Benchmark Almanac

APEX-SWE

Grok 4.6 56.4% leads since Aug 2026 In use

Software-engineering productivity tasks spanning integration work and observability, such as diagnosing failures from telemetry.

The leaderboard uses 200 held-out cases. Mercor open-sourced 50 in-distribution cases with labels. Tasks are graded by unit tests plus human-written rubrics.

In the authors’ words

We introduce the AI Productivity Index for Software Engineering (APEX-SWE), a benchmark for assessing whether frontier AI models can execute economically valuable software engineering work. Unlike existing evaluations that focus on narrow, well-defined tasks, APEX-SWE assesses two novel task types that reflect real-world software engineering: (1) Integration tasks (n=100), which require constructing end-to-end systems across heterogeneous cloud primitives, business applications, and infrastructure-as-code services, and (2) Observability tasks (n=100), which require debugging production failures using telemetry signals such as logs and dashboards, as well as unstructured context. We evaluated eleven frontier models for the APEX-SWE leaderboard. Claude Opus 4.6 leads the APEX-SWE leaderboard with 40.5% Pass@1, followed by Claude Opus 4.5 at 38.7%. Our analysis shows that strong performance is primarily driven by epistemic discipline, defined as the capacity to distinguish between assumptions and verified facts. It is often combined with systematic verification prior to acting. We open-source the APEX-SWE evaluation harness and a dev set (n=50).

Abstract of APEX-SWE (arXiv:2601.08806, CC0)
Made by Mercor Released Jan 2026 Fixing real repositories Public tasks plus a private test set Last in a launch post Aug 2026 Last model added Aug 2026
Level
Expert Specialists with years of training, often over hours of work
Scored
Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
Answers
Agent task Many steps in a terminal, browser, desktop, app, game or robot.
2
Models scored in lab cards
1
Launch post lists it, from 1 lab
2
System cards report it, from 1 lab

Grok 4.6 leads with 56.4%

Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.

  1. 1 Grok 4.6 56.4%
  2. 2 Grok 4.5 51.2%
#ModelScoreSettingDateSource
1 Grok 4.6 56.4% Aug 2026 Model card
2 Grok 4.5 51.2% Jul 2026 Model card

2 models have held number one since its release in Jan 2026

Lab claims count from the day the card came out, independent runs from the model’s release.

xAI
Lab claims Not tested since Aug 2026
Grok 4.6 56.4%

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

Reported by the lab in its model card Best so far
0% 20% 40% 60% 80% 100% Near the ceiling (85%) Grok 4.6 Grok 4.5 0% 20% 40% 60% 80% 100% Near the ceiling (85%) Grok 4.6

One lab put it in a launch post in the last six months

Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.

xAI
DateLaunch postScoreAs the post writes it
12 Aug 2026 Grok 4.6 56.4 APEX-SWE

2 system cards report it, from 1 lab

Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.

DateDocumentScoreAs the document writes itSection
12 Aug 2026 Grok 4.6 model card 56.4 APEX-SWE, Pass@1, high, Terminus-2 harness, run by Mercor (2.2) Capabilities
14 Jul 2026 Grok 4.5 model card 51.2 APEX-SWE, Pass@1, high, run by Mercor (2.3) Capabilities

More in Coding

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  • SWE-bench Multilingual In use 300 GitHub-issue tasks across nine programming languages in SWE-bench format. Patch graded by repository tests. Claude Opus 5.5 93.9%
  • LiveCodeBench v6 In use Competitive programming problems collected after model cutoffs from LeetCode, AtCoder, Codeforces. Scored pass@1 on hidden tests. DeepSeek-V4-Pro 93.5%
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  • SciCode In use Scientist-curated research coding problems split into subproblems across natural sciences. Code checked against test cases. Seed2.1 Pro 59.8%
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