Compare AI models for data analysis, statistical reasoning, data visualization, and business intelligence. Find the best model for your data workflow.
AI models are transforming data analysis — from writing SQL queries and Python scripts to interpreting complex datasets and generating visualizations. The best data analysis models combine strong coding abilities with statistical reasoning and clear communication of insights.
Here are the top models ranked by their ability to handle real-world data analysis tasks.
| # | Model | Provider | Score | Rating | Context | Input $/1M | Output $/1M |
|---|---|---|---|---|---|---|---|
| 🥇 | GPT-o1 Designed for complex reasoning tasks. Achieved 83.3% on AIME 2024 (math competition) vs GPT-4o's 13.4%. State-of-the-art on GPQA Diamond (PhD-level science). Significantly better at multi-step logic, formal proofs, and algorithmic problems. Slower but much more accurate on hard tasks. | OpenAI | 93.5 | ★ 4.6 | 200K | $15.00 | $60.00 |
| 🥈 | GPT-4o Excels at coding (HumanEval), math (GSM8K, MATH), and broad knowledge (MMLU). Strong multimodal understanding of images, audio, and text. Often cited as a top all-rounder with excellent instruction following and structured output support. | OpenAI | 92.5 | ★ 4.7 | 128K | $2.50 | $10.00 |
| 🥉 | DeepSeek R1 Matches or exceeds o1 on AIME 2024 (79.8%), MATH-500 (97.3%), and Codeforces. Open-weight with distilled smaller variants available. Uses extended thinking for complex problems. Revolutionary cost-performance for reasoning tasks. | DeepSeek | 92.0 | ★ 4.6 | 128K | $0.55 | $2.19 |
| 4 | Claude 3.5 Sonnet Outstanding at long-form writing, nuanced analysis, and instruction following. Very strong on coding and math benchmarks. Especially praised for editing, summarization, and safety-conscious outputs. 200K context window enables processing large documents and codebases. | Anthropic | 91.2 | ★ 4.6 | 200K | $3.00 | $15.00 |
| 5 | Gemini 1.5 Pro Standout 1M token context window enables processing entire codebases, long documents, and hours of video. Strong on reasoning, knowledge, and multimodal tasks. Excellent for RAG and retrieval-heavy workflows. Native understanding of images, audio, and video. | 90.8 | ★ 4.5 | 1.0M | $1.25 | $5.00 | |
| 6 | Claude 3 Opus State-of-the-art on GPQA, MMLU, and MMMU at release. Very strong on grade-school math (GSM8K) and graduate-level reasoning. Excellent for complex analysis and creative writing tasks. 200K context window. | Anthropic | 90.5 | ★ 4.6 | 200K | $15.00 | $75.00 |
| 7 | DeepSeek V3 Exceptional cost-efficiency: competitive with GPT-4o on most benchmarks at ~10x lower cost. Strong on math, coding, and Chinese language tasks. Open-weight with MoE architecture for efficient inference. Particularly good for production deployments where cost matters. | DeepSeek | 90.0 | ★ 4.5 | 128K | $0.27 | $1.10 |
| 8 | Gemini 2.0 Flash Exceptional speed-to-quality ratio with 1M context window. Native tool use, code execution, and multimodal output (image and audio generation). Outperforms Gemini 1.5 Pro on most benchmarks at a fraction of the cost. Strong for agentic workflows. | 89.5 | ★ 4.5 | 1.0M | $0.10 | $0.40 | |
| 9 | Qwen 3.5 397B-A17B Leading open-weight model on vision (MMMU, MathVision) and instruction following (IFBench). Strong coding (SWE-bench Verified) and agentic tasks. Apache 2.0 license with support for 201 languages. MoE architecture keeps inference cost low relative to quality. | Alibaba | 89.2 | ★ 4.5 | 256K | Free | Free |
| 10 | GPT-o1 mini 80% cheaper than o1 while retaining strong reasoning on STEM and coding. Good for math competitions, algorithm problems, and multi-step reasoning where speed matters more than peak performance. Better cost-performance ratio than o1 for many reasoning tasks. | OpenAI | 89.0 | ★ 4.4 | 128K | $3.00 | $12.00 |
| 11 | Grok 2 Strong on MMLU, HumanEval, and MATH. Competitive with Claude 3.5 Sonnet and GPT-4 Turbo at release. Real-time information access via X integration. Good at conversational reasoning and humor. | xAI | 89.0 | ★ 4.4 | 128K | $2.00 | $10.00 |
| 12 | Llama 3.1 405B One of the strongest open-weight models. Excellent for self-hosting, fine-tuning, and data-sovereign deployments. Strong on general reasoning, coding, and knowledge tasks. Llama license allows broad commercial use. | Meta | 88.4 | ★ 4.4 | 128K | Free | Free |
| 13 | Grok 3 Beta Trained with 10x compute of Grok 2 on the Colossus supercluster. 1M token context. Strong on MMLU-Pro, GPQA Diamond, and AIME 2025. Features Think and Big Brain reasoning modes for complex problems. | xAI | 88.2 | ★ 4.5 | 1.0M | $3.00 | $15.00 |
| 14 | Qwen 2.5 Coder 32B Leading open-source code model matching GPT-4o on HumanEval and LiveCodeBench. Strong on 92+ programming languages. Excellent for code completion, generation, and repair. Apache 2.0 license for broad commercial use. | Alibaba | 88.0 | ★ 4.5 | 128K | — | — |
| 15 | Qwen 3.5 122B-A10B Strong tool use and function-calling (BFCL-V4). Good balance of capability and efficiency with 10B active parameters. Open-weight Apache 2.0. Excellent for multilingual and code workflows. | Alibaba | 87.5 | ★ 4.4 | 256K | Free | Free |
| 16 | Mistral Large Excellent balance of reasoning and multilingual ability supporting 12+ languages natively. Strong at code and math. Trusted for EU data sovereignty with European hosting. Competitive for production use cases where multilingual support matters. | Mistral AI | 87.1 | ★ 4.3 | 128K | $2.00 | $6.00 |
| 17 | Llama 3.3 70B Matches Llama 3.1 405B quality on many benchmarks despite being ~6x smaller. Excellent for self-hosting on consumer-grade GPUs. Strong instruction following and coding. Llama license for broad commercial use. | Meta | 86.5 | ★ 4.4 | 128K | — | — |
| 18 | Codestral Among the top models for code completion and generation on HumanEval. Optimized for IDE integration with fill-in-the-middle support. Supports 80+ programming languages. Fast inference for real-time coding assistance. | Mistral AI | 86.2 | ★ 4.6 | 32K | $0.20 | $0.60 |
| 19 | Qwen 3.5 27B Efficient for its size with strong instruction following and multilingual support. Good for on-prem or cost-sensitive deployments. Competitive with much larger open models on many benchmarks. Apache 2.0 license. | Alibaba | 85.8 | ★ 4.3 | 256K | Free | Free |
| 20 | Grok 2 Mini Good balance of speed and capability. Competitive on MMLU, MATH, and HumanEval for its efficiency class. Well-suited for conversational use on X. | xAI | 85.5 | ★ 4.3 | 128K | $2.00 | $10.00 |
Models that can execute code (like ChatGPT's Code Interpreter or Claude Code) are dramatically more useful for data analysis because they can actually run queries, process data, and generate visualizations rather than just writing code.
Look at MATH and GSM8K benchmark scores for mathematical reasoning ability. Models strong in math tend to better understand statistical concepts, distributions, and quantitative relationships in your data.
If you're passing raw data to the model, context window size matters. Gemini's 2M token context can process larger datasets in a single prompt. For very large datasets, consider using code execution to process data rather than passing it as text.