Multimodal benchmark # Furniture Assembly leaderboard > Furniture Assembly results for 31 AI models, led by Claude Opus 5.5 at 83.3%. What the benchmark measures, who runs it, and a source for every score. - Canonical page: https://noometry.com/benchmarks/furniture-assembly - Last updated: 2026-10-10 - Title: Furniture Assembly Leaderboard (October 2026): Scores by Model As of October 2026, Claude Opus 5.5 has the highest published Furniture Assembly score on Noometry at 83.3%, out of 31 models with results. Last verified October 10, 2026 ## About Furniture Assembly A description with primary sources is being prepared for this benchmark. - **Category:** [Multimodal](https://noometry.com/best/multimodal) - **Introduced:** 2026 - **Unit:** Percent (random guessing ≈ 30%) - **Official site:** [epoch.ai](https://epoch.ai/benchmarks) ## Top 15 models Top models on Furniture Assembly 1. Claude Opus 5.5 83.3% 2. GPT-6.1 Sol 80% 3. GPT-6 Astra 80% 4. Claude Sonnet 5.5 75% 5. Claude Fable 5.1 70% 6. Claude Opus 5 60.8% 7. GPT-6 Sol 58.3% 8. GPT-5.6 Sol 56.7% 9. GPT-5.6 Terra 54.2% 10. Claude Haiku 5.5 47.5% 11. GPT-5.5 44.2% 12. GPT-6 Luna 44.2% 13. Claude Opus 4.8 42.5% 14. GPT-5.6 Luna 42.5% 15. Grok 4.6 40% 16. 050100 Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise) ## All results Furniture Assembly results by model | # | Model | Provider | Score | Setting | Source | Date | | --- | --- | --- | --- | --- | --- | --- | | 1 | [Claude Opus 5.5](https://noometry.com/models/claude-opus-5-5) | [Anthropic](https://noometry.com/providers/anthropic) | 83.3% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-22 | | 2 | [GPT-6.1 Sol](https://noometry.com/models/gpt-6-1-sol) | [OpenAI](https://noometry.com/providers/openai) | 80% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-29 | | 3 | [GPT-6 Astra](https://noometry.com/models/gpt-6-astra) | [OpenAI](https://noometry.com/providers/openai) | 80% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 | | 4 | [Claude Sonnet 5.5](https://noometry.com/models/claude-sonnet-5-5) | [Anthropic](https://noometry.com/providers/anthropic) | 75% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-29 | | 5 | [Claude Fable 5.1](https://noometry.com/models/claude-fable-5-1) | [Anthropic](https://noometry.com/providers/anthropic) | 70% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 | | 6 | [Claude Opus 5](https://noometry.com/models/claude-opus-5) | [Anthropic](https://noometry.com/providers/anthropic) | 60.8% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 | | 7 | [GPT-6 Sol](https://noometry.com/models/gpt-6-sol) | [OpenAI](https://noometry.com/providers/openai) | 58.3% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-23 | | 8 | [GPT-5.6 Sol](https://noometry.com/models/gpt-5-6-sol) | [OpenAI](https://noometry.com/providers/openai) | 56.7% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 | | 9 | [GPT-5.6 Terra](https://noometry.com/models/gpt-5-6-terra) | [OpenAI](https://noometry.com/providers/openai) | 54.2% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 | | 10 | [Claude Haiku 5.5](https://noometry.com/models/claude-haiku-5-5) | [Anthropic](https://noometry.com/providers/anthropic) | 47.5% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-10-07 | | 11 | [GPT-5.5](https://noometry.com/models/gpt-5-5) | [OpenAI](https://noometry.com/providers/openai) | 44.2% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 | | 12 | [GPT-6 Luna](https://noometry.com/models/gpt-6-luna) | [OpenAI](https://noometry.com/providers/openai) | 44.2% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-28 | | 13 | [Claude Opus 4.8](https://noometry.com/models/claude-opus-4-8) | [Anthropic](https://noometry.com/providers/anthropic) | 42.5% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 | | 14 | [GPT-5.6 Luna](https://noometry.com/models/gpt-5-6-luna) | [OpenAI](https://noometry.com/providers/openai) | 42.5% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 | | 15 | [Grok 4.6](https://noometry.com/models/grok-4-6) | [xAI](https://noometry.com/providers/xai) | 40% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-24 | | 16 | [GPT-5.2](https://noometry.com/models/gpt-5-2) | [OpenAI](https://noometry.com/providers/openai) | 38.3% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 | | 17 | [GPT-5.4](https://noometry.com/models/gpt-5-4) | [OpenAI](https://noometry.com/providers/openai) | 37.5% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 | | 18 | [Claude Fable 5](https://noometry.com/models/claude-fable-5) | [Anthropic](https://noometry.com/providers/anthropic) | 35.8% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 | | 19 | [DeepSeek V4.1 Flash](https://noometry.com/models/deepseek-v4-1-flash) | [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 34.2% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-10-08 | | 20 | [Kimi K3](https://noometry.com/models/kimi-k3) | [Moonshot AI](https://noometry.com/providers/moonshot) | 34.2% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-11 | | 21 | [Claude Opus 4.7](https://noometry.com/models/claude-opus-4-7) | [Anthropic](https://noometry.com/providers/anthropic) | 33.3% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 | | 22 | [Gemini 3.8 Flash](https://noometry.com/models/gemini-3-8-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 31.7% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 | | 23 | [Claude Opus 4.5](https://noometry.com/models/claude-opus-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 28.3% | 64K | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 | | 24 | [Claude Opus 4.6](https://noometry.com/models/claude-opus-4-6) | [Anthropic](https://noometry.com/providers/anthropic) | 28.3% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 | | 25 | [Gemini 3.1 Pro Preview](https://noometry.com/models/gemini-3-1-pro-preview) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 26.7% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 | | 26 | [Gemini 3.7 Flash](https://noometry.com/models/gemini-3-7-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 26.7% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 | | 27 | [Gemini 3.6 Flash](https://noometry.com/models/gemini-3-6-flash) | [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 23.3% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 | | 28 | [Grok 4.5](https://noometry.com/models/grok-4-5) | [xAI](https://noometry.com/providers/xai) | 22.5% | high | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-24 | | 29 | [Kimi K2.6](https://noometry.com/models/kimi-k2-6) | [Moonshot AI](https://noometry.com/providers/moonshot) | 21.7% | | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-11 | | 30 | [Grok 4.7](https://noometry.com/models/grok-4-7) | [xAI](https://noometry.com/providers/xai) | 20.8% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-24 | | 31 | [Qwen3.8 Max](https://noometry.com/models/qwen3-8-max) | [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 20% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) | 2026-09-10 | ## Compare the leaders - [Claude Opus 5.5 vs GPT-6.1 Sol](https://noometry.com/compare/claude-opus-5-5-vs-gpt-6-1-sol) - [Claude Opus 5.5 vs GPT-6 Astra](https://noometry.com/compare/claude-opus-5-5-vs-gpt-6-astra) - [Claude Opus 5.5 vs Claude Sonnet 5.5](https://noometry.com/compare/claude-opus-5-5-vs-claude-sonnet-5-5) - [Claude Opus 5.5 vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-claude-opus-5-5) - [GPT-6.1 Sol vs GPT-6 Astra](https://noometry.com/compare/gpt-6-1-sol-vs-gpt-6-astra) - [GPT-6.1 Sol vs Claude Sonnet 5.5](https://noometry.com/compare/claude-sonnet-5-5-vs-gpt-6-1-sol) ## 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) - [LMArena Document](https://noometry.com/benchmarks/arena-document) (reference) - [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 ### Which model has the highest Furniture Assembly score? As of October 2026, Claude Opus 5.5 has the highest published Furniture Assembly score on Noometry at 83.3%, out of 31 models with results. ### What is the best open-weight model on Furniture Assembly? DeepSeek V4.1 Flash has the highest Furniture Assembly accuracy among open-weight models at 34.2%, ranking 19 of 31 overall. ### Cite this page Noometry. (2026). Furniture Assembly leaderboard. Retrieved October 10, 2026, from https://noometry.com/benchmarks/furniture-assembly Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/benchmarks/furniture-assembly.md).