Compute Wars: OpenAI vs Anthopic. Why was Opus 4.5 such a breakthrough? Anthropic got lots more compute from AWS Madison and New Carlisle sites likely more than doubling their capacity. This got Anthropic got close to OpenAI's total capacity, and probably much higher effective capacity available for new model... runs. Remember that it takes 6+ months between getting capacity and releasing the model, so the extra OpenAI capacity might be aligning well with the 'spud' model rather than GPT-5.4. Unless something dramatic happens, OpenAI will pull away in terms of compute available in H2 2026, but 2027 will be close. Future years are less certain but so far OpenAI has much higher planned capacity, though can't imagine Anthropic isn't pushing as hard as they can to get lots more compute. Always watch the compute, other things matter, but any new capability breakthrough probably came from throwing more compute at it.show more

Interactive visualisation: Github:

A lot of the starting point for data came from @EpochAIResearch's excellent resource with some additional research (mostly backward looking) and some extrapolations

The compute race is the arms race nobody outside the industry tracks closely enough. Models don't get better because of one clever paper. They get better because someone got access to another 50,000 GPUs six months ago. Anthropic closing the compute gap with AWS explains the Opus jump better than any architectural innovation. Follow the GPUs, not the press releases.

the compute race is the new arms race everyone talks about model architecture but infrastrcture decides who wins anthropic locking in aws capacity before openai could react was a chess move

great visual, thanks for making this

as an end user who runs opus 4.6 all day the compute constraints are obvious. rate limits hit hard even on max plan. but the quality when you have capacity is unmatched. its frustrating because the model is clearly capable of more, its just capacity gated

@grok fact check this. Also, are there some public figures related to the distribution of compute usage? For example, let's say OpenAI allocates half of its resources to video models while Anthropic allocates 0, this will significantly change the status here

Assuming they don’t run out of steam… or a breakthrough pushes acceptable consumer compute to edge devices.

What assumptions for projection?

More "compute" or a "faster" AI doesn't necessarily mean a "smarter" AI.

the 6 month lag between getting capacity and shipping a model is the part people always forget when comparing these companies

OpenAI 拥有强大的计算能力，能够利用包括 AWS 和 Cerebras 在内的多个服务器资源。 此外，还有 Epoch AI 未曾记录的其他 Stargate 站点。总体来说open ai拥有更多的算力

Is this adjusted for them bailing on ram purchases?

Great analysis. The compute gap matters for training, but the Stanford paper showing 6x performance gap from harness engineering alone suggests deployment-side architecture might matter even more than raw training FLOPS for end-user impact.

Hate to break it to you cuz but nobody is getting any more compute while the entire oil infrastructure of the middle east is in peril.

Yeah, and the hidden lag is systems work. More compute helps, but training stability and eval loops usually eat months first.

How does Google compare over 26’ 27’?

it was never a war

compute is capex with better marketing. the underrated story is who converts FLOPS to capability most efficiently, not who has the most GPUs. Anthropic punching above weight on that ratio is the real signal here.

The real advantage comes from how efficiently companies turn that compute into better training runs, faster iteration, and stronger products.

The compute story explains so much of what happened in the last 6 months.

Capacity is a good signal but if it were the dominate one then why isn’t gemini tops across the board? They arguably have more than both

how does this compare to xAI. great chart

They are just starting to train new models on Blackwell setups, most of what we see now was never trained initially on the latest series. Will likely see them in Q2 and Q3.