LLM Models Comparison Table (2025) — Data & Automation Focus

Here’s a pragmatic, up‑to‑date roundup of the major LLM families you’ll actually encounter in the wild—what they’re best at, how they’re priced, and where they’re rough around the edges. I’m focusing on the models that dominate developer usage, cloud platforms, and enterprise deals rather than attempting an impossible “every model ever” list.

Comparison of popular large language models, including GPT-5, Claude, Gemini, Llama, and Mistral.

LLM Models Comparison (2025) — Part 1

Model / Family	Popularity	Quality & Strengths	Common Tasks	Pricing (API / Access)	Pros	Cons
OpenAI – GPT-5 (5, mini, nano)	★★★★★ Most used globally	SOTA reasoning, coding, agents, strong tool use, vision+text	Coding, analysis, multi-tool workflows	GPT-5: ~$1.25/M input, $10/M output; mini/nano cheaper; ChatGPT Free, Team, Pro, Enterprise	Best-in-class quality; huge ecosystem; excellent tool calling	Expensive at scale; feature gates by plan
Anthropic – Claude Opus 4.1	★★★★☆ Rapidly growing	Careful reasoning, long-form analysis, safer defaults	Policy writing, research, code assistance	API, Bedrock, Vertex; Opus pricing premium tier	Accurate on long docs; safety guardrails	Slower; higher cost for volume
Google – Gemini 2.5 (Pro, Flash, Flash-Lite)	★★★★☆ Strong Workspace adoption	Pro: deep reasoning; Flash: fast & cheap	Google integration, assistants, apps	Pro: ~$1.25/M input, $10/M output; Flash much cheaper	Great price/performance in Flash; deep Google tie-in	Version sprawl; Pro cost rises on long prompts
Meta – Llama 4 (Maverick, Scout)	★★★★☆ Popular in open-source	Open weights, custom fine-tunes, private deploys	Internal assistants, edge apps	Free (self-host); cloud partners vary	No per-token API bill self-hosted; customizable	Quality varies by version; MLOps overhead

Best for Excel, Power BI & Automation — Part 1

Model / Family	Best for Excel, Power BI & Automation
OpenAI – GPT-5	Automating Excel formulas with Office Scripts; writing complex DAX; generating Power Query M code; AI-assisted Power BI data modeling
Claude Opus 4.1	Explaining complex datasets; step-by-step Power BI report logic; writing automation documentation; validating calculation accuracy
Gemini 2.5	Integrating Google Sheets with BI tools; drafting automation flows that sync with BigQuery; building Google Workspace-based reporting
Llama 4	Offline Excel/Power BI code generation; custom automation scripts for sensitive data; local ETL workflows

LLM Models Comparison (2025) — Part 2

Model / Family	Popularity	Quality & Strengths	Common Tasks	Pricing (API / Access)	Pros	Cons
Mistral – Large 2 & open models	★★★☆☆ Growing dev adoption	Lean, efficient, multilingual	Chatbots, automation, multilingual tasks	Competitive API rates; batch API = ~50% off	Low-cost; EU-friendly; batch savings	Fewer tools than big 3; reasoning slightly lower
Cohere – Command R / R+	★★★☆☆ Enterprise niche	RAG/search-optimized; structured outputs	Retrieval QA, call-center AI	R cheap; R+ premium tier	Great for RAG; clean enterprise pricing	Less consumer buzz; not for creative tasks
xAI – Grok (3/4)	★★★☆☆ Social media tie-in	Real-time web/cultural context	Live news, trending topics	X Premium/Premium+	Real-time awareness; casual tone	Inconsistent deep reasoning
AWS – Titan Text (Premier/Express)	★★☆☆☆ AWS-first	Bedrock-native, governance	Enterprise chat, AWS-integrated agents	AWS Bedrock pricing	Governance, AWS integration	Not SOTA quality; English-first focus

Best for Excel, Power BI & Automation — Part 2

Model / Family	Best for Excel, Power BI & Automation
Mistral	Creating multilingual Excel dashboards; summarizing Power BI reports in multiple languages; affordable automation prototyping
Cohere	Creating AI-driven knowledge bases for Excel templates; integrating document retrieval in Power BI; FAQ automation
Grok	Pulling latest market/industry data for dashboards; generating live commentary for Power BI storytelling
AWS Titan	Automating AWS-hosted datasets into Power BI; building secure enterprise reporting pipelines; integration with AWS analytics services

How to choose (decision rules)

1. If quality at all costs: Pick GPT‑5; fall back to Claude 4.1 for conservative/safety‑sensitive writing and analysis.

2. If you live in Google Workspace / Vertex: Gemini 2.5 (Flash for price, Pro for depth).

3. If data can’t leave your walls or you want custom fine‑tunes: Llama 4 (self‑host) or Mistral open on your infra.

4. If heavy RAG with clear cost controls: Cohere Command R/R+. 

5. If AWS‑first with Bedrock governance: Titan Text (or run Anthropic/Cohere via Bedrock).

Real‑world pricing tips

• Model mix wins: Route easy tasks (formatting, extraction) to cheap tiers (Gemini Flash, GPT‑5‑nano/mini, Mistral Small), reserve GPT‑5/Opus for “hard” prompts.

• Exploit batch/caching: Mistral Batch API (–50% cost) and Gemini context caching can slash bills.

• Watch output tokens: The expensive side is often output, not input—especially on GPT‑5 and Gemini Pro. Trim verbosity with system prompts.

A note on “popularity = safety”

Surveys show 81%+ of devs use GPT family, with Claude and Gemini also common. That doesn’t mean they never fail—teams still report accuracy and reliability concerns, so implement validation (tests, evals, guardrails) regardless of model.

Final take

If you need one default today: GPT‑5 for hardest jobs; Claude 4.1 for careful, long‑form work; Gemini 2.5 Flash/Pro when you want price‑performance or Google integration; Llama/Mistral when you need control, customization, or to own the infra. Add Cohere for RAG‑heavy use, Titan for Bedrock governance, and Grok if live web context matters.