Methodology
AI Model Timeline tracks the release of frontier AI models since 2019 and records, for each one, what it scored on the benchmarks that mattered at the time, what it costs to call, and, for open-weight models, what it takes to run it yourself. Two views sit side by side: Cloud for proprietary and API-served models, Local for open-weight models you can download. The catalog runs through 2026-09-01 and is edited by hand; the checks below keep the editing honest.
What earns a model an entry
A model gets an entry if it meets at least one of two tests: it is a milestone release from a tracked company (a new generation, a first of its kind, or a shift in what its class of model can do), or it carries an externally verifiable signal, meaning an LMArena Elo rating, an Epoch Capabilities Index entry, or a reported score on a tracked benchmark. Everything else stays out. Without that rule the Local view would drift into a mirror of Hugging Face: there are tens of thousands of fine-tunes, and a derivative with no independent signal adds noise, not coverage.
Benchmark eras
Benchmarks saturate. When the top models bunch at the ceiling, a benchmark stops telling them apart and a harder successor takes over. The timeline records this as eras. Era 1 (2024 to mid 2025) is MMLU, GPQA Diamond, SWE-bench Verified and Humanity’s Last Exam. Era 2 (from Q3 2025) replaces MMLU with MMLU-Pro and SWE-bench Verified with SWE-bench Pro. Era 3 (from 2026) adds Terminal-Bench 2.x for agentic coding in real terminal environments and MMMU-Pro for multimodal reasoning. Terminal-Bench 3.0 is tracked as its own axis because it runs on a different scale than 2.x; a 3.0 score is never entered in the 2.x field.
An era only hands over once at least three models carry both the old and the new value, so the two generations can be compared across the handover. A saturation check and a coverage check run on a schedule: the first asks whether a benchmark is topped out, the second whether the field still reports it. The second exists because in August 2026 the leadership line froze, not because progress stopped, but because the benchmark the race was gated on had been quietly abandoned by new releases.
Where the numbers come from
Every benchmark value is numeric or absent, never a string, and should carry a source. Sources are ranked: independent evaluations first (Vals AI, SWE-rebench, Epoch AI, LiveBench), official vendor announcements second, aggregators last. Vendor tables are read with cherry-picking in mind; when a vendor figure and an independent measurement disagree by more than three percent, the independent value is recorded and the vendor figure kept as a note. The current top value on every active benchmark must be sourced, and the data validation fails otherwise: an unsourced record is the signature of a data error.
Comparative vendor claims (“beats X”, “matches frontier models”) are never copied into descriptions. The underlying score is entered with its source and the comparison is left to the reader.
Who leads, and when it is too close to call
The leadership lines on the Cloud timeline show which model held the overall and the coding lead at each point in time. From 2026 on a model enters the race with a coding signal (SWE-bench Pro or Terminal-Bench 2.x), GPQA Diamond, and at least three of the four era benchmarks. Scores are min-max normalized over the race pool before they are averaged, so a model does not gain by omitting the benchmark it would score worst on. A challenger needs a lead of one normalized point over the incumbent to take the crown, and only a model released after the incumbent can challenge; inside that band the line stays put and the health check reports the lead as contested rather than settled.
Image and video models carry no academic benchmark. Their crowns come from the LMArena text-to-image and text-to-video boards. Robotics models likewise get no scores and no leadership tracking: no tracked benchmark applies to a robot control policy, and vendor task-success rates are not comparable across fleets.
Hardware classes for open-weight models
The Local view sorts every open-weight model into a hardware class from the memory it needs at 4-bit quantization plus a 15 percent framework overhead: Mobile/NPU up to 4 GB, Edge up to 24 GB, Workstation up to 80 GB, Multi-GPU up to 300 GB, Frontier above that. The class is computed from total parameters, never authored, and mixture-of-experts models are measured on all their weights: every expert has to be resident before the first token, so a sparse model needs its full footprint regardless of how few experts fire per token. Active parameters are shown separately because they predict throughput once the weights are loaded, which is a different question from what can load them.
Within each class, one pick per task (all-round, coding and agents, reasoning) is computed from the benchmarks that task ranks on. A class is a budget, not a size bracket: a workstation can run everything an edge GPU runs, so the candidates for a larger class include every model a smaller one holds, and where the larger budget buys nothing the card says so instead of inventing a difference. Picks that rest on two candidates, on one, or on a margin inside the measurement noise are labelled rather than hidden.
Arena crowns and open weights
The open-weight LLM crown on the Local macro timeline follows the Epoch Capabilities Index, a capability measure that reacts faster than community preference. The coding crown follows the LMArena WebDev board. The board’s license field is not trusted: a model counts as open weight only if its Arena name is mapped to a catalog entry whose weights are actually published, and unmapped entries are surfaced for curation by the same health check that watches benchmark coverage.
Automation
Model announcements are picked up weekly from vendor feeds and filed as issues for review. LMArena thrones are synced daily, the Epoch index and the open-weight-versus-proprietary gap on a schedule, and the benchmark health report runs monthly and opens an issue only when there is a decision to make. None of it writes a benchmark value on its own; every score in the catalog was entered and sourced by a person.
Data, code and attribution
The catalog and the code that renders it are published in the public repository at github.com/FXND-DE/AI-Status; terms of use are those stated in the repository. The site is vendor-neutral and carries no affiliate links. It is edited by Felix Neuland and published by R3ASON GmbH, Frankfurt am Main.