BenchLM recommendation
Best Factuality AI Models in 2026
As of July 23, 2026, the top model in best factuality ai models on the BenchLM leaderboard is Claude Mythos 5 with a score of 59.
Last verified: July 23, 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 (Max) (57.9) and DeepSeek V4 Pro Base (55.2). There is meaningful separation between the top models, suggesting genuine performance differences.
The best open-weight option is DeepSeek V4 Pro (Max) (ranked #2 with a score of 57.9). Open-weight models are highly competitive in this category — self-hosting is a viable alternative to proprietary APIs.
This ranking is based on provisional overall weighted scores across BenchLM.ai's scoring formula tracked by BenchLM.ai. 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 (34 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 DeepSeek V4 Pro (Max) at position #2.
34 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.
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