BenchLM recommendation
Best Large Context Window LLMs in 2026
As of July 20, 2026, the top model in best large context window llms on the BenchLM leaderboard is Claude Mythos 5 with a score of 83.9.
Last verified: July 20, 2026
AI models with the largest context windows (200K+ tokens), ranked by benchmark performance.
Unless noted otherwise, ranking surfaces on this page use BenchLM's provisional leaderboard lane rather than the stricter sourced-only verified leaderboard.
Bottom line: A large context window means nothing if the model can't actually use it. Claude Fable 5 and Gemini 3.1 Pro both have 1M+ context and the benchmarks to back it up.
Claude Mythos 5 leads this ranking with a score of 83.9, followed by Claude Fable 5 (83.7) and GPT-5.6 Sol (82). 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 MiniMax M3 (ranked #15 with a score of 69.8). Proprietary models hold a clear advantage in this category, though open-weight options may suffice for less demanding use cases.
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 large-context models with 1M+ context and the highest overall score.
Gemini 3.1 Pro 1M context with strong reasoning (97) — best non-reasoning large-context model.
GPT-5.4 1.05M context — largest window among the top 3 overall models.
How to choose
Full Rankings (125 models)
Key Takeaways
The top model is Claude Mythos 5 by Anthropic with a BenchAlign v5 score of 83.9 and Supported evidence.
The best open-weight model is MiniMax M3 at position #15.
125 models are included in this ranking.
Score in Context
What these scores mean
Models are filtered by context window (200K+ tokens) and ranked by overall BenchLM score. A large context window alone is not enough — check long-context benchmark scores for actual retrieval and reasoning quality.
Known limitations
Context window size is self-reported by providers. Actual usable context may be smaller due to edge degradation. Long-context benchmarks test specific patterns — real workloads may differ.
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