Reasoning benchmark

# NYT Connections (extended) leaderboard

> NYT Connections (extended) results for 91 AI models, led by GPT-6 Astra at 98.1%. What the benchmark measures, who runs it, and a source for every score.
- Canonical page: https://noometry.com/benchmarks/nyt-connections
- Last updated: 2026-10-10
- Title: NYT Connections (extended) Leaderboard (October 2026): Scores by Model

As of October 2026, GPT-6 Astra has the highest published NYT Connections (extended) score on Noometry at 98.1%, out of 91 models with results.

Last verified October 10, 2026

## About NYT Connections (extended)

New York Times Connections puzzles made harder with extra distractor words: sort 16 or more words into groups of four by a hidden theme.

- **Category:** [Reasoning](https://noometry.com/best/reasoning)
- **Introduced:** 2024
- **Size:** 940 puzzles
- **Format:** Word grouping
- **Unit:** Percent (random guessing ≈ 0%)
- **Official site:** [github.com](https://github.com/lechmazur/nyt-connections)

## Top 15 models

Top models on NYT Connections (extended)

1.  GPT-6 Astra 98.1%
2.  Gemini 3.1 Pro Preview 97.4%
3.  Gemini 3.8 Flash 97.4%
4.  GPT-5.5 96.2%
5.  GPT-6.1 Sol 95.5%
6.  Gemini 3 Pro 94.4%
7.  Claude Opus 5 94.3%
8.  Gemini 3.7 Flash 94%
9.  GPT-5.6 Sol 93.8%
10.  Kimi K3 93.6%
11.  Claude Fable 5 92.7%
12.  Gemini 3.5 Flash 92.6%
13.  Claude Opus 4.6 92.1%
14.  DeepSeek V4 Pro 91.3%
15.  GPT-5.4 91.3%
16.  9092949698100

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## All results

NYT Connections (extended) results by model
| # | Model | Provider | Score | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | [GPT-6 Astra](https://noometry.com/models/gpt-6-astra) | [OpenAI](https://noometry.com/providers/openai) | 98.1% | xhigh reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 2 | [Gemini 3.1 Pro Preview](https://noometry.com/models/gemini-3-1-pro-preview) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 97.4% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 3 | [Gemini 3.8 Flash](https://noometry.com/models/gemini-3-8-flash) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 97.4% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 4 | [GPT-5.5](https://noometry.com/models/gpt-5-5) | [OpenAI](https://noometry.com/providers/openai) | 96.2% | xhigh reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 5 | [GPT-6.1 Sol](https://noometry.com/models/gpt-6-1-sol) | [OpenAI](https://noometry.com/providers/openai) | 95.5% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 6 | [Gemini 3 Pro](https://noometry.com/models/gemini-3-pro) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 94.4% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 7 | [Claude Opus 5](https://noometry.com/models/claude-opus-5) | [Anthropic](https://noometry.com/providers/anthropic) | 94.3% | xhigh reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 8 | [Gemini 3.7 Flash](https://noometry.com/models/gemini-3-7-flash) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 94% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 9 | [GPT-5.6 Sol](https://noometry.com/models/gpt-5-6-sol) | [OpenAI](https://noometry.com/providers/openai) | 93.8% | xhigh reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 10 | [Kimi K3](https://noometry.com/models/kimi-k3) | [Moonshot AI](https://noometry.com/providers/moonshot) | 93.6% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 11 | [Claude Fable 5](https://noometry.com/models/claude-fable-5) | [Anthropic](https://noometry.com/providers/anthropic) | 92.7% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 12 | [Gemini 3.5 Flash](https://noometry.com/models/gemini-3-5-flash) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 92.6% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 13 | [Claude Opus 4.6](https://noometry.com/models/claude-opus-4-6) | [Anthropic](https://noometry.com/providers/anthropic) | 92.1% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 14 | [DeepSeek V4 Pro](https://noometry.com/models/deepseek-v4-pro) |  [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 91.3% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 15 | [GPT-5.4](https://noometry.com/models/gpt-5-4) | [OpenAI](https://noometry.com/providers/openai) | 91.3% | xhigh reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 16 | [Claude Opus 4.8](https://noometry.com/models/claude-opus-4-8) | [Anthropic](https://noometry.com/providers/anthropic) | 91.1% | xhigh reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 17 | [GPT-6 Sol](https://noometry.com/models/gpt-6-sol) | [OpenAI](https://noometry.com/providers/openai) | 90.1% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 18 | [Claude Fable 5.1](https://noometry.com/models/claude-fable-5-1) | [Anthropic](https://noometry.com/providers/anthropic) | 90% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 19 | [DeepSeek V4.1 Flash](https://noometry.com/models/deepseek-v4-1-flash) |  [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 89.6% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 20 | [DeepSeek V4 Flash](https://noometry.com/models/deepseek-v4-flash) |  [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 89.6% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 21 | [Grok 4.20 Multi-Agent](https://noometry.com/models/grok-4-20-multi-agent) | [xAI](https://noometry.com/providers/xai) | 89.6% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 22 | [Gemini 3.6 Flash](https://noometry.com/models/gemini-3-6-flash) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 89% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 23 | [Claude Opus 5.5](https://noometry.com/models/claude-opus-5-5) | [Anthropic](https://noometry.com/providers/anthropic) | 88.5% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 24 | [Qwen3.8 Max](https://noometry.com/models/qwen3-8-max) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 88.3% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 25 | [Grok 4.1 Fast](https://noometry.com/models/grok-4-1-fast) | [xAI](https://noometry.com/providers/xai) | 87.4% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 26 | [Kimi K2.6](https://noometry.com/models/kimi-k2-6) | [Moonshot AI](https://noometry.com/providers/moonshot) | 87.2% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 27 | [Grok 4.20 (Non-Reasoning)](https://noometry.com/models/grok-4-20) | [xAI](https://noometry.com/providers/xai) | 85.4% | reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 28 | [Muse Spark 1.3](https://noometry.com/models/muse-spark-1-3) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 85.1% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 29 | [Qwen3.7 Max](https://noometry.com/models/qwen3-7-max) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 85.1% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 30 | [Muse Spark 1.1](https://noometry.com/models/muse-spark-1-1) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 84.9% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 31 | [GPT-5.2](https://noometry.com/models/gpt-5-2) | [OpenAI](https://noometry.com/providers/openai) | 83.6% | xhigh reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 32 | [Gemini 3 Flash Preview](https://noometry.com/models/gemini-3-flash-preview) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 83.1% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 33 | [Claude Sonnet 4.6](https://noometry.com/models/claude-sonnet-4-6) | [Anthropic](https://noometry.com/providers/anthropic) | 80.9% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 34 | [Claude Sonnet 5.5](https://noometry.com/models/claude-sonnet-5-5) | [Anthropic](https://noometry.com/providers/anthropic) | 80.5% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 35 | [Grok 4.6](https://noometry.com/models/grok-4-6) | [xAI](https://noometry.com/providers/xai) | 80% | xhigh reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 36 | [Grok 4.5](https://noometry.com/models/grok-4-5) | [xAI](https://noometry.com/providers/xai) | 79.9% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 37 | [GPT-5.2 Pro](https://noometry.com/models/gpt-5-2-pro) | [OpenAI](https://noometry.com/providers/openai) | 79.3% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 38 | [Muse Spark 1.2](https://noometry.com/models/muse-spark-1-2) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 79.2% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 39 | [GPT-5.6 Terra](https://noometry.com/models/gpt-5-6-terra) | [OpenAI](https://noometry.com/providers/openai) | 78.4% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 40 | [GLM-5.1](https://noometry.com/models/glm-5-1) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 77.7% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 41 | [Grok 4.7](https://noometry.com/models/grok-4-7) | [xAI](https://noometry.com/providers/xai) | 76.8% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 42 | [Claude Sonnet 5](https://noometry.com/models/claude-sonnet-5) | [Anthropic](https://noometry.com/providers/anthropic) | 75.1% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 43 | [GLM-5](https://noometry.com/models/glm-5) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 74.8% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 44 | [Qwen3.7 Plus](https://noometry.com/models/qwen3-7-plus) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 74.8% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 45 | [GLM-5.2](https://noometry.com/models/glm-5-2) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 74.3% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 46 | [GLM-5.3](https://noometry.com/models/glm-5-3) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 74.2% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 47 | [Qwen3.6 Max Preview](https://noometry.com/models/qwen3-6-max-preview) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 74.1% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 48 | [Gemma 4 31B IT](https://noometry.com/models/gemma-4-31b-it) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 70.6% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 49 | [Kimi K2.5](https://noometry.com/models/kimi-k2-5) | [Moonshot AI](https://noometry.com/providers/moonshot) | 69.9% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 50 | [GPT-5.6 Luna](https://noometry.com/models/gpt-5-6-luna) | [OpenAI](https://noometry.com/providers/openai) | 69.4% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 51 | [GPT-6 Luna](https://noometry.com/models/gpt-6-luna) | [OpenAI](https://noometry.com/providers/openai) | 68.7% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 52 | [Hy4 preview](https://noometry.com/models/hy4-preview) |  [![](/logos/tencent.svg) Tencent](https://noometry.com/providers/tencent) | 68.2% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 53 | [Claude Haiku 5.5](https://noometry.com/models/claude-haiku-5-5) | [Anthropic](https://noometry.com/providers/anthropic) | 65.7% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 54 | [MiniMax-M3](https://noometry.com/models/minimax-m3) |  [![](/logos/minimax.svg) MiniMax](https://noometry.com/providers/minimax) | 65.1% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 55 | [GPT-5.4 mini](https://noometry.com/models/gpt-5-4-mini) | [OpenAI](https://noometry.com/providers/openai) | 61.8% | xhigh reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 56 | [Gemini 3.5 Flash Lite](https://noometry.com/models/gemini-3-5-flash-lite) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 60.4% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 57 | [Qwen3.6 Plus](https://noometry.com/models/qwen3-6-plus) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 60.3% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 58 | [Qwen3.5 397B-A17B](https://noometry.com/models/qwen3-5-397b-a17b) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 58.9% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 59 | [Grok 4.3](https://noometry.com/models/grok-4-3) | [xAI](https://noometry.com/providers/xai) | 55.2% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 60 | [Qwen3.8 27B](https://noometry.com/models/qwen3-8-27b) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 54.5% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 61 | [Claude Opus 4.5](https://noometry.com/models/claude-opus-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 52.5% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 62 | [Qwen3.5 122B-A10B](https://noometry.com/models/qwen3-5-122b-a10b) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 51.7% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 63 | [Qwen3.5 27B](https://noometry.com/models/qwen3-5-27b) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 47.9% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 64 | [Qwen3.7 Flash](https://noometry.com/models/qwen3-7-flash) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 43.8% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 65 | [Qwen3.6 35B-A3B](https://noometry.com/models/qwen3-6-35b-a3b) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 41.6% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 66 | [Hy3](https://noometry.com/models/hy3) |  [![](/logos/tencent.svg) Tencent](https://noometry.com/providers/tencent) | 41.2% | high | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 67 | [Step 3.7 Flash](https://noometry.com/models/step-3-7-flash) |  [![](/logos/stepfun.svg) StepFun](https://noometry.com/providers/stepfun) | 39.7% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 68 | [Claude Opus 4.7](https://noometry.com/models/claude-opus-4-7) | [Anthropic](https://noometry.com/providers/anthropic) | 39% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 69 | [Claude Sonnet 4.5](https://noometry.com/models/claude-sonnet-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 37.3% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 70 | [DeepSeek-V3.2-Exp](https://noometry.com/models/deepseek-v3-2-exp) |  [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 36.7% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 71 | [MiMo-V2.5-Pro](https://noometry.com/models/mimo-v2-5-pro) | [Xiaomi](https://noometry.com/providers/xiaomi) | 34.4% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 72 | [Qwen3 Max](https://noometry.com/models/qwen3-max) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 30.1% | 2026-01-23 | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 73 | [Seed 2.0 Pro](https://noometry.com/models/dola-seed-2-0-pro) |  [![](/logos/bytedance.svg) ByteDance Seed](https://noometry.com/providers/bytedance) | 28.4% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 74 | [Step 3.5 Flash](https://noometry.com/models/step-3-5-flash) |  [![](/logos/stepfun.svg) StepFun](https://noometry.com/providers/stepfun) | 28.4% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 75 | [Mistral Large 4](https://noometry.com/models/mistral-large-4) |  [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 27.4% | high | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 76 | [MiMo-V2-Pro](https://noometry.com/models/mimo-v2-pro) | [Xiaomi](https://noometry.com/providers/xiaomi) | 25.8% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 77 | [MiniMax-M2.7](https://noometry.com/models/minimax-m2-7) |  [![](/logos/minimax.svg) MiniMax](https://noometry.com/providers/minimax) | 24.7% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 78 | [ERNIE 5.1](https://noometry.com/models/ernie-5-1) |  [![](/logos/baidu.svg) Baidu](https://noometry.com/providers/baidu) | 23.4% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 79 | [Longcat Flash Chat](https://noometry.com/models/longcat-flash-chat) | [Meituan](https://noometry.com/providers/meituan) | 17.7% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 80 | [MiniMax-M2.5](https://noometry.com/models/minimax-m2-5) |  [![](/logos/minimax.svg) MiniMax](https://noometry.com/providers/minimax) | 16.8% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 81 | [Trinity Large Thinking](https://noometry.com/models/trinity-large-thinking) |  [![](/logos/arcee.svg) Arcee AI](https://noometry.com/providers/arcee) | 16.5% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 82 | [Nemotron 3 Super](https://noometry.com/models/nemotron-3-super) |  [![](/logos/nvidia.svg) NVIDIA](https://noometry.com/providers/nvidia) | 15.4% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 83 | [MiniMax-M2](https://noometry.com/models/minimax-m2) |  [![](/logos/minimax.svg) MiniMax](https://noometry.com/providers/minimax) | 14.8% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 84 | [Claude Haiku 4.5](https://noometry.com/models/claude-haiku-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 14.3% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 85 | [Mistral Medium 3.5](https://noometry.com/models/mistral-medium-3-5) |  [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 12.9% | high | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 86 | [MiniMax-M2.1](https://noometry.com/models/minimax-m2-1) |  [![](/logos/minimax.svg) MiniMax](https://noometry.com/providers/minimax) | 11.2% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 87 | [ERNIE 5.0 0110](https://noometry.com/models/ernie-5-0) |  [![](/logos/baidu.svg) Baidu](https://noometry.com/providers/baidu) | 10.3% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 88 | [Gemini 3.1 Flash Lite](https://noometry.com/models/gemini-3-1-flash-lite) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 8.2% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 89 | [Llama 4 Maverick](https://noometry.com/models/llama-4-maverick) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 8% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 90 | [Mistral Large 3](https://noometry.com/models/mistral-large-3) |  [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 7.5% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| 91 | [Mistral Medium 3.1](https://noometry.com/models/mistral-medium-3-1) |  [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 6.5% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |

## Compare the leaders

-   [GPT-6 Astra vs Gemini 3.1 Pro Preview](https://noometry.com/compare/gemini-3-1-pro-preview-vs-gpt-6-astra)
-   [GPT-6 Astra vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-gpt-6-astra)
-   [GPT-6 Astra vs GPT-5.5](https://noometry.com/compare/gpt-5-5-vs-gpt-6-astra)
-   [GPT-6 Astra vs GPT-6.1 Sol](https://noometry.com/compare/gpt-6-1-sol-vs-gpt-6-astra)
-   [Gemini 3.1 Pro Preview vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-1-pro-preview-vs-gemini-3-8-flash)
-   [Gemini 3.1 Pro Preview vs GPT-5.5](https://noometry.com/compare/gemini-3-1-pro-preview-vs-gpt-5-5)

## Other reasoning benchmarks

-   [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2)
-   [SimpleBench](https://noometry.com/benchmarks/simplebench)
-   [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning)
-   [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1)
-   [CritPt](https://noometry.com/benchmarks/critpt)
-   [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles)
-   [EnigmaEval](https://noometry.com/benchmarks/enigmaeval)
-   [Thematic Generalization](https://noometry.com/benchmarks/thematic-generalization)
-   [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts)
-   [EBR-Bench](https://noometry.com/benchmarks/ebr-bench)
-   [LiveBench Reasoning](https://noometry.com/benchmarks/livebench-reasoning)
-   [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles)

## Frequently asked questions

### What does NYT Connections (extended) measure?

New York Times Connections puzzles made harder with extra distractor words: sort 16 or more words into groups of four by a hidden theme.

### Which model has the highest NYT Connections (extended) score?

As of October 2026, GPT-6 Astra has the highest published NYT Connections (extended) score on Noometry at 98.1%, out of 91 models with results.

### What is the best open-weight model on NYT Connections (extended)?

Kimi K3 has the highest NYT Connections (extended) accuracy among open-weight models at 93.6%, ranking 10 of 91 overall.

### Cite this page

Noometry. (2026). NYT Connections (extended) leaderboard. Retrieved October 10, 2026, from https://noometry.com/benchmarks/nyt-connections

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/benchmarks/nyt-connections.md).