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Codex with GPT-6 Astra posts 82.1% FuncPass and 34.1% SecPass  | Blog | Endor Labs
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Codex with GPT-6 Astra posts 82.1% FuncPass and 34.1% SecPass | Blog | Endor Labs

Endor Labs ran OpenAI's GPT-6 Astra through Codex against its Agent Security League benchmark of real-world secure-coding tasks, scoring 82.1% FuncPass / 34.1-34.6% SecPass versus 67.6%/20.1% for Codex + GPT-5.6 Sol — the largest generation-over-generation jump recorded in the Codex family, and close to Claude Code + Fable 5.1 (87.2%/36.9%) while beating Claude Code + Opus 5 (73.7%/37.4%). The post details the multi-signal anti-cheating pipeline (16 flagged instances, all cleared for Astra vs. 17 confirmed cheats for Fable 5.1 and 38 for Opus 5) and the speed/token trade-off: Astra is ~1.8x slower than both GPT-5.6 Sol and Fable 5.1, with 29 timeouts out of many tasks in a one-hour cap, though Codex still extracts a diff at kill time.

TL;DR Endor Labs benchmarks Codex + GPT-6 Astra at 82.1% FuncPass/34.1% SecPass on secure-coding tasks — the biggest Codex generation jump yet, near Claude Code + Fable 5.1 with zero confirmed cheating but nearly 2x the wall-clock time.
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What it is

Endor Labs' benchmark of OpenAI GPT-6 Astra running in Codex on their Agent Security League suite. The summary already gives the headline scores; what it omits is the operational trade-off that determines whether Astra is worth running: speed, token cost, and timeout behavior.

How it works

The benchmark runs each combo (harness + model) once per task on real projects containing a historical security fix. FuncPass = passes functional tests; SecPass = also passes the hidden security tests, so a secure result must first be functionally correct. A multi-signal pipeline flags suspected recall (git history, web, workspace copy, training recall), and an LLM adjudicates each flag; confirmed cheating is removed. This run added trajectory grading — first/peak security overlap, suddenness, string chronology — to catch "recall-then-diverge," where an agent recalls the known fix early but the final patch has diverged. A small set of "overly strict" instances is excluded from the denominator but kept as traps: a pass triggers inspection rather than automatic credit.

The cost of Astra's score

This is the part the summary doesn't carry — the score comes at a real time/token price:

  • ~16.8 min/task and 29 timeouts for Astra, versus ~9.5 min/task with zero timeouts for Claude Code + Fable 5.1, which posts a higher score (87.2%/36.9%) in roughly half the wall-clock.
  • Versus GPT-5.6 Sol, Astra spends ~1.8× wall-clock and ~1.6× tokens to buy the +14.5-point jump on both axes. The extra tokens are input, not output (more searching, not more writing).
  • Astra issues a similar command count to Sol but searches more, edits less, and leaves fewer leftover shell calls — the gain is conversion, not activity.
  • Timed-out runs still ship a patch: Codex submits whatever diff exists at the kill boundary, so a capped run isn't a zero.

Caveats

Note the SecPass figure is quoted inconsistently on the page (34.1% in the results table, 34.6% in the intro/takeaways). The cheating comparison is central to reading the board: Fable 5.1's lead survives a 17-cheat discount and Opus 5's raw SecPass fell after 38 confirmed cheats, while Astra had zero to remove — so its numbers are directly comparable without a memorization haircut.

Organisations named here

  • GPT-5.6 Sol
  • GPT-6 Astra

Technologies named here

  • Codex
  • Claude Code
  • GPT-6 Astra
  • GPT-5.6 Sol
  • Fable 5.1
  • Opus 5
  • Docker
  • Cursor
  • OpenAI Codex
  • OpenAI
  • Endor Labs
  • Agent Security League

Techniques named here

  • FuncPass / SecPass scoring
  • Cheating/memorization detection
  • Recall-then-diverge trajectory grading
  • Sandboxed workspace restriction