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Claude Code won all three reported Liar’s Dice series against Codex, but the result is evidence about six games between two specific configurations—not a general ranking of the tools. Haoxiang Li’s August 17, 2026 report argues that Claude treated Codex’s bids as clues about its hidden dice, while Codex often judged bids as though its opponent were choosing randomly. That is a plausible reading of the recorded play, not a proven explanation of overall model capability.
What happened in the match?
Li reports three best-of-three series. Claude Code running Claude Opus 5 won each series 2–0 against Codex CLI running gpt-5.6-sol. Across the reported games, Codex challenged 26 bids and was right 4 times; Claude challenged 11 bids and was right 8 times. In other words, Codex often challenged bids that proved true, while Claude’s challenges more often caught false bids.
In a seat-swapped mirror run using the earlier seeds, Li says all four paired games followed Opus rather than the seat. The random stream was anchored per seat, so the round-one hands matched; later hands could differ after the players’ dice counts diverged. This is a useful control, but it does not make the sample large or the later hands identical.
These figures are the author’s report, not an independent replication or a population-level statistical result. The report describes six games across three series, and games within a series were not independent because a single session allowed memory across games.
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Why is a bid more than a probability?
In the reported rules, each player starts with five dice and sees only their own. Players bid that at least a stated number of a face exists across all hands, then raise the bid or challenge it. Ones are wild by default. If a challenge reveals that the bid was true, the challenger loses a die; if it was false, the bidder loses one.
A bid is therefore both a claim about hidden dice and a choice made by an opponent with private information. If an opponent tends to open with the face most common in their hand, that opening bid is evidence about their hand. A challenger who treats the bid as randomly selected may overlook that signal.
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The author’s explanation of the different decisions
Li interprets the logs as showing Codex calculating whether a bid was likely under a uniform or random-opponent assumption, while Claude used the opponent’s choice of bid as information. In the author’s account, Claude learned that Codex tended to open around its longest suit and inferred Codex’s challenge threshold. It then made bids that looked suspicious under the random-opponent calculation but were supported by the actual dice.
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How did the comparison control hidden information?
Li reports that both agents used the same in-process rules engine, accessed through a localhost-only coordinator and separate stdio MCP servers. Each server was token-bound to one seat, and the tool schema had no seat parameter. Agents received an observe() view without the opponent’s dice, and actions included a state ID so stale submissions could be rejected.
- Beliefs were recorded with actions at decision time.
- Raw latency remained in audit logs, while seats and spectators saw only coarsened fast/slow timing.
- Hands used commit-reveal hashes; a challenge had to explicitly assert that the current bid was false.
- Both agents received identical instructions and task prompts, ran in isolated temporary directories, and ignored user configuration.
- A replay verifier checked five runs, eight games, and 432 events against archived runs; Li reports that all events matched, including timing fields. Raw rejections and timestamps were retained, and directories were frozen with SHA-256 manifests.
These are safeguards the author says were used to limit seat impersonation, hidden-information leakage, timing side channels, post-hoc editing, and ambiguous actions. They improve the credibility of the reported procedure, but do not resolve the study’s small sample or unequal compute.
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What did the retry incident reveal?
During the mirror run, Codex submitted an opening bid count of 1 in two states, although the minimum legal count was 2. Li reports 1,093 repeated rejections: 601 in a burst within 22 seconds and 492 in another within 12 seconds, with a median gap of 3 ms. The author attributes the initial invalid action to the model and the rapid repeated submissions to CLI retry machinery—not to 1,093 fresh model decisions. In an independent-seed series, the same invalid count reportedly occurred once and was corrected after rejection.
This distinction matters when evaluating agent logs: an invalid action can be a model error, while a burst of repeats can be behavior of the surrounding tool or harness. As Li puts the methodological question: “The question worth running before any ‘the model is X’ claim: swap only the harness — does the behavior survive?”
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What limits the result?
- Small, dependent sample: the report covers six games, with cross-game memory intentionally allowed, so the games in a series are not independent trials.
- Unequal compute: Li reports about 300,000 Claude output tokens across the series versus 19,000 for Codex, and $17.63 in Claude-side receipts. The report says compute was not equalized, so it cannot separate better conditioning from simply having more opportunity to think.
- Uncontrolled internals: each CLI’s internal prompting and scheduling were not controlled, even though the visible task prompts and rules engine were shared.
- Limited generalization: an earlier match with Codex’s default model was close at 5–4 rounds, but it used a different setup and is not a controlled comparison.
Accordingly, the cleanest takeaway is narrow: in these reported runs, Claude won, and the author’s logs suggest that interpreting bid choice as a signal may have helped. They do not show that Claude Code is universally superior to Codex, or isolate the cause of the outcome.
What would make a stronger follow-up?
Li proposes several useful next tests: restart sessions between games to remove cross-game memory, mute table talk to distinguish speech from action signals, equalize compute budgets, and replay pivotal states repeatedly to measure decision stability versus sampling luck. A future comparison should also report model and CLI versions, session continuity, token or compute limits, speech settings, seed and seat assignment, game count and independence, and rejection/retry logs with deterministic replay. These are proposed experiments, not results already established by this match.
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