15 Best LLM for Data Analysis in 2026

TL;DR

• GPT-5.5 is the strongest general starting point for agentic data analysis, executable code, spreadsheet work, and multi-tool workflows.
• Claude Opus 4.8 is a strong fit for dense documents and long-running analytical work, while Gemini 3.1 Pro Preview is the better starting point for multimodal analysis involving PDFs, charts, images, audio, or video.
• Snowflake Arctic-Text2SQL-R2 is the specialist option for Snowflake SQL. For broader database environments, evaluate GPT-5.5, Claude Opus 4.8, and Gemini 3.1 Pro against representative schemas.
• DeepSeek-V4-Pro, Mistral Medium 3.5, NVIDIA Nemotron 3 Ultra, Llama 4 Maverick, Gemma 4, and IBM Granite 4.1 give teams several open-weight and self-hosted paths with different quality, size, and operational trade-offs.
• No benchmark identifies one universal best LLM for data analysis. Test shortlisted models on known-answer SQL, executable Python or R, messy tables, documents, charts, and repeatability cases before production use.

The best LLM for data analysis is the model that remains accurate when connected to your actual schemas, files, code environments, and validation workflow. SQL generation, spreadsheet cleanup, statistical coding, chart interpretation, long-document review, and production analytics copilots place different demands on a model.

That distinction is increasingly important. MMTU contains 28,136 questions across 25 real-world table tasks, including table understanding, transformation, schema matching, and other operations that go well beyond natural-language-to-SQL. DARE-Bench adds 6,300 deterministic data-science tasks with programmatic ground truth. Together, these benchmarks reinforce a practical point: broad analytical reliability requires more than a high general reasoning score.

This comparison covers 15 distinct models and model families mapped to various data analysis use cases. It includes frontier hosted models, efficient API options, open-weight models, and one SQL specialist.

Quick answer: the best LLM for data analysis depends on the task

For a broad proof of concept, start with GPT-5.5, Claude Opus 4.8, and Gemini 3.1 Pro Preview. Add proven specialist or top open-weight models based on deployment, data sensitivity, throughput, and infrastructure requirements.

Data-analysis workload	Start by evaluating	Why it belongs on the shortlist
General agentic analysis	GPT-5.5	Strong combination of data analysis, spreadsheet creation, code, tool use, and published professional-work evaluations
Long-running analytical research	Claude Opus 4.8	Large context window, adaptive reasoning, tool use, and strong long-horizon agent behavior
PDFs, charts, images, audio, and video	Gemini 3.1 Pro Preview	Native multimodal inputs, code execution, structured output, search grounding, and a 1M-token input limit
High-volume multimodal analysis	Gemini 3.5 Flash or Amazon Nova 2 Lite	Optimized for faster recurring workflows, documents, media, and agent loops
Live web-connected analysis	Grok 4.3	Native web search, image understanding, structured outputs, and configurable reasoning
Open-weight code-heavy analysis	DeepSeek-V4-Pro or Mistral Medium 3.5	Open weights, long-context reasoning, coding, and agentic workflow support
Multilingual enterprise copilots	Cohere Command A+ or Qwen3.7-Max	Tool use, enterprise deployment paths, long context, and multilingual or cross-region workflows
Large self-hosted analytical agents	NVIDIA Nemotron 3 Ultra	Open model with 1M-token context and a focus on reasoning, planning, and tool calling
Compact private evaluation	Gemma 4 26B A4B or Granite 4.1 30B Instruct	More manageable open models for internal testing, customization, and controlled deployment
Snowflake SQL	Arctic-Text2SQL-R2	Specialized for Snowflake schemas, dialects, business logic, and long-schema reasoning

A model name in this table is a starting point. The same model may rank differently when the workload shifts from SQL to spreadsheet manipulation or from one-shot analysis to a long-running agent.

How we chose the 15 LLMs

Each model had to satisfy three conditions:

1. It had a verified access path through an API, managed platform, downloadable weights, or a clearly documented enterprise product.
2. It had documented relevance to code, tables, structured output, multimodal files, long-context analysis, tool use, or controlled deployment.
3. It occupied a distinct role rather than duplicating another model with nearly identical capabilities and deployment characteristics.

Availability was a hard requirement. Claude Fable 5 was excluded despite its June 2026 launch because Anthropic suspended access for all users on June 12, 2026. Claude Opus 4.8 is Anthropic’s current generally available high-capability model and therefore takes the Anthropic slot in this comparison.

The two Gemini entries are intentional rather than redundant. Gemini 3.1 Pro Preview targets the most demanding multimodal and multi-step work, while the stable Gemini 3.5 Flash targets sustained, higher-volume agentic workloads. The open-weight entries also serve different deployment envelopes, from Gemma 4 and Granite 4.1 to the much larger DeepSeek-V4-Pro and Nemotron 3 Ultra.

Benchmarks were used as scoped evidence, not as a universal ranking system. MMTU measures diverse table operations, LiveSQLBench covers evolving real-world SQL tasks, DARE-Bench evaluates executable data-science workflows, and BixBench tests long, multi-stage scientific analysis. None of these alone represents every business analytics workload.

Comparison table: 15 LLMs for data analysis in 2026

Model	Best fit	Access or deployment	Main limitation
GPT-5.5	Broad agentic analysis, spreadsheets, Python, research	OpenAI API, ChatGPT, Codex	Hosted model with provider-controlled runtime
Claude Opus 4.8	Long-context research and dense-document analysis	Claude API, Bedrock, Vertex AI, Microsoft Foundry	Higher reasoning effort may increase latency and consumption
Gemini 3.1 Pro Preview	Complex multimodal and long-context analysis	Gemini API, Google AI Studio	Preview lifecycle requires deployment planning
Gemini 3.5 Flash	High-volume multimodal and recurring workflows	Stable Gemini API model	Hardest analytical tasks may still justify a Pro-tier model
DeepSeek-V4-Pro	Open-weight code-heavy and self-hosted analysis	DeepSeek API and open weights	Large self-hosted footprint
Grok 4.3	Live-data research and web-connected agents	xAI API and Amazon Bedrock	Web findings still need source and calculation validation
Qwen3.7-Max	Long-context agentic work in Alibaba Cloud	Alibaba Cloud Model Studio	Text-only input limits chart and image analysis
Mistral Medium 3.5	Open-weight agents and structured analytical output	Mistral API and open weights	Public-preview status and substantial serving requirements
NVIDIA Nemotron 3 Ultra	Large self-hosted reasoning and orchestration	NVIDIA endpoints, NIM, open model	550B-total-parameter deployment is operationally demanding
Cohere Command A+	Multilingual RAG and enterprise analytics copilots	Cohere API and private deployment	Shorter context than several frontier alternatives
Llama 4 Maverick	Open multimodal customization and ecosystem reach	Downloadable weights and cloud partners	Older than several 2026 frontier releases
Gemma 4 26B A4B	Smaller private or local multimodal experiments	Open weights and Gemini API	All 26B parameters still need to be loaded
IBM Granite 4.1 30B Instruct	Governed enterprise text, code, and tool workflows	Open model and IBM ecosystem	Text-first rather than a full multimodal analyst
Amazon Nova 2 Lite	AWS-native documents, media, and code-interpreter workflows	Amazon Bedrock	Tied closely to AWS architecture and tooling
Arctic-Text2SQL-R2	Snowflake-specific enterprise SQL	Snowflake and Cortex Analyst workflows	Specialist model, not a general data analyst

The table narrows the field. The sections below explain where each model earns a place and where its strengths stop transferring to other analytical tasks.

1. GPT-5.5

GPT-5.5 is the strongest general-purpose model to evaluate first for broad data-analysis workflows. OpenAI documents native strengths in data analysis, spreadsheets, code, online research, software operation, and multi-tool task completion. It became available through the API on April 24, 2026.

The most relevant published signals include 60.0% on FinanceAgent v1.1, 54.1% on OfficeQA Pro, and 80.5% on BixBench. These are OpenAI-reported results, but they cover financial knowledge work, office artifacts, and executable scientific analysis rather than generic question answering.

GPT-5.5 is a sensible default for mixed workflows involving Python, spreadsheet modeling, research, and report production. It still needs database permissions, code sandboxes, output validation, and human review.

2. Claude Opus 4.8

Claude Opus 4.8 is well suited to long-running analysis over dense documents, large repositories, and complex multi-step tasks. It provides a 1M-token context window through the Claude API, Amazon Bedrock, and Vertex AI, with a 200k-token window on Microsoft Foundry. It also supports adaptive thinking, tools, and up to 128k output tokens.

Anthropic highlights improved tool triggering, long-context handling, and long-horizon agent behavior. Early users cited stronger analysis over financial documents and deeper multi-step questions in Databricks Genie, although these examples remain partner reports rather than independent benchmarks.

Use Claude Opus 4.8 when analytical judgment, document synthesis, and sustained task execution matter more than minimum latency. Re-baseline cost and response time at each effort setting.

3. Gemini 3.1 Pro Preview

Gemini 3.1 Pro Preview is the strongest multimodal candidate in this list for difficult documents, charts, PDFs, images, audio, and video. Its API accepts all of those input types and supports 1,048,576 input tokens, code execution, function calling, search grounding, structured outputs, thinking, and URL context.

That combination is useful when the analysis pipeline must preserve document layout, extract tables, inspect charts, run calculations, and produce machine-readable output. Google also demonstrates Gemini 3.1 Pro in a financial-document workflow that separates PDF parsing, table extraction, and final synthesis.

The main constraint is lifecycle risk. gemini-3.1-pro-preview is a preview endpoint, so production teams need regression tests and a migration plan.

4. Gemini 3.5 Flash

Gemini 3.5 Flash is the better Gemini choice for high-volume, recurring analysis where throughput matters. It is a stable model with text, image, video, audio, and PDF inputs, a 1,048,576-token input limit, code execution, file search, function calling, search grounding, and structured outputs.

Google positions it for sustained agentic workflows, rapid coding loops, sub-agent deployment, and long-horizon tasks at scale. That makes it relevant to recurring document extraction, media classification, report enrichment, and batch analytical pipelines.

Do not assume Gemini 3.5 Flash will match Pro on the hardest schema reasoning or ambiguous statistical tasks. Test both tiers on the high-difficulty portion of the evaluation suite before optimizing for throughput.

5. DeepSeek-V4-Pro

DeepSeek-V4-Pro is a leading open-weight candidate for code-heavy and self-hosted data-analysis agents. It combines API availability with downloadable weights, a 1M-token context window, and thinking and non-thinking modes. The model has 1.6 trillion total parameters with 49 billion active per token.

DeepSeek V4-Pro is positioned for reasoning, STEM, coding, and agentic coding. Those capabilities translate to Python generation, data-pipeline debugging, statistical scripts, and tool-driven analysis, although the provider’s benchmark claims are not direct proof of table or SQL accuracy.

Its main trade-off is infrastructure. Open weights provide deployment control, but the model’s size makes self-hosted serving a serious systems project rather than a lightweight local deployment.

6. Grok 4.3

Grok 4.3 is a strong option for analysis that must combine internal context with current web information. It supports text and image input, a 1M-token context window, configurable reasoning, structured outputs, image understanding, and native web search.

Grok 4.3’s profile fits market research, competitive monitoring, public-company research, and analytical agents that need fresh external evidence. Domain allowlists and exclusions are available for web search, which makes source selection easier to control.

Keep retrieved evidence separate from calculations over private datasets. A citation-backed web summary does not validate SQL joins, spreadsheet transformations, or statistical assumptions.

7. Qwen3.7-Max

Qwen3.7-Max is a strong hosted choice for long-context agentic work in the Alibaba Cloud ecosystem. Alibaba describes it as its latest flagship Qwen model for long-chain reasoning, cross-file code understanding, and complex engineering execution. The current qwen3.7-max series supports a 1M-token context window, thinking, function calling, built-in tools, structured output, and batch invocation.

Its available tools include web search and code interpretation, which makes it relevant to research, code-assisted analysis, and production agents.

Qwen3.7-Max accepts text rather than native image or chart input. Pair it with a document parser or multimodal model when the source data includes visual tables and dashboards.

8. Mistral Medium 3.5

Mistral Medium 3.5 is a compelling open-weight option for structured analytical agents. It is a 128B dense model with a 256k context window, configurable reasoning, open weights under a modified MIT license, and a design focused on instruction following, coding, tools, and structured output.

Mistral reports 77.6% on SWE-Bench Verified and 91.4 on τ³-Telecom. Neither is a data-analysis benchmark, but both provide useful evidence for executable code and long-running tool workflows.

This model is suitable for teams that want a stronger open model without moving to the scale of DeepSeek-V4-Pro or Nemotron 3 Ultra. Its public-preview status and serving footprint still require careful operational testing.

9. NVIDIA Nemotron 3 Ultra 550B-A55B

NVIDIA Nemotron 3 Ultra is designed for large-scale self-hosted agents that need long-context reasoning, planning, and tool calling. It is an open hybrid Mamba-Transformer mixture-of-experts model with 550 billion total parameters, 55 billion active parameters, and a 1M-token context window.

NVIDIA provides deployment paths through endpoints, NIM, and self-hosted tooling, including recipes for Text2SQL adaptation and agent harnesses.

Nemotron 3 Ultra belongs in the conversation where open deployment and sophisticated agent orchestration justify a large infrastructure footprint. It is excessive for basic extraction, short SQL prompts, or low-volume internal tools.

10. Cohere Command A+

Cohere Command A+ is a strong fit for multilingual enterprise analytics copilots grounded in internal data. It supports text and image inputs, reasoning, citations, tool use, structured outputs, and 48 languages. Its context window is 128k tokens, with up to 64k output tokens.

Cohere Command A+ is especially relevant to retrieval-augmented generation, controlled tool access, structured data retrieval, and applications where citations must accompany answers. Cohere offers API access and private production deployment through Model Vault.

Its 128k context is smaller than the 1M-token windows available from several competitors. Retrieval quality and chunk selection therefore matter more in document-heavy deployments.

11. Meta Llama 4 Maverick

Llama 4 Maverick remains relevant for teams that value the Llama ecosystem, native multimodality, and open-weight customization. It is a mixture-of-experts model with 17 billion active parameters and 128 experts, designed for text and image workflows. Meta distributes it through downloadable weights and cloud partners. (developer.meta.com)

Llama 4 Maverick is ideal for custom multimodal assistants, visual data extraction, and internal model adaptation where ecosystem compatibility matters. It also has broader serving and tooling support than many newer open models.

The model dates from April 2025, so it should not receive a shortlist position based on frontier accuracy alone. Include it when operational maturity, partner availability, or existing Llama infrastructure outweighs the gap to newer models.

12. Gemma 4 26B A4B

Gemma 4 26B A4B is a practical open model for smaller private experiments and controlled multimodal applications. Google recommends it as a starting point for many Gemma workloads because it combines broad capability with lower resource requirements. It supports text and image tasks, thinking, function calling, open-weight deployment, and hosted access through the Gemini API.

The model activates about 4 billion parameters per token, but all 26 billion parameters still need to be loaded for routing. Teams should size memory around the full model rather than the active parameter count.

Gemma 4 works well for focused extraction, coding, classification, and analyst-assistant prototypes. It is not the first choice for the hardest autonomous analysis.

13. IBM Granite 4.1 30B Instruct

IBM Granite 4.1 30B Instruct is a sensible open model for governed, text-first enterprise workflows. Granite 4.1 includes 3B, 8B, and 30B dense variants, with base and instruction-tuned versions. IBM reports improvements in tool calling, instruction following, coding, and mathematical reasoning over Granite 4.0.

The 30B Instruct variant fits internal SQL assistants, code generation, structured extraction, and workflow automation where teams prefer a manageable open model and a controlled serving environment.

IBM Granite is less suitable when charts, video, or complex page layouts are central to the workload. Pair it with dedicated parsing or vision components rather than treating it as a complete multimodal analyst.

14. Amazon Nova 2 Lite

Amazon Nova 2 Lite is a strong AWS-native option for high-volume document and media analysis. It supports text, image, video, and document inputs through Amazon Bedrock. Built-in tools include web grounding and a Python code interpreter for calculations, while its agentic features cover RAG and API-driven workflows.

Amazon Nova 2 Lite is particularly relevant when source material already lives in AWS and the application needs to extract data from PDFs, spreadsheets, charts, or video before running calculations.

Its advantage is architectural fit rather than universal model superiority. Teams outside AWS should account for the integration and governance work required to introduce Bedrock into the existing platform.

15. Snowflake Arctic-Text2SQL-R2

Arctic-Text2SQL-R2 is the most specialized model in the list and the strongest candidate for Snowflake-specific SQL evaluation. Snowflake trained it around enterprise schemas, Snowflake SQL syntax, business logic, long-schema reasoning, and execution-based rewards.

Snowflake reports that R2 outperformed Gemini 3.1 Pro, Claude Opus 4.7, and other frontier models on its internal Text-to-SQL HARD benchmark. That result is relevant to Snowflake workloads, but it is a provider-specific benchmark rather than evidence of superiority across PostgreSQL, BigQuery, Databricks SQL, or general data analysis.

Use Snowflake R2 as a specialist inside Snowflake and Cortex Analyst workflows. Use a general model for interpretation, narrative reporting, chart analysis, or cross-system orchestration.

Best LLMs by data-analysis use case

SQL and database querying

For Snowflake, Arctic-Text2SQL-R2 is the clearest specialist. For heterogeneous database estates, start with GPT-5.5, Claude Opus 4.8, and Gemini 3.1 Pro, then test the exact schemas, SQL dialects, joins, permissions, and business definitions used in production.

LiveSQLBench is a useful evaluation pattern because it covers evolving, real-world text-to-SQL tasks such as business intelligence queries and CRUD operations rather than relying only on static academic examples.

A successful query must meet three tests: it runs, it returns the correct rows, and it answers the intended question. Execution success alone does not detect a wrong join key or an accidental aggregation.

Python and R-assisted analysis

GPT-5.5 is the strongest first candidate for mixed analysis and software operation. Claude Opus 4.8 is well suited to long-running analytical projects and large code or document contexts. DeepSeek-V4-Pro and Mistral Medium 3.5 are the main open-weight candidates for teams building controlled code-execution environments.

Every generated program should run inside a sandbox with restricted file, network, package, and credential access. Compare calculated outputs against known results, not against another model’s explanation.

For R-heavy workflows, test package selection and idiomatic R directly. Strong Python performance does not automatically transfer to tidyverse, Bioconductor, data.table, or specialized statistical packages.

Spreadsheets, tables, and messy data

GPT-5.5 is the most broadly documented spreadsheet candidate. Gemini 3.1 Pro is valuable when spreadsheets are mixed with PDFs, charts, and scanned documents, while Nova 2 Lite is relevant to AWS-centered document pipelines.

MMTU demonstrates why spreadsheet evaluation needs more than question answering. Its 25 task categories cover broad table understanding and manipulation, so an internal test suite should include cleaning, deduplication, joins, schema matching, column transforms, missing values, and chained operations.

Preserve intermediate tables during evaluation. They make it easier to identify whether an incorrect final result originated in parsing, transformation, filtering, or calculation.

Charts, images, documents, and long-context analysis

Gemini 3.1 Pro is the strongest starting point for mixed media because it natively accepts PDFs, images, audio, video, and text while supporting code execution. Claude Opus 4.8 is particularly useful for large document sets and sustained synthesis. GPT-5.5 fits workflows that combine document analysis with spreadsheets, research, and software tools.

Grok 4.3 belongs in this category when the model must add current web evidence. Command A+ is relevant when multilingual document retrieval and answer citations matter.

Validate chart interpretations against the underlying dataset whenever it is available. Models may miss logarithmic axes, truncated baselines, legends, annotations, or differences between absolute values and percentage changes.

Open-source and self-hosted workflows

DeepSeek-V4-Pro is the highest-capacity open-weight option in this shortlist for code-heavy analysis. Mistral Medium 3.5 offers a more contained model for structured agent workflows, while Nemotron 3 Ultra targets large-scale orchestration and long-context reasoning.

Gemma 4 26B A4B and Granite 4.1 30B Instruct are more approachable for focused internal deployments. Llama 4 Maverick remains useful where existing Llama tooling and partner support reduce integration effort.

Open weights do not make an architecture private by default. Privacy depends on where inference runs, how prompts and outputs are logged, who controls the endpoint, and whether data reaches external observability or support systems.

Production analytics copilots

Production copilots need reliable structured output, tool permissions, audit logs, data lineage, and rollback behavior. GPT-5.5, Claude Opus 4.8, Gemini 3.5 Flash, Command A+, Qwen3.7-Max, and Nova 2 Lite all deserve evaluation, but for different platform environments.

Use the strongest model only where stronger reasoning changes the outcome. Routing extraction, classification, and formatting calls to a faster model may reduce latency and cost without weakening the final analysis.

How to evaluate LLMs on your own data

A useful model evaluation resembles an internal software test suite rather than a prompt competition.

1. Build representative tasks. Include SQL, executable Python or R, spreadsheet transformations, document extraction, chart interpretation, and long-context questions that mirror real workflows.
2. Create verifiable answers. Use known query results, checked calculations, reference code, analyst-reviewed outputs, and deterministic assertions. DARE-Bench uses executable ground truth for this reason.
3. Run the full workflow. Evaluate file ingestion, retrieval, tools, code execution, and output parsing. A good answer inside a chat interface may fail once placed behind function calls and permissions.
4. Repeat difficult cases. Run the same task several times and track changes in queries, assumptions, code, output structure, and conclusions.
5. Measure operational performance. Record task success, executable-code rate, exact-match fields, analyst correction time, P95 latency, token or infrastructure cost, tool errors, and the percentage of cases that require escalation.

Keep a held-out regression set after selecting a model. Provider updates, prompt changes, tool-schema changes, and serving-stack upgrades may alter behavior even when the application code looks unchanged.

Risks and guardrails: when not to trust an LLM as analyst of record

An LLM should not be the final authority for regulated decisions, financial reporting, medical interpretation, executive metrics, or other high-impact outputs unless the surrounding system provides appropriate validation and human sign-off.

Common failures include:

• Semantically wrong SQL: the query executes but uses the wrong join, date range, population, or denominator.
• Plausible but incorrect code: the program runs successfully while answering a different statistical question.
• Unsupported document interpretation: the model overlooks a footnote, chart scale, or qualification in the source.
• Inconsistent conclusions: repeated runs produce different assumptions or summaries from the same data.
• Excessive permissions: a model or agent receives broader database, file, network, or write access than the task requires.

Use read-only credentials by default. Separate query generation from execution, validate structured outputs against schemas, and require explicit approval for write operations.

Self-correction is useful, but it is not a control mechanism by itself. A model may confidently approve its own flawed query or code.

Hosted API vs open-source and self-hosted LLMs

Deployment path	Best suited to	Main operational responsibility	Relevant models
Hosted frontier API	Fast evaluation and highest-capability managed access	Provider review, data terms, rate limits, cost, logging, fallback design	GPT-5.5, Claude Opus 4.8, Gemini 3.1 Pro, Grok 4.3
Managed efficient API	Recurring or high-volume production workflows	Model routing, quality thresholds, platform integration	Gemini 3.5 Flash, Qwen3.7-Max, Command A+, Nova 2 Lite
Open weights on a managed endpoint	More deployment control without owning every serving component	Endpoint configuration, observability, data flow, model lifecycle	DeepSeek-V4-Pro, Mistral Medium 3.5, Llama 4 Maverick
Fully self-hosted	Customization, internal policy, isolated environments, model research	GPUs, serving stack, security, monitoring, upgrades, capacity planning	DeepSeek-V4-Pro, Nemotron 3 Ultra, Gemma 4, Granite 4.1
Platform specialist	Deep integration with one data platform	Platform-specific semantics, access controls, evaluation coverage	Arctic-Text2SQL-R2

Hosted APIs are usually the fastest path to a credible comparison. Self-hosting becomes reasonable when deployment control, customization, policy, or workload volume justifies the additional engineering.

Cost comparisons must include more than token or GPU prices. Account for idle capacity, engineering time, observability, model upgrades, incident response, and the analyst time spent correcting outputs.

Infrastructure considerations for LLM-powered data analysis

Infrastructure requirements change as the project moves from prompt testing to batch evaluation, fine-tuning, self-hosted inference, and production agents.

A hosted API proof of concept may need little dedicated compute. An open-weight evaluation may require GPU-backed endpoints, model storage, container images, secure data access, sandboxed code execution, and a repeatable test runner. Production adds autoscaling decisions, capacity limits, monitoring, failure handling, and rollback.

Match infrastructure to the workload

For batch evaluation, prioritize reproducibility and cost tracking. For interactive analytics, measure P95 latency and concurrency. Fine-tuning introduces data preparation, checkpoint storage, experiment tracking, and holdout evaluations. Self-hosted inference adds model loading, quantization, serving frameworks, GPU memory planning, and upgrade procedures.

Do not choose hardware from parameter count alone. Precision, quantization, context length, KV-cache requirements, batch size, parallelism, and latency targets all affect the final configuration.

Where Fluence GPU Cloud fits

Fluence GPU Cloud is an infrastructure option for teams whose model evaluation points toward GPU-backed experimentation, fine-tuning, inference, or self-hosted deployment. The approved product scope includes a GPU compute marketplace, GPU containers, GPU virtual machines, GPU bare metal, and documented console and API deployment workflows.

Before planning a production deployment, verify the currently available configurations on Fluence, pricing, locations, capacity, access behavior, and operational constraints.

Conclusion

There is no universal best LLM for data analysis. GPT-5.5 is the strongest broad starting point, Claude Opus 4.8 fits long-running analytical work, Gemini 3.1 Pro leads the multimodal shortlist, and Arctic-Text2SQL-R2 provides a targeted Snowflake SQL option. Open-weight models provide more control, but they introduce serving, security, monitoring, and capacity responsibilities.

Build a representative evaluation suite before committing. Include known-answer SQL, executable Python or R, messy spreadsheet transformations, document and chart checks, and repeated runs. Measure task success, analyst correction time, P95 latency, operational cost, and failure behavior.

When the selected path requires GPU-backed evaluation, fine-tuning, inference, or self-hosting, map those workloads to concrete compute requirements. Fluence GPU Cloud is one infrastructure option to assess after model selection, subject to verification of its current configurations, pricing, locations, availability, and operating constraints.

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