where the field’s attention is: a third of the score comes from a model’s ability to complete multi-step agentic work, not from answering exam questions. The component evaluations, their weights, and the scoring details are documented in Artificial Analysis’s intelligence benchmarking methodology. What you end up with is a single number that represents model capability, sort of like an IQ for LLMs. It isn’t perfect, and two models with the same score may have different strengths, but I have found that this score does a reasonably good job of indicating a model’s capability. As a reference point, Anthropic’s “Claude 4.5 Sonnet (Reasoning)” was for me and many others the first time a model felt capable enough to use in an agentic harness for writing code. At the time I was using Cline, and this model provided substantial productivity gains over auto-complete and copy/paste workflows. That model had an intelligence score of 37.4 (based on today’s intelligence scoring system). The top current model is Claude Opus 5 max effort, at 63.1. To give a more visceral sense of what the different index levels mean, I borrowed Simon Willison’s pelican benchmark: prompt a model with “Generate an SVG of a pelican riding a bicycle” and look at what comes back. It is not what the index measures, but it is a task anyone can judge by eye. The panels below use the GPT-5.6 family at four points on the index: Luna at low, high, and xhigh effort, and Sol at max effort. I generated three samples per model and show the first one; all of them are in the site repository. The progression is visible: more detail, better proportions, and a pelican that is clearly riding the bicycle instead of hovering over it. Measuring Cost per Task Cost per token is easily available, but different models can use a very different number of tokens, so a better indication of the cost of a model needs to take this into account. Cost per task is Artificial Analysis’s own measured number: the average cost in USD to run one task from their Intelligence Index evaluation suite, including the input, reasoning, and answer tokens actually billed during the run. The website displays it but the free API tier does not include it, so I scraped it from the data embedded in each model’s page on the site, covering both the models they currently benchmark and retired models whose pages still carry the measurement (older Claude Opus and Sonnet versions, the GPT-5.x line, and others). That yields measured cost for 137 models reaching back to DeepSeek V3 in December 2024, each paired with a release date and an Intelligence Index score on the current scale. How the Frontier Has Moved The chart below plots intelligence against measured cost per task and traces the Pareto frontier, the cheapest way to reach each intelligence level, as it stands today and as it stood at two month intervals over the past year, using each model’s release date to reconstruct what was available. The chart builds up one frontier at a time, pauses on the full picture, and repeats; use the button to stop it. Hover any point for the model behind it. Each successive frontier sits above and to the left of the last: more intelligence at the same cost, or the same intelligence for less. The pace of that movement is accelerating. Through the second half of 2025 the frontier inched forward: only two small bumps between August and October, and a single one between October and December. The February and April frontiers each moved a large part of the curve, and the last two snapshots have replaced the frontier almost entirely, with ten of the eleven June frontier models new since April and fifteen of today’s sixteen new since June. The right edge tells the capability story. The ceiling of the frontier rose from index 35.3 in August 2025 (GPT-5 at $0.26 per task) to 37.4 that October (Claude 4.5 Sonnet), 48.4 in February (Claude Sonnet 4.6), 55.0 in April (Claude Opus 4.7 at $2.23), 62.1 in June (Claude Fable 5 at $3.14), and 63.1 today (Claude Opus 5 at $2.34): twenty eight Intelligence Index points in a year. The left half shows the rising intelligence of cheap models. As of August 19, 2026, the GPT-5.6 Luna effort ladder now owns almost everything below index 52, with the level that was the August 2025 ceiling available for $0.0088 per task. The June 2026 frontier was unusually dominated by open weights models: six of its eleven models were open (MiMo-V2.5, DeepSeek V4 Pro, MiniMax-M3, and GLM-5.2 among them), and they held the whole middle of the range from index 38 to 53. In earlier snapshots open models appeared only at the bottom of the range, and today, after the GPT-5.6 Luna release, only two of sixteen frontier models are open. Note that a single model can cover a large part of this range through different reasoning levels: the GPT-5.6 Luna ladder runs from $0.0088 at low effort to $0.047 at max and covers the whole lower half of the frontier, while Claude Opus 5 spans $0.43 at low effort to $2.34 at max and buys about ten Intelligence Index points along the way. Effort is now a key dial in the cost/intelligence trade-off, and as a consequence, cost and intelligence are inextricably linked. The records plot below tracks the cheapest measured cost per task achieved by any released model at or above a given Intelligence Index tier. The series reaches back to mid 2025 for the lower tiers, and higher tiers appear when they become available. | Tier | First measured crossing | Cost collapse | Halving time | |---|---|---|---| | Index ≥ 30 | Aug 2025 (GPT-5 high) | 29x | ~73 days | | Index ≥ 40 | Feb 2026 (Claude Sonnet 4.6) | 56x | ~28 days | | Index ≥ 50 | Mar 2026 (GPT-5.4 xhigh) | 35x | ~29 days | | Index ≥ 60 | Jun 2026 (Claude Fable 5 max) | 3.8x | ~34 days | A capability level is first reached by a large frontier model at a premium price. After some time, cheaper models arrive at the same level, and the record steps down by an order of magnitude or more. The ≥ 40 tier opens with Claude Sonnet 4.6 in February at $1.22 per task, undercut within two days by Gemini 3.1 Pro Preview at $0.33; MiMo-V2.5-Pro, an open weights model, cut the record