Which LLM is best at DAX?
DAXBench tests how models understand, write, and reason about DAX.
Methodology designed by Maxim Anatsko.
Last updated: Jul 10, 2026
127 models · 30 tasks · Initial Release
Ranked by score
| Model | ||
|---|---|---|
1 | Gemini 3.1 Flash Lite PreviewHIGH Google | 97.38% |
2 | Claude Fable 5Auto (legacy) Anthropic | 96.92% |
3 | GPT-5.3 ChatAuto (legacy) OpenAI | 96.9% |
4 | Qwen3.5 Plus 2026-02-15MED Qwen | 96.84% |
5 | GLM 5Auto (legacy) Z.AI | 96.23% |
6 | Qwen3.7 MaxAuto (legacy) Qwen | 94.53% |
7 | Gemini 3.1 Pro PreviewHIGH Google | 94.49% |
8 | Gemma 4 31BAuto (legacy) Google | 94.46% |
9 | Qwen3.6 Plus Preview (free)Auto (legacy) Qwen | 93.92% |
10 | Qwen3.5 397B A17BAuto (legacy) Qwen | 93.89% |
11 | GPT-5.4 MiniAuto (legacy) OpenAI | 93.26% |
12 | Qwen3.6 Max PreviewAuto (legacy) Qwen | 91.55% |
13 | Qwen3.5-FlashMED Qwen | 90.82% |
14 | GLM 5.1Auto (legacy) Z.AI | 90.28% |
15 | Qwen3.6 Plus (free)Auto (legacy) Qwen | 89.66% |
16 | GLM 5V TurboAuto (legacy) Z.AI | 89.11% |
17 | GPT-5.3-CodexHIGH OpenAI | 88.65% |
18 | gpt-oss-120bAuto (legacy) OpenAI | 88% |
19 | Grok 4.5Auto (legacy) xAI | 87.87% |
20 | Claude Sonnet 4.6MED Anthropic | 87.39% |
21 | Claude Sonnet 4Auto (legacy) Anthropic | 87.35% |
22 | KAT-Coder-Pro V2Auto (legacy) Kwaipilot | 87.23% |
23 | GLM 5 TurboAuto (legacy) Z.AI | 86.65% |
24 | Gemini 2.5 Flash Preview 09-2025Auto (legacy) Google | 86.17% |
25 | GPT-5.1-Codex-MaxAuto (legacy) OpenAI | 85.63% |
26 | Claude Opus 4.8Auto (legacy) Anthropic | 85.41% |
27 | Gemini 3 Pro PreviewAuto (legacy) Google | 84.9% |
28 | Claude Sonnet 4.5Auto (legacy) Anthropic | 84.43% |
29 | o3Auto (legacy) OpenAI | 84.4% |
30 | Gemini 3.1 Flash LiteAuto (legacy) Google | 84.36% |
31 | Kimi K2 ThinkingAuto (legacy) Moonshot AI | 84.35% |
32 | GPT-5.4HIGH OpenAI | 83.82% |
33 | Grok 4.3Auto (legacy) xAI | 83.74% |
34 | Grok 4.20 BetaHIGH xAI | 83.04% |
35 | Claude Opus 4.5Auto (legacy) Anthropic | 82.71% |
36 | Grok 4Auto (legacy) xAI | 82.7% |
37 | Claude Opus 4.6Auto (legacy) Anthropic | 82.03% |
38 | Gemini 3 Flash PreviewAuto (legacy) Google | 81.38% |
39 | GPT-5.2Auto (legacy) OpenAI | 81.36% |
40 | R1Auto (legacy) DeepSeek | 81.33% |
41 | Grok Build 0.1Auto (legacy) xAI | 81.32% |
42 | Kimi K2.7 CodeAuto (legacy) Moonshot AI | 81.1% |
43 | DeepSeek V4 ProAuto (legacy) DeepSeek | 80.79% |
44 | Aurora AlphaAuto (legacy) Openrouter | 80.56% |
45 | GPT-5.2 ChatAuto (legacy) OpenAI | 80.1% |
46 | Qwen3 Max ThinkingAuto (legacy) Qwen | 80.09% |
47 | Gemini 2.5 FlashAuto (legacy) Google | 79.58% |
48 | Kimi K2.6Auto (legacy) Moonshot AI | 78.71% |
49 | DeepSeek V3.2 SpecialeAuto (legacy) DeepSeek | 78.31% |
50 | DeepSeek V3.1Auto (legacy) DeepSeek | 78.28% |
Models are tested against DAX tasks of varying complexity using the Contoso sample dataset. Responses are evaluated for syntax correctness and output accuracy.
Harder tasks are worth more points. Correct solutions also earn bonus points for following DAX best practices, writing efficient code, and producing clear, readable output.
Tasks cover aggregation, time intelligence, filtering, calculation, table manipulation, iterator, context transition across basic, intermediate, and advanced levels.