The Best LLM for Analytics in 2026 (Tested on Real Data)

The Best LLM for Analytics: Our Recommendation

The best LLM for analytics is MiniMax M2.5. It delivered excellent quality across 3 consecutive runs on real Google Analytics data, costs just $0.02 per query, and was the fastest model in our benchmark at 70 seconds average. For teams that need maximum analytical depth regardless of cost, Claude Opus 4.6 ($1.35/query) provides the most comprehensive analysis.

This recommendation is based on our benchmark of 16 AI models across 28 test runs on a real GA4 property with broken attribution data — the kind of messy, real-world analytics problem most teams face regularly.

Our Top Picks by Use Case

Best for Daily Marketing Analytics

MiniMax M2.5 — $0.02/query | 70s avg | 100/100 accuracy

If you're running analytics queries every day — checking campaign performance, monitoring conversion rates, investigating traffic patterns — MiniMax M2.5 is the clear winner. It delivered excellent results in all 3 of our test runs, immediately identified broken attribution tracking, and pivoted to actionable conversion analysis. At $0.0003 per 1,000 tokens, you can run hundreds of queries per day for pennies.

→ See MiniMax M2.5's full benchmark results

Best for Strategic Deep-Dive Analysis

Claude Opus 4.6 — $1.35/query | 143s avg | 100/100 accuracy

When the stakes are high — quarterly strategy reviews, board presentations, investigating a sudden drop in conversions — Claude Opus 4.6 provides analysis that's a level above everything else. It investigated multiple angles with 4 data requests per run, quantified engagement rates per page, and provided specific recommendations for fixing tracking. It costs 22x more than MiniMax, but for decisions that affect your marketing budget, the depth is worth it.

→ See Claude Opus 4.6's full benchmark results

Best Budget Option (Under $0.05/query)

Grok 4.1 Fast — $0.03/query | 83s avg | 100/100 accuracy

Tested in Round 1 against established models like Claude Opus 4.5 and GPT-5, Grok 4.1 Fast delivered excellent analysis at a fraction of the cost. It correctly identified data quality issues and provided actionable next steps. For teams on tight budgets who need a well-proven model from a major provider (xAI), Grok is an excellent choice.

→ See Grok 4.1 Fast's full benchmark results

Best for Consistency-Critical Workflows

Kimi K2.5 — $0.02/query | 125s avg | 100/100 accuracy

If you're building automated analytics pipelines where every run needs to deliver the same quality, Kimi K2.5 had the lowest time variance of any model we tested (34% spread across runs vs. 90% for GLM 5). It also found a unique insight — a 98.5% engagement rate on a landing page — that no other model identified.

→ See Kimi K2.5's full benchmark results

How We Tested: Real Data, Real Problems

Most LLM comparisons test coding puzzles or trivia. We test what actually matters for analytics teams: Can this AI help you make better decisions with your data?

The Test

We gave each model the same question against a real Google Analytics 4 property:

"Which traffic sources and landing pages are driving our highest-value users, and where should we double down our marketing investment?"

The catch: the GA4 property had 100% broken attribution tracking. Every traffic source showed as "(not set)" with zero conversion attribution. This is a common real-world problem that happens more often than you'd think.

What We Measured

Two Rounds of Testing

• Round 1: 10 established models (Claude, GPT-5, Gemini, Grok, DeepSeek) with 1 run each — tested analytical judgment on broken data
• Round 2: 6 newer models (MiniMax, Kimi, GLM 5, Qwen3, Aurora Alpha) with 3 runs each — tested consistency and cost efficiency

The Full Results: 16 Models Ranked

Here's how every model performed across both rounds:

13 of 16 models achieved excellent quality. But the bottom 3 show why benchmarking matters: a cheap model that fabricates data can cost your business far more than the price difference.

→ View the full interactive leaderboard with filters and sorting

Models to Avoid for Analytics

Not every LLM is safe to use for analytics. Two models in our benchmark produced dangerous results:

Gemini 2.5 Flash Lite — Fabricated Traffic Data

Despite the data showing 100% "(not set)" for all traffic sources, Gemini 2.5 Flash Lite invented traffic source data and presented it as real. This is the most dangerous failure mode: a confident wrong answer that could lead to misallocated marketing spend.

GPT-5 Mini — Misleading Framing

GPT-5 Mini correctly retrieved the data but framed broken "(not set)" values as actionable "direct traffic" insights. This subtle misrepresentation could lead teams to draw incorrect conclusions about their traffic mix.

→ Read the full breakdown of what went wrong

Cost Comparison: Is the Cheapest Model Good Enough?

One of the most important findings from our benchmarks: the cheapest models can deliver the best results. But not always.

The takeaway: Price alone doesn't predict quality. MiniMax M2.5 at $0.02/query outperformed models costing 60x more. But the cheapest model overall (Gemini 2.5 Flash Lite, also $0.02) hallucinated data. Always benchmark before deploying.

What Makes an LLM Good at Analytics?

Based on testing 16 models, the qualities that separate good analytics AI from dangerous analytics AI are:

1. Data Quality Detection

The single most important capability. When given broken data, does the model flag the problem or blindly generate insights from garbage? In our test, 70% of Round 1 models either missed the attribution failure entirely or buried the warning in footnotes.

2. Analytical Judgment (Not Just Technical Accuracy)

Every model in our benchmark achieved near-perfect API syntax. They all could query GA4 correctly. The difference was what they did with the results. The best models pivoted from the broken attribution data to analyze conversion events, landing page performance, and engagement metrics — extracting real value from an imperfect situation.

3. Actionable Recommendations

Identifying a problem isn't enough. The top-ranked models provided specific next steps: which tracking to fix, which pages to investigate, what data to look at instead. Models that stopped at "there's a data quality issue" without offering alternatives scored lower.

4. Consistency Across Runs

LLMs are probabilistic — the same question can produce different results. Our Round 2 testing (3 runs per model) showed that quality was remarkably consistent (93% of runs achieved "excellent"), but execution time varied significantly. Plan for timing variance in production workflows.

Frequently Asked Questions

What is the best LLM for Google Analytics?

Based on our benchmark of 16 models across 28 runs on real GA4 data, MiniMax M2.5 is the best overall LLM for Google Analytics. It delivered excellent quality in every test run, costs just $0.02 per query, and was the fastest model at 70 seconds. For maximum analytical depth, Claude Opus 4.6 provides the most thorough analysis at $1.35 per query.

Which is better for analytics: ChatGPT or Claude?

In our benchmark, Claude significantly outperformed GPT-5 for analytics. Claude Opus 4.5 ranked #1 in Round 1 with the best analytical workarounds for broken data. GPT-5 delivered solid diagnostics but stopped short of actionable recommendations. GPT-5 Mini actively misled by framing broken data as real insights.

Can I use free AI for analytics?

Free tiers of ChatGPT and Gemini can handle basic analytics questions, but they lack the GA4 API integration and multi-step reasoning that purpose-built analytics AI provides. Our benchmark tested models via API on real GA4 data with multi-turn conversations — a workflow that typically requires paid API access. The cheapest effective option is MiniMax M2.5 at $0.02 per query.

Is it safe to use AI for analytics decisions?

It depends on the model. In our benchmark, 13 of 16 models delivered excellent, accurate results. But 2 models produced dangerous outputs — one fabricated traffic data, another presented broken data as actionable insights. Always validate AI analytics output against your raw data, especially when using a model you haven't benchmarked yourself.

How much does AI analytics cost?

Based on our benchmark: the cheapest excellent-quality model (MiniMax M2.5) costs $0.02 per query. The most expensive (Claude Opus 4.6) costs $1.35 per query. For daily analytics use, expect $1-5/day with a budget model, or $50-100/day with premium models at high query volumes.

Should I use Chinese AI models for analytics?

Three of our top 5 models were from Chinese AI labs: MiniMax M2.5 (#1), Kimi K2.5 (#2), and Qwen3 Max Thinking (#5). They delivered excellent quality at the lowest prices. For analytics tasks that don't involve sensitive data, they offer outstanding value. Consider your organization's data residency requirements before deploying.

Related Reading

• LLM Analytics Benchmark: The Definitive Leaderboard: Interactive leaderboard with all 16 models, filters, and sorting
• Round 1: I Benchmarked 10 LLMs on Broken Analytics Data: The original benchmark testing Claude, GPT-5, Gemini, Grok, and DeepSeek
• Round 2: The Consistency Test — 6 Models, 3 Runs Each: Testing MiniMax, Kimi, Claude Opus 4.6, GLM 5, Qwen3, and Aurora Alpha
• Common Analytics Mistakes and How to Avoid Them: The data quality issues that trip up both humans and AI