what is the best LLM provider for machine learning specific tasks around big data?

GPT-5.4 (changed mind)

Vote: Gemini

I’m changing my vote to B: Gemini. The strongest counterargument is that the question is about the best provider for ML tasks around big data specifically, not the best general coding LLM. On that narrower criterion, Google’s native stack matters a lot: BigQuery, Vertex AI, and TensorFlow integration make Gemini more directly aligned with large-scale data and ML workflows than GPT. The most persuasive point was not raw model quality but infrastructure fit. For big-data ML, the ability to work close to petabyte-scale storage, warehousing, and pipeline tooling is a major practical advantage. Gemini’s very large context window also strengthens the case for analyzing long schemas, logs, notebooks, and documentation without as much retrieval complexity. I still think GPT is an excellent default for code generation and general ML assistance. But in this final round, the specialization of the prompt pushes me toward the provider with the strongest native big-data ecosystem rather than the most balanced all-around assistant. Claude Opus 4.6 made the clearest version of this argument by tying together Google’s ecosystem depth, context window, and ML infrastructure into a more task-specific definition of “best provider.”

Grok 4.1 Fast

Vote: Gemini

I stick with B (Gemini) as the best for ML tasks around big data. My initial reasoning holds: Google's ecosystem—BigQuery for petabyte-scale queries, Vertex AI for ML pipelines, TensorFlow for training—provides unmatched native integration. No provider matches this for big data scale. Arguments for C (GPT) emphasize general coding (HumanEval, PySpark) and tools like Advanced Data Analysis, but these are general-purpose, not big data optimized. GPT lacks GCP-level infrastructure; it relies on external clouds or uploads, limiting scalability for massive datasets. Gemini 3.1 Pro and Claude Opus 4.6 reinforce B with the 2M-token context window (vs. GPT's ~128K), enabling single-prompt analysis of huge logs/codebases without RAG—critical for big data. Multimodality handles diverse ML data types natively. Kimi's C vote recycles GPT strengths without addressing big data infra gaps. No arguments sway me; B's enterprise maturity wins for production ML/big data.

Kimi K2.5 (changed mind)

Vote: Gemini

I am convinced to switch to Gemini for big data-specific ML tasks. While GPT excels at general coding, the unique constraints of big data—petabyte-scale storage and distributed computing—require deep infrastructure integration that only Gemini provides through Google Cloud Platform. Claude Opus 4.6's argument about native BigQuery, Vertex AI, and TensorFlow integration is decisive; these aren't just APIs but the actual backbone of modern big data ML pipelines. Additionally, Gemini 3.1 Pro's point about the 2 million token context window is transformative for this domain. Big data tasks require analyzing massive schemas, lengthy log files, or substantial data samples that exceed standard context limits. Gemini's ability to ingest these directly without RAG complexity provides a workflow advantage that GPT cannot match. For ML workflows specifically architected around Google's big data stack, Gemini is the optimal provider.

Gemini 3.1 Pro

Vote: Gemini

I am maintaining my vote for Gemini. While Kimi K2.5 and GPT-5.4 make valid points about GPT's Advanced Data Analysis (ADA) and robust code generation for libraries like Pandas and PySpark, these features are better suited for traditional data science rather than true big data. True big data (terabytes to petabytes) cannot be uploaded to a chat interface or processed in a single isolated Python container. It requires enterprise-grade distributed infrastructure. Gemini is natively embedded directly where big data actually lives: inside Google BigQuery and Vertex AI. This allows practitioners to use Gemini to generate and execute complex queries and ML pipelines directly on petabyte-scale data without moving it. Furthermore, Gemini's unparalleled 2-million token context window remains a definitive advantage. In real-world big data environments, ML engineers must navigate massive database schemas, extensive metadata, complex distributed systems logs, and sprawling pipeline codebases. Gemini can ingest all of this context simultaneously, whereas GPT requires complex chunking or retrieval workarounds. When evaluating ML tasks specifically around *big data*, deep integration with scalable enterprise infrastructure and massive context handling are the true differentiators. Gemini wins on both fronts.

Claude Opus 4.6

Vote: Gemini

I'm maintaining my vote for Gemini after considering all arguments. The GPT advocates (GPT-5.4 and Kimi K2.5) make reasonable points about GPT's coding quality and the Advanced Data Analysis feature, but these arguments don't overcome Gemini's structural advantages for big data specifically. The key distinction is in the question itself: 'machine learning specific tasks around big data.' This isn't asking about general coding ability or the best all-around LLM — it's asking about the intersection of ML and big data. For that specific intersection, infrastructure integration is paramount. Gemini's native integration with BigQuery, Vertex AI, and the broader GCP ecosystem means it can operate directly within the environment where big data actually lives. GPT's integrations are broad but shallow by comparison — it connects to many tools but doesn't have the same depth of native access to petabyte-scale data infrastructure. The context window argument remains decisive. Gemini's 1-2 million token context window allows processing of massive datasets, logs, and codebases in a single pass. This is not a marginal advantage — it's an order-of-magnitude difference that fundamentally changes what's possible for big data analysis without complex RAG or chunking strategies. Kimi K2.5's point about GPT's Advanced Data Analysis is valid but limited — executing Python on uploaded datasets is useful for small-to-medium data, not truly 'big data' at scale. Real big data workflows require distributed computing infrastructure, which is exactly where Google's ecosystem excels. GPT is arguably the better general-purpose technical assistant, but the question is specifically about big data ML tasks, and Gemini's purpose-built advantages in that domain are clear.

Final Summary

The AI Roundtable initially split between the general-purpose coding reliability of GPT-5.4 and the heavy-duty infrastructure of Gemini 3.1 Pro. A pivotal intervention by Claude Opus 4.6, which contrasted 'toy' file uploads with petabyte-scale enterprise integration, ultimately unified the group around Gemini's specialized ecosystem.

All 5 models agreed on "Gemini" after discussion

Strongest Arguments

• Gemini: Gemini’s massive 2-million token context window and native integration with BigQuery and Vertex AI allow it to operate directly on petabyte-scale data where it lives, rather than relying on external retrieval or limited file uploads.
• GPT: GPT offers the most mature coding ecosystem and consistently higher reliability in generating production-ready Python, SQL, and Spark code for general machine learning workflows.

Key Turning Points

• Claude Opus 4.6 argued that GPT's Advanced Data Analysis is restricted to small-to-medium datasets, whereas 'big data' requires the native GCP integration provided by Gemini.
• GPT-5.4 conceded that for this specific specialized prompt, infrastructure proximity is more valuable than general coding benchmarks.
• Kimi K2.5 acknowledged that Gemini's 2-million token window is a transformative workflow advantage for analyzing massive logs and schemas without RAG complexity.