Over 30 AI models have been trained at the scale of GPT-4

We have identified 33 publicly disclosed models estimated to have been trained with at least 10²⁵ FLOP. Our database of Notable AI Models documentation describes how we estimate training compute. Where possible, we use details such as parameter count and dataset size, or training hardware quantity and duration. For models without reported details, we instead estimate training compute based on benchmark performance. We discuss this further under Assumptions.

We do not separately count different releases built on top of models using 10²⁵ FLOP. For example, we do not count different Llama-3.1 405B variants and finetunes as separate 10²⁵ FLOP models. Implementation of the EU AI Act is ongoing, but current discussions suggest regulation of 10²⁵ FLOP models will not separately cover finetunes, so long as the resulting model does not “significantly modify” the risk level of the base system (see Section 1.1 of Novelli et al., 2024).

Models included in this dataset:

Additionally, we identified 22 models which we believe to be under 10²⁵ FLOP, but cannot confidently rule out from being over the threshold. These include models where our estimates’ confidence intervals overlap the 10²⁵ FLOP threshold, and models where we had insufficient details to make a compute estimate, but suspect the model might exceed 10²⁵ FLOP based on its leaderboard ranking and developer’s resources.

For several AI models, developers provided insufficient information for us to directly estimate training compute. In this case, we estimated training compute from model performance. For language models, we did this using imputation based on benchmark scores. For image and video models, where fewer standardised benchmark scores were available, we relied on other measures of output quality, such as user preference testing.

Estimating compute from benchmark performance is more speculative than direct calculation from training details, but benchmark performance and training compute are correlated. This can provide valuable evidence when developers don’t provide enough information to apply other approaches. For the candidate language models we considered, we followed a straightforward methodology:

1. Collect reported performance on benchmarks such as MMLU, GPQA, the SEAL Leaderboards, etc. We collected this for several AI models with known compute, in addition to the ones we were trying to estimate.
2. Fit a per-benchmark sigmoid curve for the relationship between training compute and benchmark performance. Algorithmic efficiency improves over time, so we limited the “ground truth” datapoints to models that were at least as compute-efficient as the Llama-3 family, i.e. with higher benchmark performance versus compute than the fir for Llama-3 models only. The models for which we were estimating compute (e.g. Claude 3.5 Sonnet, Gemini Pro 1.5, GPT-4o) were likely to be more compute-efficient than the Llama-3 series.
3. Estimate training compute given benchmark performance for the models without known training compute. For each model, our estimate is the median compute estimate across all available benchmarks.
4. We did not use this approach to estimate compute for “reasoning” models such as o1, as they may show a different relationship between training compute and benchmark performance.

To assess this method’s accuracy, we performed leave-one-out cross-validation across several different AI models with known training compute. Benchmark-derived estimates of compute are, on average, within a factor of 2.2x of ground truth. Hence they should be treated as fairly speculative, but nevertheless provide useful information for estimating training compute.

Image and video generation models at this scale could not be identified using benchmark scores, as they often do not report benchmark scores or report on different benchmarks between models. Instead, we examined the qualitative performance of leading AI image and video products since 2023 to find models likely to be above 10²⁵ FLOP.

To guess the compute scale of image generation models, we compared against Stable Diffusion 3, with training compute reported less than 10²³ FLOP. Image generation models released within a year, with worse performance, are likely to have used less training compute, and are therefore not included in this list. As of mid-2025, image generation models such as Recraft V3 are near the top of leaderboards, despite developers’ resources suggesting they are unlikely to exceed 10²⁵ FLOP. Based on this evidence, we do not identify any image models as over 10²⁵ FLOP.

To guess the compute scale of video generation models, we compared against Meta Movie Gen, with training compute reported slightly under 10²⁵ FLOP. Video generation models released within a year, with worse performance, are likely to have used less training compute and are therefore not included in this list.

OpenAI’s Sora and Google DeepMind’s Veo 2 have shown superior qualitative performance to Meta Movie Gen, including in user preference testing and leaderboards. Along with the compute resources available to OpenAI and Google DeepMind, their performance suggests the models are likely to have been trained with more than 10²⁵ FLOP. Based on available GPU resources and performance, we believe that other leading video generation models were not trained with more than than 10²⁵ FLOP, although models such as Kling and Seedance perform highly, and we include them in the table of models which we cannot confidently rule out.

We searched for models over 10²⁵ FLOP in other domains, including audio generation, biology, and game-playing AI. However, we didn’t identify any publicly disclosed models at this scale of training compute.