Vals.ai Benchmark – International Olympiad in Informatics
The agent sees only what a contestant sees: the statement, the sample grader and one sample. It has no tests, no internet and no submission feedback, so every point comes from code the model wrote and checked itself. Scores decline on the newest problems: cohort mean accuracy is 58.38% on 2024 and 53.67% on 2025 but 50.82% on 2026, and fifteen of the twenty-six models score lowest on the 2026 problems.
- ▪The agent sees only what a contestant sees: the statement, the sample grader and one sample.
- ▪It has no tests, no internet and no submission feedback, so every point comes from code the model wrote and checked itself.
- ▪Scores decline on the newest problems: cohort mean accuracy is 58.38% on 2024 and 53.67% on 2025 but 50.82% on 2026, and fifteen of the twenty-six models score lowest on the 2026 problems.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Vals |
| Canonical URL | https://www.vals.ai/benchmarks/ioi |
| Publication time | Sat, 12 Sep 2026 15:05:04 +0000 |
| Retrieval time | 2026-09-12T16:45:11.138Z |
| Last seen | 2026-09-12T16:45:26.610Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | WLv8tUTmWJd8 · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Key Takeaways Unlike the saturated knowledge benchmarks, IOI still sharply separates models: GPT-6 Astra solves every problem in all three years, GPT-5.6 Sol (91.17%), Claude Fable 5.1 (90.78%) and GPT-5.6 Terra (87.61%) follow, and the twenty-six-model field then spreads across ninety points, down to 9.33%, so competitive-programming ability remains a real differentiator. The agent sees only what a contestant sees: the statement, the sample grader and one sample. It has no tests, no internet and no submission feedback, so every point comes from code the model wrote and checked itself. Scores decline on the newest problems: cohort mean accuracy is 58.38% on 2024 and 53.67% on 2025 but 50.82% on 2026, and fifteen of the twenty-six models score lowest on the 2026 problems.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Vals.
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