Long Context benchmark # Fiction.LiveBench leaderboard > Fiction.LiveBench results for 47 AI models, led by GPT-5 at 97.2%. What the benchmark measures, who runs it, and a source for every score. - Canonical page: https://noometry.com/benchmarks/fiction-livebench - Last updated: 2026-10-10 - Title: Fiction.LiveBench Leaderboard (October 2026): Scores by Model As of October 2026, GPT-5 has the highest published Fiction.LiveBench score on Noometry at 97.2%, out of 47 models with results. Last verified October 10, 2026 ## About Fiction.LiveBench Comprehension questions about long fiction stories that require tracking plot and characters across the text. Scores here are at 16k tokens. - **Category:** [Long Context](https://noometry.com/best/long-context) - **Introduced:** 2025 - **Format:** Long-context QA - **Unit:** Percent (random guessing ≈ 0%) - **Official site:** [fiction.live](https://fiction.live) ## Top 15 models Top models on Fiction.LiveBench 1. GPT-5 97.2% 2. o3-pro 97.2% 3. Grok 4 94.4% 4. Grok 4 Fast 94.4% 5. Gemini 2.5 Pro 91.7% 6. o3 88.9% 7. Kimi K2.5 86.1% 8. Claude 3.7 Sonnet 83.3% 9. DeepSeek-V3.2-Exp 83.3% 10. o1 83.3% 11. QwQ-32B 83.3% 12. Gemini 2.5 Flash 77.8% 13. o4-mini 77.8% 14. DeepSeek-R1 75% 15. Qwen3 235B-A22B 75% 16. 60708090100 Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise) ## All results Fiction.LiveBench results by model | # | Model | Provider | Score | Setting | Source | Date | | --- | --- | --- | --- | --- | --- | --- | | 1 | [GPT-5](https://noometry.com/models/gpt-5) | [OpenAI](https://noometry.com/providers/openai) | 97.2% | medium | [Epoch AI](https://epoch.ai/benchmarks) | | | 2 | [o3-pro](https://noometry.com/models/o3-pro) | [OpenAI](https://noometry.com/providers/openai) | 97.2% | medium | [Epoch AI](https://epoch.ai/benchmarks) | | | 3 | [Grok 4](https://noometry.com/models/grok-4) | [xAI](https://noometry.com/providers/xai) | 94.4% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 4 | [Grok 4 Fast](https://noometry.com/models/grok-4-fast) | [xAI](https://noometry.com/providers/xai) | 94.4% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 5 | [Gemini 2.5 Pro](https://noometry.com/models/gemini-2-5-pro) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 91.7% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 6 | [o3](https://noometry.com/models/o3) | [OpenAI](https://noometry.com/providers/openai) | 88.9% | medium | [Epoch AI](https://epoch.ai/benchmarks) | | | 7 | [Kimi K2.5](https://noometry.com/models/kimi-k2-5) | [Moonshot AI](https://noometry.com/providers/moonshot) | 86.1% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 8 | [Claude 3.7 Sonnet](https://noometry.com/models/claude-3-7-sonnet) | [Anthropic](https://noometry.com/providers/anthropic) | 83.3% | 8K | [Epoch AI](https://epoch.ai/benchmarks) | | | 9 | [DeepSeek-V3.2-Exp](https://noometry.com/models/deepseek-v3-2-exp) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 83.3% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 10 | [o1](https://noometry.com/models/o1) | [OpenAI](https://noometry.com/providers/openai) | 83.3% | medium | [Epoch AI](https://epoch.ai/benchmarks) | | | 11 | [QwQ-32B](https://noometry.com/models/qwq-32b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 83.3% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 12 | [Gemini 2.5 Flash](https://noometry.com/models/gemini-2-5-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 77.8% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 13 | [o4-mini](https://noometry.com/models/o4-mini) | [OpenAI](https://noometry.com/providers/openai) | 77.8% | medium | [Epoch AI](https://epoch.ai/benchmarks) | | | 14 | [DeepSeek-R1](https://noometry.com/models/deepseek-r1) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 75% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 15 | [Qwen3 235B-A22B](https://noometry.com/models/qwen3-235b-a22b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 75% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 16 | [Qwen3 32B](https://noometry.com/models/qwen3-32b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 74.2% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 17 | [GPT-5 Mini](https://noometry.com/models/gpt-5-mini) | [OpenAI](https://noometry.com/providers/openai) | 69.4% | medium | [Epoch AI](https://epoch.ai/benchmarks) | | | 18 | [MiniMax M1](https://noometry.com/models/minimax-m1) | [![](/logos/minimax.svg) MiniMax](https://noometry.com/providers/minimax) | 69.4% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 19 | [GPT-4o](https://noometry.com/models/gpt-4o) | [OpenAI](https://noometry.com/providers/openai) | 66.7% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 20 | [Grok-3 mini](https://noometry.com/models/grok-3-mini) | [xAI](https://noometry.com/providers/xai) | 66.7% | medium | [Epoch AI](https://epoch.ai/benchmarks) | | | 21 | [Kimi K2 (Jul 2025)](https://noometry.com/models/kimi-k2) | [Moonshot AI](https://noometry.com/providers/moonshot) | 66.7% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 22 | [Qwen Max](https://noometry.com/models/qwen-max) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 66.7% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 23 | [Qwen3 Max](https://noometry.com/models/qwen3-max) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 66.7% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 24 | [GPT-4.1](https://noometry.com/models/gpt-4-1) | [OpenAI](https://noometry.com/providers/openai) | 63.9% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 25 | [GPT-4.5](https://noometry.com/models/gpt-4-5) | [OpenAI](https://noometry.com/providers/openai) | 63.9% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 26 | [Qwen3 14B](https://noometry.com/models/qwen3-14b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 62.5% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 27 | [Qwen3 8B](https://noometry.com/models/qwen3-8b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 62.1% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 28 | [Claude Opus 4](https://noometry.com/models/claude-opus-4) | [Anthropic](https://noometry.com/providers/anthropic) | 61.1% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 29 | [Gemini 2.0 Flash (Feb 2025)](https://noometry.com/models/gemini-2-0-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 61.1% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 30 | [GLM-4.5](https://noometry.com/models/glm-4-5) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 58.3% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 31 | [Grok 3](https://noometry.com/models/grok-3) | [xAI](https://noometry.com/providers/xai) | 58.3% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 32 | [Qwen3-Next 80B-A3B Instruct](https://noometry.com/models/qwen3-next-80b-a3b-instruct) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 55.6% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 33 | [DeepSeek-V3.1](https://noometry.com/models/deepseek-v3-1) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 52.8% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 34 | [DeepSeek-V3](https://noometry.com/models/deepseek-v3) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 50% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 35 | [o3-mini](https://noometry.com/models/o3-mini) | [OpenAI](https://noometry.com/providers/openai) | 50% | medium | [Epoch AI](https://epoch.ai/benchmarks) | | | 36 | [Gemini 2.5 Flash-Lite](https://noometry.com/models/gemini-2-5-flash-lite) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 47.2% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 37 | [Claude Sonnet 4](https://noometry.com/models/claude-sonnet-4) | [Anthropic](https://noometry.com/providers/anthropic) | 46.9% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 38 | [Llama 4 Maverick](https://noometry.com/models/llama-4-maverick) | [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 46.2% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 39 | [GPT-4.1 mini](https://noometry.com/models/gpt-4-1-mini) | [OpenAI](https://noometry.com/providers/openai) | 44.4% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 40 | [GPT-5 Nano](https://noometry.com/models/gpt-5-nano) | [OpenAI](https://noometry.com/providers/openai) | 44.4% | medium | [Epoch AI](https://epoch.ai/benchmarks) | | | 41 | [gpt-oss-120b](https://noometry.com/models/gpt-oss-120b) | [OpenAI](https://noometry.com/providers/openai) | 44.4% | high | [Epoch AI](https://epoch.ai/benchmarks) | | | 42 | [Gemini 2.0 Pro](https://noometry.com/models/gemini-2-0-pro) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 41.7% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 43 | [Qwen3-30B-A3B](https://noometry.com/models/qwen3-30b-a3b) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 40.6% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 44 | [Llama 4 Scout](https://noometry.com/models/llama-4-scout) | [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 36% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 45 | [Gemma 3 27B](https://noometry.com/models/gemma-3-27b) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 33.3% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 46 | [Llama-3.3-70B-Instruct](https://noometry.com/models/llama-3-3-70b-instruct) | [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 33.3% | | [Epoch AI](https://epoch.ai/benchmarks) | | | 47 | [GPT-4.1 nano](https://noometry.com/models/gpt-4-1-nano) | [OpenAI](https://noometry.com/providers/openai) | 25% | | [Epoch AI](https://epoch.ai/benchmarks) | | ## Compare the leaders - [GPT-5 vs o3-pro](https://noometry.com/compare/gpt-5-vs-o3-pro) - [GPT-5 vs Grok 4](https://noometry.com/compare/gpt-5-vs-grok-4) - [GPT-5 vs Grok 4 Fast](https://noometry.com/compare/gpt-5-vs-grok-4-fast) - [GPT-5 vs Gemini 2.5 Pro](https://noometry.com/compare/gemini-2-5-pro-vs-gpt-5) - [o3-pro vs Grok 4](https://noometry.com/compare/grok-4-vs-o3-pro) - [o3-pro vs Grok 4 Fast](https://noometry.com/compare/grok-4-fast-vs-o3-pro) ## Other long context benchmarks - [CL-bench](https://noometry.com/benchmarks/cl-bench) - [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) - [CL-bench Life](https://noometry.com/benchmarks/cl-bench-life) ## Frequently asked questions ### What does Fiction.LiveBench measure? Comprehension questions about long fiction stories that require tracking plot and characters across the text. Scores here are at 16k tokens. ### Which model has the highest Fiction.LiveBench score? As of October 2026, GPT-5 has the highest published Fiction.LiveBench score on Noometry at 97.2%, out of 47 models with results. ### What is the best open-weight model on Fiction.LiveBench? Kimi K2.5 has the highest Fiction.LiveBench accuracy among open-weight models at 86.1%, ranking 7 of 47 overall. ### Cite this page Noometry. (2026). Fiction.LiveBench leaderboard. Retrieved October 10, 2026, from https://noometry.com/benchmarks/fiction-livebench Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/benchmarks/fiction-livebench.md).