2025 Comparison: Gemini vs GPT-4o vs Mistral Large

Tech Lead, CTO AXI Technologies

Romain DE LA SOUCHÈRE

Published on 22 mai 2025 · 10 min of reading

Table of content

Testing Methodology

Performance Results

Licenses & Business Models

Decision-Making Criteria for Enterprises

Recommended Use Cases

Limitations & Updates

Conclusion

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The wave of generative AI models is accelerating: Gemini (Google), GPT-4o (OpenAI), and Mistral Large (Mistral AI) are now at the heart of companies' innovation strategies. But between marketing promises and ground reality, which model should you choose for your use cases? This 2025 comparison, decision-oriented, relies on benchmark references (MMLU, MT-Bench…) and licensing conditions to guide CTOs, IT Directors, and MLOps in their choices.

Testing Methodology

The compared performances are based on:

Datasets: MMLU (Massive Multitask Language Understanding), HumanEval (code generation), MT-Bench (multimodality), GSM8K (mathematics).
Parameters: 5-shot (MMLU), 0-shot (HumanEval), 10-shot (HellaSwag).
Environment: Execution on GPU A100 80GB (cloud), official API, maximum allowed context.
Versions: GPT-4o (May/August 2024), Gemini 2.5 Pro (April 2025), Mistral Large 2.1 (February 2025).

Sources: LLM Leaderboard, DocsBot, Mistral AI.

Performance Results

Comparison Table (Key Benchmarks)

Model	MMLU (%)	HumanEval (%)	MT-Bench/MMMU (%)	Context Window	Price Input/Output (1M tokens)
GPT-4o	88.7	90.2	69.1 (MMMU)	128,000	$5 / $15
Gemini 2.5	86.0	85.0*	67.5*	1,000,000	$0.10 / $0.40
Mistral L.	81.2	92.0	65.0*	32,000-128,000	$8 / $8

* Estimates or unpublished results on all benchmarks.

Interpretation:

-> GPT-4o remains the leader in general understanding (MMLU) and multimodal versatility.

-> Mistral Large surpasses GPT-4o in code generation (HumanEval: 92% vs 90.2%).

-> Gemini 2.5 Pro impresses with its context window (1M tokens) and ultra-competitive cost ($0.10/$0.40 per million tokens).

Licenses & Business Models

Model	License/Usage	On-prem/Cloud EU	Open Source	API Price (input/output)
GPT-4o	Proprietary (OpenAI)	No/US/EU cloud	No	$5 / $15 (1M tokens)
Gemini 2.5	Proprietary (Google)	No/US/EU cloud	No	$0.10 / $0.40 (1M tokens)
Mistral L.	Commercial/Research	Yes/EU Cloud	Yes (under conditions)	$8 / $8 (1M tokens)

GPT-4o: API, SaaS, US/EU hosting, no open source, restrictive license.
Gemini 2.5: Google Cloud API, Workspace integration, no on-prem, proprietary license.
Mistral Large: API, EU cloud (Azure, GCP, AWS), on-premises options, commercial or research license, open source offering on previous versions.

Decision-Making Criteria for Enterprises

Performance: Prefer GPT-4o for versatility, Mistral Large for code, Gemini for managing large contexts.
Cost: Gemini 2.5 Pro is unbeatable for massive usage or low-cost prototyping.
Compliance: Mistral Large is the only one offering native EU hosting and an open source license suited for sovereignty.
Support: OpenAI and Google provide premium support, Mistral stands out for its proximity and adaptability for European clients.
Ecosystem: GPT-4o (plugins, integrations), Gemini (Google Workspace), Mistral (multi-cloud API, open source).

If you are still unsure or need specific support, our team is here to help

Recommended Use Cases

GPT-4o: versatile assistants, customer support automation, generation of complex reports, multilingual conversational agents.
Gemini 2.5 Pro: processing large documents, Google Workspace integration, multimodal analysis (text, image, audio, video).
Mistral Large: code generation, GDPR compliance, on-premises integration, processing sensitive data, projects requiring a large context window (up to 128K tokens on certain versions).

👉 To go further: Which AI model to choose in 2025? Expert guide & comparison.

Limitations & Updates

Benchmarks: Scores vary according to version, prompt, and language. Benchmarks do not always reflect performance in real production environments.
Licenses: Conditions are evolving rapidly, particularly regarding open source and on-prem access.
Updates: Models are frequently updated (GPT-4o: May/August 2024, Mistral Large: Nov. 2024/Feb. 2025, Gemini: April 2025), monitor releases to stay updated.

Conclusion

Gemini vs GPT-4o: GPT-4o dominates in general understanding and multimodality, Gemini 2.5 Pro impresses with its cost and context management, Mistral Large excels in code, compliance, and sovereign integration. The choice must align with your priorities: performance, cost, compliance, support, and ecosystem.

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Romain DE LA SOUCHÈRE

Tech Lead, CTO AXI Technologies

Expert Data Engineering et Cloud, Romain affiche plus de 11 ans d'expérience, dont plusieurs années comme Lead Developer sur des solutions Smart Building haute performance. Il y a conçu et mis en production des moteurs de traitement capables d'absorber des centaines de milliers de données de capteurs par minute, ainsi que des bases clusterisées gérant plus de 10 millions de données dynamiques. Certifié Microsoft Azure DevOps Engineer Expert, il maîtrise aussi bien le développement back-end (Python, C#) que le DevOps (Docker, Kubernetes, Terraform) et les agents LLM. Formateur en Python, cloud, DevOps et IA générative appliquée, il forme avec une obsession : Amener chaque apprenant à concevoir et déployer des architectures réellement scalables en production.

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