Concept card

Foundation Model Selection — GPT-4o vs Claude vs Gemini vs Llama

As of 2026-07-24T14:00:00Z

Edited by Cédric-Olivier Mâry, sole editor and publication director — 27 years in the SAP ecosystem. How these pages are produced, and where automation is used.

What is Foundation Model Selection — GPT-4o vs Claude vs Gemini vs Llama?

Generic leaderboards (HumanEval, MMLU) don't predict SAP task performance — the concrete finding is dimension-specific: Gemini 2.0 Pro's 1M-token window wins for reading a full Datasphere metadata export, while GPT-4o and Claude's 128K/200K windows lag on that exact task.

Foundation model selection is the recurring architectural decision every SAP AI initiative eventually faces: which large language model — GPT-4o and the GPT-5 family from OpenAI, Claude from Anthropic, Gemini from Google, or an open-weight model such as Llama or Mistral — should sit behind a given Joule extension, BTP AI API integration, or custom analytics feature. It matters because the decision is expensive to reverse: prompt engineering, evaluation harnesses, fine-tuning data, and cost models are all built around a specific model's behavior, and swapping providers later means re-validating all of it against a new baseline.

The mistake most enterprise architects make is treating this as a leaderboard lookup. General benchmarks — coding tests, broad knowledge quizzes, reasoning puzzles — measure capabilities that correlate weakly with the tasks SAP analytics work actually needs: extracting typed fields from a semi-structured S/4HANA BAPI response without inventing values that were not present, generating ABAP or SQLScript that respects SAP naming and performance conventions, reasoning correctly over a Datasphere Analytic Model's metadata, or drafting ESRS-aligned narrative from structured ESG figures. A model that tops a general leaderboard can still underperform on these narrow, high-stakes extraction and generation tasks, and the only reliable way to know is to run task-specific evaluation against real SAP data before committing.

How to Decide: The Four Dimensions That Actually Matter

Why it matters

Structured-data extraction from S/4 BAPI JSON/XML favors GPT-4o and Claude 3.7 Sonnet per a Q1 2026 operator panel (n=18 practice leads), not vendor leaderboards
Only Llama 3 on SAP AI Core's EU-region deployment fully satisfies Art. 44 GDPR for high-sensitivity personal data — GPT-4o and Gemini EU endpoints cover standard data only
Code generation splits by task: GPT-4o leads ABAP generation, Claude leads long-context SQL reasoning, Llama 3 70B is competitive for SQLScript once fine-tuned

Key points

Generic benchmarks (MMLU, HumanEval) do not predict SAP-specific task performance — run your own eval on ABAP generation, S/4 extraction, and Datasphere metadata tasks.
GPT-4o: best for ABAP generation and S/4 structured-data extraction; deepest SAP fine-tuning via Microsoft partnership.
Claude 3.7 Sonnet: best for long-context SQL reasoning and nuanced instruction-following.
Gemini 2.0 Pro: 1M-token context window — clear leader for full InfoProvider catalogue or large CSRD disclosure analysis.
Llama 3 70B on SAP AI Core EU: best for EU data residency compliance and cost-at-scale; requires fine-tuning for SAP-specific tasks.
Model selection is a 3-5 year architectural commitment; migration on underperformance costs 6-12 months.
Foundation Model Selection — GPT-4o vs Claude vs Gemini vs Llama is mastered only when it changes a named buyer decision.
Start with the semantic contract and control model before demonstrating the tool.
Use current SAP, analyst, study, KG, and news signals as evidence, not decoration.
Separate verified facts from directional trends and modeled assumptions.

Terms used on this page

Foundation model: Large pre-trained model (LLM or multimodal) used as a general-purpose base for downstream enterprise tasks via fine-tuning or prompting.
GPT-4o: OpenAI's multimodal flagship model (2024); available via Azure OpenAI Service with EU-region endpoints.
Claude 3.7 Sonnet: Anthropic's flagship reasoning model (2025); available via AWS Bedrock and Azure AI Foundry.
Gemini 2.0 Pro: Google's flagship model with 1M-token context window (2025); available via Vertex AI with EU-region deployments.
Llama 3: Meta's open-weight LLM family (2024); self-hostable on EU infrastructure or available via SAP AI Core model catalogue.
SAP BTP AI API: SAP's managed API gateway for foundation model access on Business Technology Platform — provides unified endpoint for multiple models via SAP AI Core.
Decision owner: The accountable person who accepts the trade-off and funds the next action.
Semantic contract: The shared definition of business terms, metrics, entities, and access rules used by tools and teams.

Sources

Full card available to members. What the full card adds: the full decision framework · the SAP vs Snowflake / Databricks / Fabric comparison · the common pitfalls and their fix · the cheat sheet · the architecture schemas · the code blocks · the facts worth quoting.

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