Benchmark Almanac

Novel compiler task (Anthropic)

Claude Opus 5 80.9% leads since Jul 2026 Only in system cards

Agents build a compiler for an unusual programming language from a specification and test cases.

In-house Anthropic evaluation. The tasks are not public.

Made by Anthropic Released May 2025 Machine learning engineering Tasks kept private Last in a system card Jul 2026 Last model added Jul 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
Code A program or patch run against tests.
4
Models scored in lab cards
11
System cards report it, from 1 lab

Claude Opus 5 leads with 80.9% among models released by Jul 2026

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.

The newest model with a score here came out in Jul 2026, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:

  1. 1 Claude Opus 5 80.9%
  2. 2 Claude Mythos Preview 77.2%
  3. 3 Claude Sonnet 5 76.1%
  4. 4 Claude Opus 4.7 71.1%
#ModelScoreSettingDateSource
1 Claude Opus 5 80.9% Jul 2026 Model card
2 Claude Mythos Preview 77.2% Apr 2026 Model card
3 Claude Sonnet 5 76.1% Jun 2026 Model card
4 Claude Opus 4.7 71.1% Apr 2026 Model card

2 models have held number one since its release in May 2025

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

Anthropic
Lab claims Not tested since Jul 2026

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% 2026 Near the ceiling (85%) Claude Opus 5 Claude Mythos Preview 0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) Claude Opus 5

Labs report it only in system cards

We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark. Some system cards do, below.

11 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
24 Jul 2026 Claude Opus 5 system card 80.9 Novel Compiler, pass rate on complex tests (Table 2.3.5.A) AI R&D risk
30 Jun 2026 Claude Sonnet 5 system card 76.1 Novel Compiler, pass rate on complex tests 76.1% (Table 2.3.3.A) AI R&D risk
16 Apr 2026 Claude Opus 4.7 system card 71.1 Novel Compiler, pass rate on complex tests (Table 2.3.4.A) AI R&D risk
7 Apr 2026 Claude Mythos Preview system card 77.2 Novel Compiler, pass rate on complex tests 77.2% (Table 2.3.3.A) AI R&D risk
17 Feb 2026 Claude Sonnet 4.6 system card Novel compiler, 93.7 of basic tests and 67.27 of complex tests passed, no overall (6.3.3.6) AI R&D risk
5 Feb 2026 Claude Opus 4.6 system card Novel compiler, two test sets with no overall: 98.2% of basic tests and 65.83% of complex tests passed, threshold 90% on complex tests (8.3.3.6) AI R&D risk
24 Nov 2025 Claude Opus 4.5 system card Novel compiler, two test sets with no overall: 93.7% of basic tests and 69.37% of complex tests passed, threshold 90% on complex tests (7.3.2.6) AI R&D risk
29 Sep 2025 Claude Sonnet 4.5 system card Novel compiler, two test sets with no overall: mean pass rate 81.7% on basic tests and 29.7% on advanced tests, threshold 90% (9.3.3.6) AI R&D risk
5 Aug 2025 Claude Opus 4.1 system card addendum Novel compiler, no overall: mean pass rate 74.4% on basic tests and 6.81% on advanced tests (6.4.1) AI R&D risk
22 May 2025 Claude Sonnet 4 system card Novel compiler, no overall: mean pass rate 50.00% on basic tests and 17.08% on advanced tests. The 7.3 summary says 52.96% on basic tests (7.3.3.6) AI R&D risk
22 May 2025 Claude Opus 4 system card Novel compiler, no overall: mean pass rate 64.44% on basic tests and 9.44% on advanced tests (7.3.3.6) AI R&D risk

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