Startup guide: Leading AI Models for Commercial Use in 2025

One of the first – and biggest – choices for any AI startup is picking the right foundation model.

This article provides a comprehensive comparison of the leading AI models for commercial use in 2025, detailing their technical specifications, performance benchmarks, licensing terms, costs, and integration capabilities. By exploring the strengths and limitations of each model, founders and technical teams can more confidently select an AI foundation that best suits their specific needs.

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Read on to discover how you can leverage the right AI model for your business, then learn how Lagom’s hands-on approach can support your growth journey.

Key Players in the 2025 AI Model Landscape

Several standout AI models dominate the market in 2025, each excelling in particular domains:

• OpenAI’s GPT-4: Highly regarded for superior language understanding and multimodal capabilities.

• Google’s Gemini: Exceptional at long-context reasoning and versatile across text, images, audio, and video.

• Anthropic’s Claude: Renowned for its accuracy in specialised domains and document processing.

• Meta’s Llama 4: Offers open-source flexibility and powerful performance through a mixture-of-experts architecture.

• Mistral Small 3.1: Notable for competitive capabilities, smaller size, and efficient deployment.

Implementation Considerations for Startups

Choosing the right model is a nuanced process, influenced by:

1. Task Complexity: Larger, more capable models like GPT-4 or Gemini 2.5 Pro excel at intricate tasks, but lighter alternatives (Mistral) may suffice for simpler needs.

2. Budget: Open-source models, while free from per-token fees, still require hosting infrastructure.

3. Data Privacy: Self-hostable options (Llama, Mistral) keep sensitive data in-house.

4. Scaling: Bear in mind user thresholds, such as with Llama’s 700M monthly active users limit.

5. Integration: Models aligned with familiar cloud services (AWS, Google Workspace) often yield quicker development cycles.

Technical Specifications and Capabilities

Modern AI models typically build upon transformer-based architectures with proprietary enhancements. While parameter count often correlates with capacity, architectural efficiency is equally crucial.

Model	Architecture	Parameter Count	Context Window	Multimodal	Training Data
GPT-4 (OpenAI)	Transformer with proprietary modifications	Not publicly disclosed	8K-128K tokens	Yes (text, images)	Extensive dataset from books, websites, scientific papers
Gemini 2.5 Pro (Google)	Decoder-only transformer with TPU optimizations	Not publicly disclosed	1M+ tokens	Yes (text, images, audio, video)	Multimodal, multilingual dataset
Claude 3.5 Sonnet (Anthropic)	Not disclosed	Not disclosed	200K tokens	Yes	Not fully disclosed
Llama 4 (Meta)	Mixture of experts	Scout: 109B total (17B active), Maverick: 400B total (17B active)	Scout: 10M tokens, Maverick: 1M tokens	Yes (text, images)	15T tokens, data cutoff August 2024
Mistral Small 3.1	Mixture of experts	Not fully disclosed	128K tokens	Yes	Not fully disclosed

• GPT-4: Notably adept at complex conversations and maintaining context across lengthy interactions. Its multimodal ability enables text-and-image processing, adding versatility for product design and customer support scenarios.

• Gemini 2.5 Pro: Employs a decoder-only transformer optimised for Google’s TPUs. Supports multiple modes (text, images, audio, video) within expansive context windows for more complex real-time analysis.

• Llama 4: Recently overhauled with a mixture-of-experts framework, scaling up to 400B parameters in the Maverick variant.

• Mistral Small 3.1: Despite its relatively compact size, it handles text, images, and extended context windows of up to 128K tokens while delivering high-speed inference.

Performance Benchmarks

Performance metrics help determine which model suits specific tasks, from long-context comprehension to coding and mathematics.

Model	Reasoning	Mathematics	Coding	Multimodal	Long-Context
GPT-4.5	Strong	Strong	86.6% (HumanEval)	Good	48.8% (MRCR 128K)[5]
Gemini 2.5 Pro	18.8% (Humanity's Exam)	92.0% (AIME 2024)	70.4% (LiveCodeBench v5)	81.7% (MMMU)	91.5% (MRCR 128K)[5]
Claude 3.5 Sonnet	High	96.4% (GSM8K)	92.0% (HumanEval)	75% (MMMU)	Not specified[6]
Llama 4 Maverick	Competitive with GPT-4o	Strong	Strong	Strong	Strong[3]
Mistral Small 3.1	Outperforms GPT-4o Mini	Good	Good	Good	Good[4]

• Gemini 2.5 Pro: Excels across multiple domains, notably scoring 18.8% on “Humanity’s Last Exam” without tools, higher than several competitors. Impressively, it achieves 92.0% on AIME 2024 problems and 91.5% on MRCR 128K for long-context tasks.

• Claude 3.5 Sonnet: Outstanding in specialised tasks, with 96.4% on GSM8K (math word problems) and 92.0% on HumanEval coding accuracy, making it a strong contender for both research and development applications.

• Llama 4: Claimed by Meta to surpass GPT-4o on the LMArena AI benchmark through an “experimental chat version,” though these early findings remain somewhat controversial.

• Mistral Small 3.1: Demonstrates remarkable performance given its smaller architecture and offers higher inference speeds than many similarly sized models.

Licensing and Commercial Terms

Licensing terms range from strictly proprietary agreements to fully open-source models. This factor is critical for startups, as it influences potential user thresholds, usage restrictions, and alignment with compliance requirements.

Model	License Type	Commercial Use	User Threshold Limits	Industry Restrictions
GPT-4	Proprietary	Permitted with API access	None specified	Various content restrictions
Gemini	Proprietary	Permitted with API access	None specified	Various content restrictions
Claude	Proprietary	Permitted with API access	None specified	Various content restrictions
Llama 4	Open with conditions	Permitted	700M monthly active users	Military, transportation, heavy machinery
Mistral	Apache 2.0	Fully permitted	None	None

• Llama 4: Issued under an “open with conditions” licence with a 700 million monthly active user threshold, plus restrictions on military, transportation, and heavy machinery applications.

• Mistral Small 3.1: Released under Apache 2.0, making it fully permissive with no additional fees. This open licensing fosters simpler integration in regulated or data-sensitive environments.

Notably, Anthropic’s Claude 3.5 terms assign users “all of our right, title, and interest—if any—in Outputs,” acknowledging potential legal nuances around copyright and human authorship.

Cost and Scalability Considerations

For cost-sensitive startups, the variation in pricing models can heavily influence adoption. Proprietary models often charge per token, while open-source models typically entail only hosting costs.

Model	Input Token Price	Output Token Price	Pricing Model	Notes
GPT-4 (8K context)	$30.00 per 1M tokens	$60.00 per 1M tokens	Pay-per-token
GPT-4 (128K context)	$10.00 per 1M tokens	$30.00 per 1M tokens	Pay-per-token	gpt-4-turbo
Gemini 2.5 Pro (<200K tokens)	$1.25 per 1M tokens	$10.00 per 1M tokens	Pay-per-token
Gemini 2.5 Pro (>200K tokens)	$2.50 per 1M tokens	$15.00 per 1M tokens	Pay-per-token
Claude 3.5 Sonnet	Not specified in search results	Not specified in search results	Not specified
Llama 4	Hosting costs only	Hosting costs only	Self-hosted	Open weights
Mistral Small 3.1	Hosting costs only	Hosting costs only	Self-hosted	Apache 2.0 license

• Gemini 2.5 Pro: At $1.25 per million input tokens for contexts under 200K tokens, rising to $2.50 for longer contexts, it remains relatively affordable compared to some premium offerings.

• GPT-4: Operates on a tiered pricing scheme, charging $30.00 per million tokens for 8K context, with the 128K context version (gpt-4-turbo) costing $10.00 per million prompt tokens.

• Llama 4 and Mistral: Both open-source, so no direct API fees apply. However, self-hosting expenses should be factored in, especially at scale. Mistral Small 3.1’s efficiency lets it run on a single RTX 4090 or a Mac with 32GB RAM, lowering infrastructure costs for early-stage companies.

Subscription Options

In addition to pay-per-token structures, some providers offer subscription packages:

• Gemini: Ranging from a free plan that grants access to certain models, to advanced business and enterprise tiers costing $19.99/month or more.

Fine-Tuning and Integration Capabilities

Customisation opportunities can be vital for certain use cases. Fine-tuning, integration ease, and self-hosting versatility are all key factors:

Model	Fine-tuning Support	Integration Options	Hosting Flexibility
GPT-4	Limited (experimental access)	API, ChatGPT plugins	Cloud only
Gemini	Not specified in search results	Google Workspace	Cloud only
Claude	Not supported for latest models	API, Amazon Bedrock	Cloud only
Llama 4 (405B)	Supported via AWS, Databricks, Dell, NVIDIA	Multiple cloud platforms	Self-hosted or cloud
Mistral	Designed for customization	Multiple deployment options	Self-hosted or cloud

• GPT-4: Provides experimental fine-tuning access via OpenAI’s platform, though it is not universally available yet.[14]

• Claude (Anthropic): Does not currently support fine-tuning for its latest models, according to AWS Bedrock documentation.[15]

• Llama 4: Readily fine-tuned across multiple cloud providers, offering retrieval-augmented generation (RAG) and other advanced features.[16]

• Mistral: Embraces customisation with open-source code that allows deep configuration and integration in diverse environments.[8]

Real-World Applications and Use Cases

Practical implementations of these AI models span a broad spectrum of industries:

1. Natural Language Processing and Generation

• GPT-4 is a leader in content creation and complex dialogue management, ideal for virtual assistants and content platforms.

• Claude 3.5 Haiku is particularly adept at document extraction and labelling.

2. Coding and Development

• Claude 3.5 Sonnet yields a standout 92.0% coding accuracy on HumanEval.

• Mistral integrates effectively in local development environments, thanks to its small footprint and open licence.

3. Customer Support

• Gemini integrates seamlessly with Google Workspace for robust support automation, as demonstrated by Discover Financial’s virtual assistant.

• GPT-4’s multimodality allows handling of text and images, enhancing ticketing systems where customers provide screenshots.

4. Healthcare and Scientific Applications

• Claude excels in science diagram interpretation (94.7%) and medical data analysis, offering advanced clinical research search capabilities.

• Gemini’s extensive context window benefits researchers dealing with large datasets or medical literature reviews.

5. Financial Services

• Mistral’s Apache 2.0 licence is a strong fit for data privacy in the financial sector, enabling fully in-house deployments.

• Llama supports fintech chatbots and streamlined lending services through advanced, multimodal interactions.

Conclusion

The AI model market in 2025 offers an unprecedented breadth of capabilities for startups. From high-end models like GPT-4, Gemini, and Claude—requiring minimal setup yet commanding notable fees—to open-source models such as Llama 4 and Mistral, which exchange hosting overhead for greater flexibility, there is an option to suit nearly every technical and financial requirement.

For startups where performance is paramount and budget less constrained, Gemini 2.5 Pro and Claude 3.5 Sonnet stand out as particularly compelling choices. By contrast, those seeking a balance of cost-effectiveness, control, and customisation may find Llama 4 or Mistral to be the strongest fit. Ultimately, selecting the right AI model hinges on a careful appraisal of your business objectives, data privacy needs, funding environment, and projected product roadmap.

Where You Can Go from Here: Practical Next Steps with Lagom Consulting

Choosing the right AI model marks a critical milestone—yet it’s only the first step toward sustainable, long-term success. At Lagom Consulting, we combine deep industry insight with hands-on support to help AI startups achieve real, measurable growth.

1. Strategic Clarity: We pinpoint the high-impact markets, revenue streams, and expansion opportunities where your startup can thrive.

2. Practical Implementation: Our team works alongside yours, helping to pilot solutions, optimize operations, and scale effectively.

3. Long-Term Focus: We continually refine your strategy in response to changing market dynamics, ensuring you stay ahead of the curve.

If you want a growth partner who provides both strategic guidance and tangible, on-the-ground execution, reach out to Lagom Consulting today. Let’s collaborate to transform your AI ambitions into a thriving, future-proof business.

Who are Lagom Consulting? 

At Lagom Consulting, we pride ourselves on being more than marketing and management consultants; we are your strategic allies in building marketing strategies to market into financial services market.  

Our ethos centres around delivering first-class service, underpinned by a hands-on approach that melds practical problem-solving with time-tested marketing solutions. We recognise that effective marketing is an ongoing journey, not a one-off exercise. We steer clear of ‘random acts of marketing’, opting instead for a comprehensive and sustained approach.  

Working with Lagom Consulting means gaining more than a consultant; it means acquiring a partner committed to your enduring success

References:

1. [1] GPT-4: 12 Features, Pricing & Accessibility in 2025 – https://research.aimultiple.com/gpt4/

2. [2] Gemini (language model) – Wikipedia – https://en.wikipedia.org/wiki/Gemini_(language_model)

3. [3] Llama (language model) – Wikipedia – https://en.wikipedia.org/wiki/Llama_(language_model)

4. [4] Mistral Small 3.1 – https://mistral.ai/news/mistral-small-3-1

5. [5] gemini-2-5-pro – https://www.datacamp.com/blog/gemini-2-5-pro

6. [6] Latest Anthropic (Claude AI) Statistics (2025) | StatsUp – Analyzify – https://analyzify.com/statsup/anthropic

7. [7] What are the problems with using Llama in a commercial app? – https://www.reddit.com/r/MachineLearning/comments/1e9lfu3/d_what_are_the_problems_with_using_llama_in_a/

8. [8] What Is Mistral AI? | Built In – https://builtin.com/articles/mistral-ai

9. [9] Licensing Llama 3.1 for Commercial Use – https://llamaimodel.com/commercial-use/

10. [10] Who Owns Claude’s Outputs and How Can They Be Used? – https://terms.law/2024/08/24/who-owns-claudes-outputs-and-how-can-they-be-used/

11. [11] How much does GPT-4 cost? – OpenAI Help Center – https://help.openai.com/en/articles/7127956-how-much-does-gpt-4-cost

12. [12] Gemini 2.5 Pro is Google’s most expensive AI model yet – TechCrunch – https://techcrunch.com/2025/04/04/gemini-2-5-pro-is-googles-most-expensive-ai-model-yet/

13. [13] Gemini Pricing: Is It Worth It In 2025? [In-Depth Review] – Team-GPT – http://team-gpt.com/blog/gemini-pricing/

14. [14] Fine-Tuning OpenAI’s GPT-4: A Step-by-Step Guide – DataCamp – https://www.datacamp.com/tutorial/fine-tuning-openais-gpt-4-step-by-step-guide

15. [15] Anthropic’s Claude – Models in Amazon Bedrock – AWS – https://aws.amazon.com/bedrock/claude/

16. [16] Meta’s New Llama 3.1 AI Model: Use Cases & Benchmark in 2025 – https://research.aimultiple.com/meta-llama/

17. [17] Real-world gen AI use cases from the world’s leading organizations – https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders

18. [18] GPT-4: API Provider Performance Benchmarking & Price Analysis – https://artificialanalysis.ai/models/gpt-4/providers

19. [19] Meta releases Llama 4, a new crop of flagship AI models – TechCrunch – https://techcrunch.com/2025/04/05/meta-releases-llama-4-a-new-crop-of-flagship-ai-models/

Disclaimer: All information and data presented in this article is accurate as of April 2025. Readers should be aware that model capabilities, licensing terms, and performance benchmarks are subject to change as the AI landscape continues to evolve.