Grok 4.6 · Media / benchmark · Editorial analysis
Article conclusion Based on the ten-row evaluation table released by xAI, the author counted each result: among the 9 rows with scores for both Grok 4.6 and GPT-5.6 Sol Max, Grok 4.6 won 6 and lost only on DeepSWE v1.1 and Terminal-Bench v3.0; in the ten-row c。
Unverified: the original source could not be rechecked. Historical figures below are not current verified results.
Based on the ten-row evaluation table released by xAI, the author counted each result: among the 9 rows with scores for both Grok 4.6 and GPT-5.6 Sol Max, Grok 4.6 won 6 and lost only on DeepSWE v1.1 and Terminal-Bench v3.0; in the ten-row comparison with Fable 5 Max, Grok won 3 rows and lost 7.
Intelligence Index: Grok 4.6 and Sol Max both scored 61, while Fable 5 Max scored 62.
GDPval-AA v2: Grok scored 1753, above Sol's 1728 and Fable's 1741.
AA-Briefcase: Grok scored 1577, above Sol's 1502 and slightly above Fable's 1574.
Harvey LAB: Grok scored 15.8%, versus 2.5% for Sol and 11.3% for Fable. The author argues that this sixfold gap looks more like a signal of differences in evaluation criteria or model adaptation, and should not by itself be used to prove a gap in legal capability.
CursorBench v3.2: Grok scored 69.9%, slightly below Fable's 70.5%.
DeepSWE v1.1: Grok scored 65.9%, below Sol's 73% but clearly above Grok 4.5's 54%.
Terminal-Bench v3.0: Grok scored 26%, below Sol's 34.6% and Fable's 34.1%.
APEX-Agents: Grok scored 57.5%, above Sol's 56.7% but below Fable's 59.2%.
The author points out that xAI's table mixes different reasoning efforts and different test harnesses: Grok 4.6 and Grok 4.5 use High, while the competitors use Max; the competitor figures are also self-reported or publicly available scores. Therefore, "won 6/9" is a count of the published figures, not a new experiment under uniform conditions.
This article is useful for identifying strengths and gaps in the scorecard, but it cannot replace testing on the same codebase and under the same Agent harness.
The figures, task set, reasoning tier, and client conditions apply only to the listed source and collection snapshot. Different versions, harnesses, or providers must not be compared directly; undisclosed parameters remain unknown.
For a reproduction, fix the model version, provider or client, reasoning tier, tools, task-set version, sample count, and collection date, and record failures, retries, and human corrections. Full steps are in the source notes below.
Medium / Data Science Collective · Mehmet Özel · Original publication date 2026-08-13 · Site edit date 2026-09-20
Open original sourceGrok 4.6
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