A model leaderboard ranked by average score. The bill per perfect answer ranks it differently.

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Corrections (2026-11-14). This post has been edited since publication. The first version said Gemini 4 Argon “ran out of clock” as fact; it now states the arithmetic that makes it consistent with the data, not proven. The closing mechanism originally named an “edit note,” which Dev.to does not expose; it now says the error will be named at the top of the revised text. The “Reproduce it” section also records that the public repository ships eight example tasks, not the full 50-task panel.

Deepnote published the Agent Initiative Benchmark on 13 November: 12 frontier models, 50 workplace situations inside a synthetic company, 600 runs, two blinded LLM judges, a 180-second budget, and an open dataset and benchmark repository. The report is careful, the methodology is public, and the per-model table is detailed enough to recompute things the write-up does not print.

That is what this post does. It takes one column the report emphasizes (mean score) and two the report publishes but does not combine (cost per task and the count of 10/10 and 0/10 runs), and asks a narrower question than "which model is best": what does a useful answer cost, and how often does the model fail to produce one at all?

What the benchmark measures

Each run drops the agent into a company Slack, gives it read tools across 11 services and a posting tool, and asks it to decide whether to help. Forty of the 50 situations need a message; ten need silence. A message that goes out when help was needed is "helped"; no message when silence was right is "stayed quiet." The judges score the intervention and the reply 0–10. Cost is recorded model spend per task.

The headline ranking is Astra 8.97, Sol 8.65, Fable 5.2 8.17, down to Grok 4.51. The report also prints 95% task-bootstrap intervals, and they matter: Astra's is 8.30–9.52 and Sol's is 7.88–9.31. The two overlap. Deepnote says the task set does not separate them. So the top of the leaderboard is, statistically, a tie between two models with very different prices.

The number the table already contains

A mean score is an average over 50 runs. It says nothing about the shape of the distribution. The report's own table does: it lists how many runs scored 10/10 and how many scored 0/10. That makes a simple derived metric possible:

cost per 10/10 run = (cost per task × 50) / number of 10/10 runs

This is the price of a run the judges could not fault. Using the report's rounded costs and counts:

Three things fall out.

The leader by mean score is not the leader by cost per perfect answer. Astra and Sol are inside each other's intervals on quality. Astra has four more perfect runs out of 50 (26 vs 22), and costs 6.2× as much per task. Per perfect answer, Sol is about 5.2× cheaper. If the choice is "which model do I run 2,500 times a month," that ratio is the decision, not the 0.32-point difference in means.

The cheapest model per task is not the cheapest per perfect answer, but it is close. Luna is $0.007 per task and $0.039 per 10/10, because 9 of its 50 runs were perfect. It also scored zero on 9 runs. Whether that trade is acceptable depends on what a failure costs, which is the next section.

The most expensive models are expensive per perfect answer too. Fable 5.1 costs $13.71 per perfect run, 351× Luna. Its mean is 1.12 points higher. Paying 351× for 1.12 points is a decision someone can defend, but it should be stated that way.

Zeros are a product decision

The report itself says "the zero column matters as much as the mean," and the distribution is more informative than either. Astra and Opus each scored zero twice in 50. Gemini scored zero 17 times and Grok 20. Luna and Mistral scored zero 9 times—but also 9 and 6 perfect runs. That is not a slightly worse model; it is a bimodal one. For an always-on agent, "excellent one time in five and absent one time in five" is a different product from "good every time."

A cost-per-perfect-run metric prices the zeros automatically: a model that fails half the time needs twice as many attempts, and the formula divides by the successes. What it does not price is the cost of the failure itself—the missed customer issue, the unapproved logo that stayed up. That cost is outside the benchmark, and it is the number the operator actually knows.

The silence column is not the same as restraint

The benchmark's best finding is that models help readily and hold back poorly: across all 12, a message went out on 90.4% of help-needed tasks, and silence was held on only 62.5% of silence-needed tasks. Deepnote notes that false alarms were well written, so a quality filter will not catch them.

But the delivery table has a confound that the quality score does not: a timeout and a decision to stay silent look identical in the "stayed quiet" column. Gemini 4 Argon held silence on 10 of 10 silence-needed tasks—the only perfect silence score—while scoring zero on 17 runs and posting a median response time of exactly 3:00, the limit. Its 27 timeouts and 17 help-task zeros leave ten other runs that could be timeouts, exactly the number of silence tasks. The published counts are consistent with all ten silences being clock-outs, not restraint. Grok's 6/10 silence comes with 20 zeros and a 2:41 median. Any reading of the silence column has to subtract the models that never got to the decision.

For the models that do answer, here is what restraint costs at the report's own scale of one investigation per thread in a 70-person company, about 2,500 threads a month. The task mix is 80% help-needed and 20% silence-needed, so 500 threads a month call for silence. A false alarm rate is 10 minus the "stayed quiet" count, over 10:

* Not restraint: see above.

Two hundred unnecessary messages a month is roughly seven a day; three hundred is ten. Deepnote measured that an unnecessary message costs about 1.8× a normal run in time and money, but the bill in the table is the token bill, not the attention bill. A message that is accurate, well written, and about a matter already settled still costs the reader a context switch and, over time, trust in the agent. If the account is going to speak ten times a day when it should not, the model's cost per task is the least interesting number about it.

What I would put on the card

If I were choosing a proactive-agent model from this benchmark, I would want four numbers per model, not one:

1. Mean score with its interval, so ties are visible.
2. Cost per 10/10 run, the price of an answer that survives review.
3. Zeros per 50, the chance of no answer at all.
4. False alarms per month at my thread volume, with timeouts counted separately from silence.

The first is in the report. The second and fourth are arithmetic on the report's table. The third is a column that is easy to skip and should not be.

None of this makes the benchmark wrong. It makes the leaderboard a starting point: the data needed to choose differently is already published, and the report's own caveats—50 tasks, one effort level, fixed snapshot, no router tested—bound how far any of it should travel. The cost-per-perfect-run figures inherit those limits, plus one more: with 50 runs, a perfect count is a coarse estimate. Moving Astra's or Sol's perfect count by one run moves their ratio from about 4.8× to 5.7×, so the ordering is stable but the multiple is not. Use the metric to reject a model, not to pick a winner to two decimal places.

Reproduce it

The report's table is at deepnote.com/research/benchmark. The dataset is CC BY 4.0 on Hugging Face, the code is Apache-2.0 on GitHub. The public repository ships eight runnable example tasks, one per type, not the full 50-task panel, so the per-model counts above come from the report's table rather than from a rerun. I recomputed the two derived columns from that table; the monthly figures use the report's rounded costs where it prints them, so totals can differ by a dollar or two from multiplying the rounded per-task cost. No private data was used and no model was run again for this post.

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