Technical Report No. 16 · Harness Study No. 02

Grok 4.6 in Grok Build vs. Cursor vs. a bare-bones harness

2026-08-18 · 23 tasks · 277 runs · 3 harnesses · 4 effort levels · v3 suite
Abstract.

Inside xAI’s own harness the effort knob finally does what it promises. Grok 4.6 in Grok Build climbs 84.1, 88.4, 89.9, 92.8 across low, medium, high and xhigh: monotone, every step up buying accuracy. Neither other system behaves that way. Through the raw API the same knob is non-monotone and collapses at high, from 87.0% to 73.9%; inside Cursor it is nearly flat, moving 4.3 points across the whole range and peaking a level early. The 92.8% at xhigh is the highest pass@1 VulcanBench has recorded for Grok 4.6 on this suite in any harness, 14.5 points above the raw API at the same setting and 7.3 above Cursor’s best. Grok Build is also the fastest of the three at every level, finishing a high-effort task in a median 4.5 minutes against the API’s 16.3. The gains are not free: token use nearly triples from low to xhigh.

Harness Study card: Grok 4.6 scores 84.1, 88.4, 89.9 and 92.8 percent across effort levels in Grok Build, against 84.1, 84.1, 88.4 and 85.5 inside Cursor and 82.6, 87.0, 73.9 and 78.3 through the bare-bones API loop
Card. One line per harness on a shared effort axis, whiskers ±1 stderr. Only the Grok Build line rises at every step.

The only knob that goes straight up.

EffortGrok BuildIn CursorBare-bones APIGrok Build time/taskAPI time/task
low84.1% ±6.984.1% ±7.582.6% ±8.12.4 min4.8 min
medium88.4% ±5.484.1% ±7.287.0% ±7.24.3 min14.2 min
high89.9% ±5.388.4% ±6.573.9% ±9.44.5 min16.3 min
xhigh92.8% ±5.185.5% ±7.278.3% ±8.84.1 min15.8 min

System C (Grok Build) is the grok CLI 1.0.5 on a grok.com plan, three attempts per task (69 to 70 runs per level, 23/23 tasks), effort sent with --reasoning-effort and read back from the session receipt on every run. System B is Report No. 15, three attempts per task. System A is Report No. 14, one attempt per task. All three are graded by the same deterministic hidden tests in Docker. pass@1 is the mean per-task success rate; times are medians and include provider latency. Grok Build clears the bare API’s error bars at high and xhigh; its margin over Cursor sits inside them at every level.

1.

The effort knob works, and only here. 84.1, 88.4, 89.9, 92.8 is monotone across all four settings, an 8.7-point climb. The raw API over the same knob is non-monotone with a collapse at high; Cursor is flat within 4.3 points and peaks a level early. Of the three delivery systems, xAI’s own is the only one where paying for more reasoning reliably returns more correctness.

2.

92.8% is the best result this suite has seen from Grok 4.6. Higher than any level of either other harness, and 14.5 points above the raw API at the same xhigh setting. pass@3 reaches 95.7%, which is 22 of 23 tasks solved at least once.

3.

A task nothing had ever solved fell. sqlglot-canonicalize-internal-names went unsolved in every run of Report No. 15, by both the API and Cursor, at every setting. Grok Build solves it 2 of 3 times at medium. pennylane-trotter-fragmented, also never solved by the raw API, climbs 0/3, 1/3, 2/3, 3/3 across the knob: the effort curve reproduced inside a single task.

4.

Both vendor harnesses share a blind spot the minimal loop does not have. itertools-strip-prefix is solved by the bare API at all four levels but fails 12 of 12 Cursor attempts and 11 of 12 Grok Build attempts, and the failure is identical in both: not a missed fix but a regression, breaking an existing passing test while making the change. Richer scaffolds edit more broadly, and on this task breadth is the defect.

5.

Speed and accuracy moved together. Grok Build is fastest at every level while scoring highest, finishing high-effort tasks 3.6 times faster than the raw API at the same setting. Its own time curve is nearly flat from medium up, so the extra tokens at xhigh buy accuracy without buying latency.

What the extra effort costs.

Effortpass@1pass@3Prompt tokensCompletionAPI-equivalent
low84.1%91.3%40.4M454K$28.93
medium88.4%95.7%80.6M986K$55.71
high89.9%95.7%92.7M1.20M$63.88
xhigh92.8%95.7%112.4M1.38M$75.13

Cost is counterfactual value at xAI list prices, not cash: these runs billed a grok.com subscription, so marginal cash was zero. Prompt tokens include cache reads, which dominate agentic loops. Across the sweep that is $223.65 of API-equivalent value for 277 runs. Grok Build is the first subscription harness VulcanBench has measured that reports enough per-run detail to price at all; Cursor reported none.

EffortSolved every attemptMixedNever solved
low18/2332
medium18/2341
high19/2331
xhigh21/2311

Consistency tightens as effort rises: 21 of 23 tasks solved on every attempt at xhigh, with a single mixed task. Only itertools-strip-prefix goes unsolved above low.

What this study can and cannot attribute.

Grok Build is xAI’s harness running xAI’s model, so this study cannot separate scaffold quality from first-party advantage: prompt tuning, serving, and model variant all sit on the same side of the comparison. There is direct evidence of the last one. VulcanBench requested grok-4.6 and the session receipt reports grok-4.6, but the CLI’s own usage ledger names the served model grok-4.6-build. Whether that is a distinct checkpoint or an internal billing label is not visible from outside, and it is the same class of unknown that “Cursor Grok 4.6” raised in Report No. 15.

On the measurement side: attempt counts differ across systems (3 vs 3 vs 1), so the bare-bones column carries the widest error bars and its per-task results turn on single runs. Costs for the bare API in Report No. 14 are lower bounds, because xAI reports reasoning tokens outside completion_tokens and the harness under-counted them during that sweep, so cost is not comparable across systems; the Grok Build figures here are fully measured. Grok Build’s advantage over Cursor is inside the error bars at every level, and only its margin over the bare API at high and xhigh clears them. The integrity audit certifies that no run reached the web or the benchmark’s own files; it cannot certify what was in the model’s training data.

VulcanBench v3: 23 tasks from real merged post-cutoff PRs (Python 9, TypeScript 4, Rust 4, Go 3, JavaScript 3). All three systems are graded by the same deterministic hidden tests in Docker; model judges disabled. System A ran 2026-08-12 (Report No. 14), System B 2026-08-15/16 (Report No. 15), System C 2026-08-17/18 with grok 1.0.5, three attempts per task at each of four levels. Grok Build runs used a custom kernel sandbox profile (Seatbelt) that permits workspace writes while denying reads of the VulcanBench checkout, an agent workspace created outside the repository, web tools removed from the agent entirely, and cross-session memory disabled so repeats cannot recall each other. Every run carries an integrity audit of both leak channels and all 277 are clean. Effort is not taken on trust: each run records the level the CLI reports it actually used, and all 277 match what was requested. pass@1 is the mean per-task success rate, so uneven attempt counts do not bias it. One level, xhigh, failed to start on its first attempt and was rerun in full.

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