Data · best for
Best AI model for Data Analysis (2026)
Exploring datasets, drawing conclusions, computing summary stats. Ranked from 346 live models on the OpenRouter catalog, weighted for reasoning quality, tool calling, structured output.
| # | Model | Score | In / 1M | Out / 1M | Context | |
|---|---|---|---|---|---|---|
| 1 | MoonshotAI: Kimi K2.6moonshotai/kimi-k2.6 | 124 | $0.80 | $3.50 | 262,144 | Try → |
| 2 | Google: Gemma 4 26B A4B (free)google/gemma-4-26b-a4b-it:free | 124 | Free | Free | 262,144 | Try → |
| 3 | Google: Gemma 4 26B A4B google/gemma-4-26b-a4b-it | 124 | $0.07 | $0.35 | 262,144 | Try → |
| 4 | Google: Gemma 4 31B (free)google/gemma-4-31b-it:free | 124 | Free | Free | 262,144 | Try → |
| 5 | Google: Gemma 4 31Bgoogle/gemma-4-31b-it | 124 | $0.13 | $0.38 | 262,144 | Try → |
| 6 | Qwen: Qwen3.6 Plusqwen/qwen3.6-plus | 124 | $0.33 | $1.95 | 1,000,000 | Try → |
| 7 | Z.ai: GLM 5V Turboz-ai/glm-5v-turbo | 124 | $1.20 | $4.00 | 202,752 | Try → |
| 8 | xAI: Grok 4.20x-ai/grok-4.20 | 124 | $2.00 | $6.00 | 2,000,000 | Try → |
| 9 | Xiaomi: MiMo-V2-Omnixiaomi/mimo-v2-omni | 124 | $0.40 | $2.00 | 262,144 | Try → |
| 10 | OpenAI: GPT-5.4 Nanoopenai/gpt-5.4-nano | 124 | $0.20 | $1.25 | 400,000 | Try → |
| 11 | OpenAI: GPT-5.4 Miniopenai/gpt-5.4-mini | 124 | $0.75 | $4.50 | 400,000 | Try → |
| 12 | Mistral: Mistral Small 4mistralai/mistral-small-2603 | 124 | $0.15 | $0.60 | 262,144 | Try → |
| 13 | ByteDance Seed: Seed-2.0-Litebytedance-seed/seed-2.0-lite | 124 | $0.25 | $2.00 | 262,144 | Try → |
| 14 | Qwen: Qwen3.5-9Bqwen/qwen3.5-9b | 124 | $0.10 | $0.15 | 262,144 | Try → |
| 15 | OpenAI: GPT-5.4openai/gpt-5.4 | 124 | $2.50 | $15.00 | 1,050,000 | Try → |
How we ranked these
For Data Analysis, we weight models on reasoning quality, tool calling, structured output. Higher means better. Scores combine OpenRouter's model metadata (context length, modality support, tool calling, structured output, reasoning capability) with public pricing. See full methodology →
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Normalizing messy tabular data with consistent fields.
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Best for JSON Extraction
Pulling structured fields out of unstructured text.
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Best for Bulk Data Labeling
Cheaply tagging thousands of items with consistent labels.
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Best for OCR / Document Parsing
Reading text out of images, PDFs, and scanned documents.
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Best for Table Extraction from PDFs
Pulling structured tables out of complex documents.