Artificial Analysis Intelligence Index v5 launches with 60% private evaluations
Artificial Analysis released Intelligence Index v5, stating that 60% of the Index is now based on private test sets or held-out answers. The update adds FrontierCode 2.0 and Terminal-Bench-Science, upgrades HLE-Diamond, AA-Omniscience v1.1 and CritPt v2, and removes GDPval-AA v2.1, SciCode and AA-LCR v1.1. The article reports Claude Opus 5.5 (max) leading at 58, with the best open weights model at 46 (Kimi K3.1 Pro, weights not yet released), a 12-point gap. It also reports higher token use and wider effort-setting spreads than in v4.3.
Open original source artificialanalysis.aiOne primary source; other links are context, not independent corroboration.
- Primary sourceX discovery · automated brief
- Discovery linkX
- Linked in sourceartificialanalysis.ai/methodology/intelligence-benchmarking
Grok Bot submitted the link. AINewsIQ fetched the primary source and prepared a source-based brief automatically. An editor has not reviewed the story; social attention and the brief do not independently verify its claims.
Open full article view →- Linked article+30
- Fetched source page+30
- No editor link review+0
- Original non-discussion link+5
This measures the stored source trail and editorial steps. It does not certify accuracy, peer review, or independent corroboration. Popularity adds no points.
View full scoring →Read the scoring rubric →What the source says
Artificial Analysis released Intelligence Index v5, stating that 60% of the Index is now based on private test sets or held-out answers. The update adds FrontierCode 2.0 and Terminal-Bench-Science, upgrades HLE-Diamond, AA-Omniscience v1.1 and CritPt v2, and removes GDPval-AA v2.1, SciCode and AA-LCR v1.1. The article reports Claude Opus 5.5 (max) leading at 58, with the best open weights model at 46 (Kimi K3.1 Pro, weights not yet released), a 12-point gap. It also reports higher token use and wider effort-setting spreads than in v4.3.
A READER'S EVIDENCE CHECKHow to read this signalSix questions about claims, sources, risks, and open checks
This is a signal-level guide until claim-level evidence is available. “Not recorded” means the evidence has not been assessed here; it does not mean a result is false. How we check AI news →
- 01Who is making each claim?
The claimant and whether each claim was measured or asserted are not recorded in this public brief.
- 02What was actually tested?
Not recorded per claim. Check the source for sample size, tasks, model and benchmark versions, and reproducibility.
- 03Where are the sources?
Original link: artificialanalysis.ai. Brief basis: fetched source page. Primary page date and quote locations are not established in this panel.
- 04What could mislead?
Source claims not independently checked. Scores and rankings are the publisher's own measurements, and several top open weights results are for a model whose weights are not yet released.
- 05What is at risk if wrong?
Not assessed per claim. Consider safety, privacy, cost, and adoption decisions before relying on the result.
- 06What would change the view?
No claim-specific check is recorded. An inspectable primary method or independent replication could change the assessment.
Key findings from the stored brief
- With v5, 60% of the Index is based on evaluations with private test sets or held-out answers.
- Claude Opus 5.5 (max) leads the Index at 58; the top open weights model, Kimi K3.1 Pro, scores 46, a 12-point gap versus 3 in v4.3.
- Among released-weights models, DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) lead at 32.
- Median output tokens per Intelligence Index task rose from 35k in v4.3 to 59k in v5.
Source claims not independently checked. Scores and rankings are the publisher's own measurements, and several top open weights results are for a model whose weights are not yet released.
This is a source-based abstract, not independent validation of the source's claims.
Evidence & sources
These links show where the signal came from and what the source page linked to. A linked page is context, not independent corroboration.
Related archive coverage
These relationships record a citation or topic overlap, not confirmation of the finding.
Grok Bot submitted the link. AINewsIQ fetched the primary source and prepared a source-based brief automatically. An editor has not reviewed the story; social attention and the brief do not independently verify its claims.
These are Grok Bot discovery notes. AINewsIQ prepared a brief from available source material. The bot's rationale, linked posts, authors, and stances have not been independently verified.
Artificial Analysis' Oct 27 article launches Intelligence Index v5: 60% of the index now uses private test sets or held-out answers, with new FrontierCode 2.0 and Terminal-Bench-Science. Claude Opus 5.5 (max) leads at 58. The best open-weights entry, Kimi K3.1 Pro (weights not yet released), trails by 12 points vs 3 in v4.3. Third-party results; methods are AA's own.
Source claims not independently checked. Scores and rankings are the publisher's own measurements, and several top open weights results are for a model whose weights are not yet released.
- Bot tagged: context Wharton professor Ethan Mollick says to take the open-weights gap finding with a grain of salt, though it matches his general impression. Inspect post ↗
Engagement counts, when shown, are observed snapshots. They do not establish accuracy or corroboration.
- hn post ↗ · 2 points, 0 comments · observed 10/28/2026, 4:06:32 AM
- x post ↗ · 1,695 likes, 155 reposts, 188 replies, 196 quotes · observed 10/28/2026, 4:05:56 AM
- reddit post ↗
Timeline & updates
- Signal dated by source or discovery feed
This timestamp places the signal in the radar. It may be the discovery post date rather than the primary page's first publication date.
- Shared brief stored
AINewsIQ prepared one abstract from fetched source page.
A publication revision history is not stored for this signal. New engagement snapshots do not mean the source itself changed.
Why this may matter
The shift toward private and held-out evaluations aims to reduce contamination in benchmark scores. The widening open weights gap and large cost and effort-setting differences bear on how developers choose models.
Limits and open questions
Source claims not independently checked. Scores and rankings are the publisher's own measurements, and several top open weights results are for a model whose weights are not yet released.
Organization-specific financial, security, and operational effects require the organization's own context and review.
Discussion
Comments are reviewed before publication. Discuss the evidence, not the person.
No published comments yet.
Reader discussion opens with accounts. You can read the original source and published comments now.