BenchLM recommendation
Best Factuality AI Models in 2026
As of August 21, 2026, the top model in best factuality ai models on the BenchLM leaderboard is Claude Mythos 5 with a score of 59.
Last verified: August 21, 2026
This reporting page is intentionally narrow. It focuses on currently tracked sourced factuality signals such as SimpleQA, HLE without tools, and multimodal factuality. It is a reporting page, not a mature weighted category.
This page ranks models using only sourced factuality benchmarks in the reporting family.
Bottom line: Factuality benchmarks are intentionally narrow — SimpleQA and HLE-no-tools are the primary signals. Claude Fable 5 leads, but this category is still maturing.
Claude Mythos 5 leads this ranking with a score of 59, followed by DeepSeek V4 Pro 0813 (57.9) and Claude Opus 5 (56.3). The top three are separated by just a few points — any of them would perform well for this use case.
The best open-weight option is Ornith-1.5-397B (ranked #10 with a score of 44.6). While proprietary models lead, open-weight options are within striking distance for teams willing to trade a few points of performance for full model control.
This ranking uses provisional overall weighted scores from the active scoring formula. For detailed model profiles, click any model name below. To compare two specific models head-to-head, use the "vs #" links.
What changed
Claude Fable 5 leads factuality with the best SimpleQA and HLE-no-tools scores.
Gemini 3.1 Pro strong factuality for a non-reasoning model.
GPT-5.4 solid SimpleQA performance, especially on knowledge-heavy queries.
How to choose
Full Rankings (36 models)
Key Takeaways
The top model on this sourced reporting-family slice is Claude Mythos 5 by Anthropic with an average of 59.
The best open-weight model is Ornith-1.5-397B at position #10.
36 models are listed with sourced benchmark coverage in this reporting family.
Score in Context
What these scores mean
This is a reporting family ranking, not a weighted category. It averages sourced factuality benchmarks to give a focused view of this capability.
Known limitations
Models must have sourced results on at least a quarter of the benchmarks in this family to be included. Coverage varies — a model with 2 benchmark scores is less reliable than one with 5.
Explore More
Know when it’s worth switching models
The model to choose, the cheaper alternative, and the release we would wait on.
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