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LLM for Use CaseData AnalysisGPT-5.4 vs o4-mini

GPT-5.4 vs o4-mini for Data Analysis

Which AI model is better for data analysis? We compare GPT-5.4 and o4-mini on the criteria that matter most - with a clear verdict.

Why your data analysis LLM choice matters

Data analysis LLMs must reason correctly about numbers, generate accurate and executable code, and translate raw data into clear, actionable narrative. Unlike writing errors that are easy to spot, quantitative mistakes can go undetected - making model reliability and confidence calibration especially critical for data workflows.

Key evaluation criteria for data analysis

1Accuracy of quantitative reasoning and calculations
2Quality of SQL and Python code generation
3Ability to interpret charts and structured data
4Clear, concise data-driven narrative generation

Side-by-Side Comparison

FeatureGPT-5.4Winnero4-mini
ProviderOpenAIOpenAI
Model Typetexttext
Context Window1,050,000 tokens200,000 tokens
Input Cost
$2.50/ 1M tokens
$1.10/ 1M tokens
Output Cost
$15.00/ 1M tokens
$4.40/ 1M tokens
Top pick for Data Analysis

Strengths for Data Analysis

GPT-5.4

OpenAI

1. Best Intelligence at Scale

  • OpenAI positions GPT-5.4 as its frontier model for agentic, coding, and professional workflows.
  • Built for complex professional work where stronger reasoning and higher answer quality matter.

2. Configurable Reasoning + Multimodal Input

  • Supports configurable reasoning effort from none to xhigh, letting teams balance speed and depth.
  • Accepts both text and image inputs while producing text output.

3. Massive Context for Long-Running Work

  • 1.05M token context window supports very large codebases, documents, and multi-step workflows.
  • Allows up to 128 k output tokens for long-form answers and larger generations.

4. Updated Knowledge & Broad Tool Support

  • Knowledge cut-off of Aug 31 2025 keeps it current for newer frameworks and business context.
  • Supports tools like web search, file search, code interpreter, hosted shell, computer use, and MCP in the Responses API.

o4-mini

OpenAI

1. Fast and efficient reasoning

  • Provides strong reasoning capabilities with significantly lower latency and cost compared to larger o-series models.
  • Ideal for lightweight reasoning tasks, logic steps, and quick multi-step thinking.

2. Optimized for coding tasks

  • Performs exceptionally well in code generation, debugging, and explanation.
  • Useful for IDE integrations, coding assistants, and developer tools with tight latency budgets.

3. Strong visual reasoning

  • Accepts image inputs for tasks such as diagram interpretation, charts, UI analysis, and visual logic.
  • Great for hybrid text-image reasoning flows.

4. Large 200K-token context window

  • Capable of processing long documents, multi-file codebases, or extended analysis.
  • Reduces need for chunking or external retrieval pipelines.

5. High 100K-token output limit

  • Supports lengthy reasoning sequences, full codebase explanations, or multi-section documents.

6. Broad API compatibility

  • Available in Chat Completions, Responses, Realtime, Assistants, Batch, Embeddings, and Image workflows.
  • Supports streaming, function calling, structured outputs, and fine-tuning.

7. Cost-efficient for production

  • Lower input/output pricing makes it suitable for large-scale deployments, SaaS products, and recurring tasks.

8. Succeeded by GPT-5 mini

  • GPT-5 mini offers improved speed, reasoning power, and pricing, but o4-mini remains a strong option for cost-sensitive workloads.

Verdict: Best LLM for Data Analysis

For data analysis tasks, GPT-5.4 edges ahead based on its performance profile and design priorities. It scores higher on accuracy of quantitative reasoning and calculations - the criterion that matters most for data analysis workflows.

That said, o4-mini remains a strong option. If clear, concise data-driven narrative generation is a higher priority than raw performance, or if your team is already using OpenAI's tooling, o4-mini can deliver strong results for data analysis workloads.

With Appaca, you can build data analysis apps powered by either model and switch between them at any time - no rebuild required. Test what actually performs best for your users before committing.

You know GPT-5.4 wins for data analysis. Now build with it.

Most teams spend days comparing models and hours copy-pasting prompts. With Appaca, you build a dedicated data analysis app - powered by GPT-5.4 - in minutes. No code, no re-prompting, runs on any device.

Free to start. Switch models any time. No rebuild required.

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Frequently asked questions

Is GPT-5.4 or o4-mini better for data analysis?

For data analysis tasks, GPT-5.4 has the edge based on its performance profile and design priorities. It ranks higher on accuracy of quantitative reasoning and calculations, which is the most important criterion for data analysis workflows. That said, both models can handle data analysis workloads - the best choice depends on your specific requirements and budget.

What are the key differences between GPT-5.4 and o4-mini for data analysis?

The main differences are in accuracy of quantitative reasoning and calculations, quality of sql and python code generation, ability to interpret charts and structured data. GPT-5.4 is developed by OpenAI and shares the same provider as o4-mini. Context window, pricing, and speed all differ - check the comparison table above for a side-by-side breakdown.

How much does it cost to use GPT-5.4 vs o4-mini?

o4-mini is cheaper at $1.10/million input tokens, versus $2.50/million for GPT-5.4. For data analysis workloads, the total cost difference depends on your average prompt length and volume.

Can I build a data analysis app with GPT-5.4 or o4-mini?

Yes. Both models can power data analysis applications. With Appaca, you can build a data analysis app using either GPT-5.4 or o4-mini - and switch between them at any time to find the model that performs best for your specific workflow, without rebuilding your product.

Which model should I choose if I care most about accuracy of quantitative reasoning and calculations?

GPT-5.4 is the stronger choice when accuracy of quantitative reasoning and calculations is your top priority. It ranks #4 overall for data analysis tasks. If cost or latency are constraints, o4-mini may still meet your needs at a lower cost.