All benchmarks
Long contextBeside the Index

Fiction.LiveBench

Deep comprehension of long stories: questions that need the whole text, at a 120K-token context length.

Keeping track of a story across a very long document. Long context is a single-test category in this snapshot (fewer than 2 comparable tests), so it is shown beside the Index and never averaged into it.

Publisher: Fiction.live

What a model is asked to do

Answer questions that need the whole of a very long story, at a 120K-token context length.

For exampleHalfway through the novel, which promise from chapter two does the narrator break, and who is the first to notice?

Why it matters. Long documents are the normal case at work. This shows whether a model still holds the thread at the end.

The example is original and illustrative, not an item from the dataset.

Models scored here
7
Who produced the numbers
Official leaderboard
Best published result
100.0%
License
CC BY 4.0 (via Epoch AI)
Direction
Higher is better
Scale
Published as a share of items solved. For the Index, each score is a share of the best published result (100.0%), so the frontier reads 100. Guessing earns nothing on this test, so no chance correction applies.
Provenance
Produced by the benchmark's own leaderboard or a third-party evaluator, not by the model's maker.

Snapshot September 8, 2026

Ranking on this test

7 models on Fiction.LiveBench

Best published run per model, and how close each one comes to the best published result on this test (the frontier reads 100). Every number links to the publisher that produced it. Internal runs are marked and carry their run details on the model page.

#ModelScoreProduced by
1GPT-5OpenAI96.9%Leaderboard
2Grok 4xAI96.9%Leaderboard
3Kimi K2.5Moonshot AI78.1%Leaderboard
4Grok 4 FastxAI75.0%Leaderboard
5GPT-5 miniOpenAI62.5%Leaderboard
6Kimi K2Moonshot AI40.6%Leaderboard
7GPT-5 nanoOpenAI21.9%Leaderboard
Theo

The best models, ranked here, working inside Theo.

28 of the models on this page run inside Theo today. Theo picks the right one for each step and always shows which engine answered.