The AGI Race Has Two Scoreboards. You Only Need One. — Hélder Vasconcelos<br>It is August, which makes this roughly the halfway mark, and I have lost count.

Somewhere around the fourth frontier release in six weeks I stopped keeping a mental table of which model was ahead. They arrive faster than anyone outside a lab can evaluate them. Each one lands with its own benchmark card, its own effort setting, its own quiet choice about how a failure gets counted, and by the time you have worked out whether two of the numbers can even be compared, a third model has shipped. Half of them are open weights now. That used to be the distinguishing feature, and it no longer narrows the field at all.

So I sat down to do the thing I had been putting off since spring, which was to work out properly which model we should be standardising on.

Then yesterday Mark Zuckerberg published fourteen pages arguing that American open-source AI needs fewer restrictions to compete with China, and shipped Muse Glimmer to prove he meant it. Meta has committed to opening the weights of Muse Spark 1.2 as well. That was the model it launched in April as its first closed, proprietary frontier system, the one that was supposed to end the Llama era.

That era lasted four months.

Most of the commentary has read this as a geopolitical story, and it is one. But if you build systems for a living, the geopolitics is downstream of something more useful. The question “which model should we use” has quietly stopped having an answer, and not because the answer is hard. It’s the wrong shape of question.

Your model choice is now a routing policy. Everything below is why, and how to build one.

Two scoreboards

The AGI race has two scoreboards and only one of them gets reported.

The reported one is capability. On the Artificial Analysis Intelligence Index, Claude Opus 5 sits at the top. Kimi K3 lands fourth, a couple of points behind GPT-5.6 Sol and Claude Fable 5. That is 2.8 trillion parameters, the largest open-weight release anyone has ever made, on Hugging Face since 27 July. It’s the best result an open-weight model has ever posted, and it’s still fourth. On the hardest reasoning evaluations the closed frontier keeps a lead of roughly three to eight points, and has for a year.

So there’s no parity at the top. Anyone telling you otherwise is reading launch slides.

INTELLIGENCE INDEX<br>Artificial Analysis v4.1.1 · 10 Aug 2026

Claude Opus 5

60.7<br>Claude Fable 5

59.9<br>GPT-5.6 Sol

58.9<br>Kimi K3<br>[open]

57.1<br>Claude Opus 4.8

55.7<br>GPT-5.6 Terra

55.0<br>GPT-5.5

54.8<br>Grok 4.5

53.8<br>Claude Opus 4.7

53.5<br>Claude Sonnet 5

53.4<br>GLM-5.2<br>[open]

51.1<br>DeepSeek V4 Pro<br>[open]

44.3

The reported scoreboard. Nine evaluations averaged into one number, and the gap between first and fourth is 3.6 points. Source: Artificial Analysis Intelligence Index v4.1.1, 10 August 2026.

The unreported scoreboard is what engineers actually chose when they had to ship. A year ago Chinese open-weight models carried under two percent of the tokens flowing through OpenRouter. They now carry more than forty-five percent. Four of the five most-used models are Chinese. Llama, the open-weight leader two years ago, has fallen off the rankings entirely.

Nobody live-tweets a routing curve. It went vertical anyway.

These two scoreboards disagree, and the disagreement is the whole story. The frontier labs are winning a contest about the ceiling. The people building things are answering a question about the floor, and the floor came up so fast that the ceiling became somebody else’s problem.

You only need one of these scoreboards, and it isn’t the one on the front page. Capability rankings tell you which models are worth putting in a bake-off. They can’t tell you what to ship. The second scoreboard you have to build yourself. What clears your bar, at what cost, on your workload. It’s the only one your bill responds to.

Why the floor is the interesting number

Every production system I have built in twenty years has been an exercise in finding the cheapest thing that clears the bar. Not the best thing. The cheapest thing that clears the bar, because the bar is set by the workload and everything above it is money you set on fire at scale.

Break the aggregate benchmark scores apart and you can see exactly where the bar got cleared. Kimi K3 takes the Frontend Code Arena outright. It leads on BrowseComp, SpreadsheetBench and Automation Bench, the evaluations closest to the work a business actually does. What it doesn’t win is frontier reasoning. Which is a precise description of most production workloads: a thin layer of genuinely hard decisions sitting on a mountain of tool calls, extractions, classifications, retries and formatting.

Then look at the price column. DeepSeek V4 Pro has been running about fifty times cheaper per Intelligence Index task than the model at the top of it. Fifty. There’s no three-point benchmark delta that survives that ratio on a workload you run a billion times a...