Scale Labs FORTRESS (FORTRESS)
We show this table for reference; we do not rank on it.
A Scale Labs public leaderboard mirrored as display-only reference data. It does not affect BenchLM rankings. The catalog still links this board, but its public route was unavailable during the latest refresh; these are the last successfully captured rows.
Scale score on FORTRESS — 2026-09-28 last available snapshot
We mirror the published scale score view for FORTRESS. DeepSeek-R1 leads the public snapshot at 74.4%, followed by glm-4p5-air (63.2%) and Gemini 2.5 Pro (06-05) (61.7%). We do not use these results to rank models overall.
DeepSeek-R1
DeepSeek
glm-4p5-air
Z.AI
Gemini 2.5 Pro (06-05)
68 modelsKnowledgeCurrentDisplay onlyUpdated 2026-09-28 last available snapshot
Scale score table (68 models)
ScoreHow to read this leaderboard
Compare the published configurations as complete evaluation systems. The source can combine a base model, agent scaffold, tools, budget, and inference setting in each result.
Operator receipt: 68 sourced rows are currently displayable on this page; the leading published row is DeepSeek-R1 at 74.4%.
Honest limit: This Scale table is display-only context, not benchmark provenance or a weighted model-only comparison. The catalog still links this board, but its public route was unavailable during the latest refresh; these are the last successfully captured rows.
How BenchLM shows FORTRESS
BenchLM mirrors 68 published rows from Scale Labs’ public FORTRESS leaderboard, captured on 2026-09-28 last available snapshot. The catalog still links this board, but its public route was unavailable during the latest refresh; these are the last successfully captured rows.
The table is display only. It is useful context for a published agent or model configuration, but it does not enter BenchLM’s overall or category rankings.
Snapshot
The published FORTRESS snapshot places DeepSeek-R1 first at 74.4%. The third row is 12.7 points behind. The broader top-10 range is 18.9 points, so the table still separates the published systems.
68 models have been evaluated on FORTRESS. The benchmark falls in the Knowledge category. We keep external benchmark mirrors separate from the weighted global scoring system, so these results remain source-specific evidence. FORTRESS is currently displayed for reference but excluded from the scoring formula, so it does not directly affect overall rankings.
About FORTRESS
Year
2026
Tasks
68 published rows
Format
Published Scale leaderboard score
Difficulty
External agent and model evaluation
BenchLM mirrors 68 published rows from the FORTRESS public table captured on 2026-09-28 last available snapshot.
Freshness and provenance
Version
FORTRESS 2026
Refresh cadence
Quarterly
Staleness state
Current
Question availability
Public benchmark set
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 FORTRESS measure?
A Scale Labs public leaderboard mirrored as display-only reference data. It does not affect BenchLM rankings. The catalog still links this board, but its public route was unavailable during the latest refresh; these are the last successfully captured rows.
Which model leads the published FORTRESS snapshot?
DeepSeek-R1 currently leads the published FORTRESS snapshot with 74.4% scale score. BenchLM shows this benchmark for display only and does not use it in overall rankings.
How many models are evaluated on FORTRESS?
The 2026-09-28 last available snapshot snapshot contains 68 AI models.
Know when it’s worth switching models
The model to choose, the cheaper alternative, and the release we would wait on.
Read a sample issueJoin 2,000+ readers.
One email each week. Unsubscribe anytime.