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Data

DiG-bench: Discovery in Games (DiG-bench)

We show this table for reference; we do not rank on it.

Data verified

Tests scientific discovery by asking agents to infer undisclosed rules across 70 interactive text-based games.

Expected composite score on DiG-bench — November 12, 2026

We mirror the published expected composite score view for DiG-bench. Claude Opus 5 leads the public snapshot at 68.5%, followed by Claude Fable 5 (57.3%) and Claude Opus 4.8 (42.4%). We do not use these results to rank models overall.

9 modelsAgenticCurrentDisplay onlyUpdated November 12, 2026

Expected composite score table (9 models)

Expected composite score results for DiG-bench
RankModel / configurationScoreParameters (B)Open / closed
1Claude Opus 5AnthropicBasic HarnessBasic Harness
68.5%
Not reportedClosed
2Claude Fable 5AnthropicClaude CodeClaude Code
57.3%
Not reportedClosed
3Claude Opus 4.8AnthropicBasic HarnessBasic Harness
42.4%
Not reportedClosed
4Claude Opus 4.8AnthropicClaude CodeClaude Code
31.4%
Not reportedClosed
5GPT-5.5OpenAIBasic HarnessBasic Harness
27.9%
Not reportedClosed
6Gemini 3.1 ProGoogleBasic HarnessBasic Harness
19.4%
Not reportedClosed
7GLM-5.2Z.AIBasic HarnessBasic Harness
18.6%
Not reportedOpen
8Qwen3.5 397BAlibabaBasic HarnessBasic Harness
5.9%
Not reportedOpen
9DeepSeek V4 Pro 0813DeepSeekBasic HarnessBasic Harness
5.7%
Not reportedOpen

How we show DiG-bench

We mirror DiG-bench's combined scientific-discovery leaderboard. Agents play 70 interactive text games whose rules are withheld, using the same states, actions, and step budgets as human testers.

The combined score weights game wins at 80% and partial progress at 20%. Model and harness stay attached because Claude Code and the basic harness produce separate rows. Confidence bounds come from the benchmark's published table.

The published DiG-bench snapshot places Claude Opus 5 first at 68.5%. The third row is 26.1 points behind. The broader top-10 range is 62.8 points, so the table still separates the published systems.

9 models have been evaluated on DiG-bench. The benchmark falls in the Agentic category. We keep external benchmark mirrors separate from the weighted global scoring system, so these results remain source-specific evidence. DiG-bench is currently displayed for reference but excluded from the scoring formula, so it does not directly affect overall rankings.

About DiG-bench

Year

2026

Tasks

70 interactive games across seven difficulty tiers

Format

Expected composite score with confidence interval

Difficulty

Unknown-rule discovery and interactive reasoning

Humans and models receive the same game states, legal actions, and step budgets. The combined leaderboard weights successful game completion at 80% and partial progress at 20%, with model-harness variants kept separate. We mirror the score and published confidence bounds as display-only evidence.

Freshness and provenance

Version

DiG-bench 2026

Refresh cadence

Quarterly

Staleness state

Current

Question availability

Public benchmark set

CurrentDisplay only

BenchLM uses freshness metadata to decide whether a benchmark should still be treated as a strong differentiator, a benchmark to watch, or a display-only reference. For the full scoring policy, see the BenchLM methodology page.

Questions

What does DiG-bench measure?

Tests scientific discovery by asking agents to infer undisclosed rules across 70 interactive text-based games.

Which model leads the published DiG-bench snapshot?

Claude Opus 5 currently leads the published DiG-bench snapshot with 68.5% expected composite score. BenchLM shows this benchmark for display only and does not use it in overall rankings.

How many models are evaluated on DiG-bench?

The November 12, 2026 snapshot contains 9 AI models.

Last updated: November 12, 2026 · mirrored from the public benchmark leaderboard

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