Multimodal benchmark # LMArena Document leaderboard > LMArena Document results for 38 AI models, led by Claude Opus 5 at 1516. What the benchmark measures, who runs it, and a source for every score. - Canonical page: https://noometry.com/benchmarks/arena-document - Last updated: 2026-10-10 - Title: LMArena Document Leaderboard (October 2026): Scores by Model As of October 2026, Claude Opus 5 has the highest published LMArena Document score on Noometry at 1516, out of 38 models with results. Last verified October 10, 2026 ## About LMArena Document Blind votes on answers to questions about uploaded documents. - **Category:** [Multimodal](https://noometry.com/best/multimodal) - **Introduced:** 2026 - **Format:** Pairwise human votes - **Unit:** Arena rating (Bradley–Terry) - **Official site:** [lmarena.ai](https://lmarena.ai/leaderboard) ## Top 15 models Top models on LMArena Document 1. Claude Opus 5 1516 2. Claude Fable 5.1 1513 3. Claude Opus 4.6 1507 4. Claude Fable 5 1496 5. Claude Opus 4.7 1495 6. GPT-5.5 1486 7. GPT-5.6 Sol 1483 8. Claude Sonnet 4.6 1482 9. Claude Opus 4.8 1475 10. GPT-5.6 Terra 1472 11. GPT-5.4 1471 12. Muse Spark 1.3 1471 13. GPT-6 Astra 1468 14. Claude Sonnet 5 1466 15. Muse Spark 1.1 1465 16. 14401460148015001520 Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise) ## All results LMArena Document results by model | # | Model | Provider | Rating | Setting | Source | Date | | --- | --- | --- | --- | --- | --- | --- | | 1 | [Claude Opus 5](https://noometry.com/models/claude-opus-5) | [Anthropic](https://noometry.com/providers/anthropic) | 1516 | high | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 2 | [Claude Fable 5.1](https://noometry.com/models/claude-fable-5-1) | [Anthropic](https://noometry.com/providers/anthropic) | 1513 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 3 | [Claude Opus 4.6](https://noometry.com/models/claude-opus-4-6) | [Anthropic](https://noometry.com/providers/anthropic) | 1507 | high | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 4 | [Claude Fable 5](https://noometry.com/models/claude-fable-5) | [Anthropic](https://noometry.com/providers/anthropic) | 1496 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 5 | [Claude Opus 4.7](https://noometry.com/models/claude-opus-4-7) | [Anthropic](https://noometry.com/providers/anthropic) | 1495 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 6 | [GPT-5.5](https://noometry.com/models/gpt-5-5) | [OpenAI](https://noometry.com/providers/openai) | 1486 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 7 | [GPT-5.6 Sol](https://noometry.com/models/gpt-5-6-sol) | [OpenAI](https://noometry.com/providers/openai) | 1483 | xhigh | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 8 | [Claude Sonnet 4.6](https://noometry.com/models/claude-sonnet-4-6) | [Anthropic](https://noometry.com/providers/anthropic) | 1482 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 9 | [Claude Opus 4.8](https://noometry.com/models/claude-opus-4-8) | [Anthropic](https://noometry.com/providers/anthropic) | 1475 | high | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 10 | [GPT-5.6 Terra](https://noometry.com/models/gpt-5-6-terra) | [OpenAI](https://noometry.com/providers/openai) | 1472 | xhigh | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 11 | [GPT-5.4](https://noometry.com/models/gpt-5-4) | [OpenAI](https://noometry.com/providers/openai) | 1471 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 12 | [Muse Spark 1.3](https://noometry.com/models/muse-spark-1-3) | [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 1471 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 13 | [GPT-6 Astra](https://noometry.com/models/gpt-6-astra) | [OpenAI](https://noometry.com/providers/openai) | 1468 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 14 | [Claude Sonnet 5](https://noometry.com/models/claude-sonnet-5) | [Anthropic](https://noometry.com/providers/anthropic) | 1466 | high | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 15 | [Muse Spark 1.1](https://noometry.com/models/muse-spark-1-1) | [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 1465 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 16 | [Gemini 3.5 Flash](https://noometry.com/models/gemini-3-5-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 1463 | medium | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 17 | [Claude Opus 4.5](https://noometry.com/models/claude-opus-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 1462 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 18 | [GPT-5.6 Luna](https://noometry.com/models/gpt-5-6-luna) | [OpenAI](https://noometry.com/providers/openai) | 1457 | xhigh | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 19 | [Gemini 3.6 Flash](https://noometry.com/models/gemini-3-6-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 1456 | high | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 20 | [Grok 4.6](https://noometry.com/models/grok-4-6) | [xAI](https://noometry.com/providers/xai) | 1452 | high | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 21 | [Grok 4.5](https://noometry.com/models/grok-4-5) | [xAI](https://noometry.com/providers/xai) | 1452 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 22 | [Kimi K2.6](https://noometry.com/models/kimi-k2-6) | [Moonshot AI](https://noometry.com/providers/moonshot) | 1451 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 23 | [Claude Sonnet 4.5](https://noometry.com/models/claude-sonnet-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 1450 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 24 | [Muse Spark](https://noometry.com/models/muse-spark) | [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 1444 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 25 | [Qwen3.7 Plus](https://noometry.com/models/qwen3-7-plus) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 1444 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 26 | [Gemini 3.1 Pro Preview](https://noometry.com/models/gemini-3-1-pro-preview) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 1444 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 27 | [MiniMax-M3](https://noometry.com/models/minimax-m3) | [![](/logos/minimax.svg) MiniMax](https://noometry.com/providers/minimax) | 1435 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 28 | [Gemini 3 Pro](https://noometry.com/models/gemini-3-pro) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 1434 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 29 | [Kimi K2.5](https://noometry.com/models/kimi-k2-5) | [Moonshot AI](https://noometry.com/providers/moonshot) | 1430 | thinking | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 30 | [Gemma 4 31B IT](https://noometry.com/models/gemma-4-31b-it) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 1425 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 31 | [Gemini 2.5 Pro](https://noometry.com/models/gemini-2-5-pro) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 1421 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 32 | [Claude Haiku 4.5](https://noometry.com/models/claude-haiku-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 1420 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 33 | [Grok 4.20 (Non-Reasoning)](https://noometry.com/models/grok-4-20) | [xAI](https://noometry.com/providers/xai) | 1416 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 34 | [GLM-5V-Turbo](https://noometry.com/models/glm-5v-turbo) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 1416 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 35 | [Gemini 3 Flash Preview](https://noometry.com/models/gemini-3-flash-preview) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 1413 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 36 | [GPT-5.2](https://noometry.com/models/gpt-5-2) | [OpenAI](https://noometry.com/providers/openai) | 1405 | high | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 37 | [GPT-5.1](https://noometry.com/models/gpt-5-1) | [OpenAI](https://noometry.com/providers/openai) | 1403 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | | 38 | [GPT-5.5 Instant](https://noometry.com/models/gpt-5-5-instant) | [OpenAI](https://noometry.com/providers/openai) | 1403 | | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 | ## Compare the leaders - [Claude Opus 5 vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-claude-opus-5) - [Claude Opus 5 vs Claude Opus 4.6](https://noometry.com/compare/claude-opus-4-6-vs-claude-opus-5) - [Claude Opus 5 vs Claude Fable 5](https://noometry.com/compare/claude-fable-5-vs-claude-opus-5) - [Claude Opus 5 vs Claude Opus 4.7](https://noometry.com/compare/claude-opus-4-7-vs-claude-opus-5) - [Claude Fable 5.1 vs Claude Opus 4.6](https://noometry.com/compare/claude-fable-5-1-vs-claude-opus-4-6) - [Claude Fable 5.1 vs Claude Fable 5](https://noometry.com/compare/claude-fable-5-vs-claude-fable-5-1) ## Other multimodal benchmarks - [LMArena Vision](https://noometry.com/benchmarks/arena-vision) - [Video-MME](https://noometry.com/benchmarks/video-mme) - [GeoBench](https://noometry.com/benchmarks/geobench) - [VPCT](https://noometry.com/benchmarks/vpct) - [Blueprint-Bench 2](https://noometry.com/benchmarks/blueprint-bench-2) - [Furniture Assembly](https://noometry.com/benchmarks/furniture-assembly) - [MindCube](https://noometry.com/benchmarks/mindcube) (reference) - [ScienceQA](https://noometry.com/benchmarks/scienceqa) (reference) - [SpatialViz-Bench](https://noometry.com/benchmarks/spatialviz-bench) (reference) ## Frequently asked questions ### What does LMArena Document measure? Blind votes on answers to questions about uploaded documents. ### Which model has the highest LMArena Document score? As of October 2026, Claude Opus 5 has the highest published LMArena Document score on Noometry at 1516, out of 38 models with results. ### What is the best open-weight model on LMArena Document? Kimi K2.6 has the highest LMArena Document rating among open-weight models at 1451, ranking 22 of 38 overall. ### Cite this page Noometry. (2026). LMArena Document leaderboard. Retrieved October 10, 2026, from https://noometry.com/benchmarks/arena-document Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/benchmarks/arena-document.md).