Claude won all three reported best-of-three Liar’s Dice series against Codex, taking each 2–0. The result comes from six games in one small experiment—not a general ranking of Codex and Claude. Its most interesting finding is the author’s interpretation that Claude treated Codex’s bids as clues about its hidden dice, while Codex often assessed bids as if its opponent were random.
What the match tested
Haoxiang Li’s August 17, 2026 report compared Codex CLI running gpt-5.6-sol with Claude Code running Claude Opus 5 at Liar’s Dice. The game requires each player to reason from a hidden hand while reacting to an opponent who may adapt to earlier play, making it a test of probability judgments, opponent modeling, bluffing, and memory—not simply a calculation benchmark.
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The rules behind the decisions
Each player starts with five dice and can see only their own. Players bid that at least a specified number of a face appear across all hands, then raise the bid or challenge it. Ones are wild by default. When a challenge reveals the dice, the challenger loses a die if the bid was true; otherwise, the bidder loses one.
A bid therefore does two things: it makes a claim about hidden dice and signals what the bidder may believe about their own hand. An opponent’s choice of bid can itself be evidence, though it is not proof, of what they hold.
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What happened in the reported games
Li reports that Claude won each of three best-of-three series 2–0. Across the series, Codex challenged 26 times and was correct 4 times; Claude challenged 11 times and was correct 8 times. Put another way, bids Claude made were true 22 of the 26 times Codex challenged them, while bids Codex made were false 8 of the 11 times Claude challenged them.
In a seat-swapped mirror run using earlier seeds, Li says all four paired games followed Opus rather than the seat. The random stream was anchored by seat, so round-one hands matched; later hands could diverge as players lost different numbers of dice.
Those are the author’s reported outcomes, not independently reproduced results. The series were small, and games within a series were connected by the agents’ ability to carry memory forward.
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Why the author thinks bid interpretation mattered
Li’s proposed explanation is that Codex often calculated whether a bid was plausible under a uniform or random-opponent assumption. That can mislead: the opponent chose the bid, and may have had a private hand that made it more likely to be true. Claude, in the author’s reading of the decisions, learned that Codex tended to open around its longest suit and learned its challenge threshold. It then made bids that could look suspicious under a random-opponent calculation while still being supported by its hand.
This is an interpretation of the observed decisions, not an established general difference between the two products. It illustrates a broader issue in interactive evaluations: a probability estimate based on random choices may miss information conveyed by a strategic opponent’s choices.
How the experiment tried to protect fairness
The report describes a shared in-process rules engine, a localhost-only coordinator, and separate stdio MCP servers for the two agents. Tokens assigned servers to seats, while the tool schema had no seat parameter. Each agent received an observe() view that excluded the opponent’s dice; actions included a state ID so stale submissions could be rejected.
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Li also reports recording beliefs alongside actions at decision time, coarsening raw latency to fast or slow for seats and spectators, and using commit-reveal hashes for hands. Challenges had to explicitly assert that the current bid was false. These controls were intended to reduce hidden-information leakage, seat impersonation, timing side channels, post-hoc log editing, and ambiguous actions.
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For replay validation, the author reports that an event-by-event verifier matched five runs, eight games, and 432 events, including timing fields. Raw rejections and timestamps were retained, and run directories were frozen with SHA-256 manifests. Both agents received the same task prompts and instructions, ran in isolated temporary directories, and ignored user configuration. Each series used a single session, allowing cross-game memory. However, the internal prompting and scheduling of the two CLIs were not controlled.
What the retry incident says about attribution
In the mirror run, Codex submitted a bid count of 1 in two opening states, although the minimum legal count was 2. Li reports 1,093 repeated rejections: one burst of 601 within 22 seconds and another of 492 within 12 seconds, each with a median gap of 3 milliseconds. An independent-seed series reportedly had the same invalid count once, followed by a correction after rejection.
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Li attributes the invalid first action to the model and the rapid repetition to CLI retry machinery, rather than treating the retries as 1,093 fresh model decisions. That distinction matters when logs combine model output with harness behavior: an observed failure may belong to one layer, another, or both.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.“The question worth running before any ‘the model is X’ claim: swap only the harness — does the behavior survive?”
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What the result does—and does not—establish
The report is a narrow case study of Codex CLI with gpt-5.6-sol and Claude Code with Claude Opus 5 in six games across three series. It does not establish that one product is generally better at reasoning, games, or coding.
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- The sample is small, and games in each series are not independent because the sessions allowed cross-game memory.
- Compute was not equalized. Li reports about 300,000 Claude output tokens across the series versus 19,000 for Codex, and $17.63 in Claude-side receipts. The experiment cannot separate better conditioning from the effect of more thinking.
- The report mentions an earlier, close 5–4-round match using Codex’s default model, but its setup differed, so it is not a controlled comparison.
- The reported mirror result is informative, but seed anchoring by seat does not guarantee identical later hands after dice counts diverge.
What a stronger follow-up would need to test
Li proposes several next experiments rather than reporting them as completed evidence:
- Restart each session for every game to separate within-game reasoning from cross-game memory.
- Mute table talk to distinguish information conveyed by speech from information conveyed by bids and actions.
- Give both systems equal compute budgets to test whether the apparent strategic difference persists under a more comparable allowance.
- Replay pivotal states repeatedly to measure whether decisions are stable or sensitive to sampling luck.
Any follow-up comparison would also be easier to interpret if it reported the model and CLI versions, session continuity, compute budget, speech settings, seed and seat assignment, number and independence of games, and how rejections, retries, and replays were logged.
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