Benchmarking Open-Source vs Closed LLMs: A Comprehensive Guide

The landscape of Large Language Models (LLMs) has evolved dramatically, creating a fundamental choice for organizations and developers: should you build your AI applications on open-source or closed (proprietary) LLMs? This decision impacts everything from performance and cost to customization capabilities and privacy considerations.

As AI becomes increasingly central to business operations across industries, understanding the benchmarks and tradeoffs between these two approaches is crucial for making informed decisions. Whether you’re a content creator looking to enhance your workflow, an educator building interactive learning tools, or a healthcare professional developing patient support systems, the LLM foundation you choose will significantly influence your AI application’s capabilities.

In this comprehensive guide, we’ll dive deep into benchmarking open-source versus closed LLMs across multiple dimensions. We’ll explore their respective strengths and limitations, provide data-driven comparisons, and offer a framework to help you determine which approach best suits your specific needs—all while showing how platforms like Estha enable you to leverage either type without technical expertise.

Understanding Open-Source and Closed LLMs

Before diving into benchmarking, it’s essential to clearly understand what defines open-source and closed LLMs and the fundamental differences between them.

Open-Source LLMs: Definition and Examples

Open-source LLMs are machine learning models whose code, weights, and architecture are publicly available. This transparency allows developers to view, modify, and distribute the model according to the terms of their licenses. Some prominent examples include:

Llama 2 and Llama 3 (Meta): Powerful models available for research and commercial use with various parameter sizes (7B, 13B, 70B). The Llama family represents some of the most capable open-source models available today.

Mistral and Mixtral: French-developed models known for impressive performance despite smaller parameter counts. Mixtral’s mixture-of-experts architecture delivers performance that rivals much larger models.

Falcon (Technology Innovation Institute): A family of models trained on massive datasets with different parameter sizes (7B, 40B, 180B).

Open-source models are typically downloaded and run locally or on your own infrastructure, giving you complete control over their deployment and usage.

Closed LLMs: Definition and Examples

Closed or proprietary LLMs are models where the underlying code, weights, and training methodologies remain private. These models are accessible only through APIs or specific platforms controlled by their creators. Notable examples include:

GPT-4/GPT-4o (OpenAI): The current industry leader in many performance benchmarks, available exclusively through OpenAI’s API.

Claude 3 (Anthropic): A family of models (Haiku, Sonnet, and Opus) known for thoughtful responses and reasoning capabilities.

Gemini (Google): Google’s advanced model available through Google Cloud’s Vertex AI and direct APIs.

With closed models, you’re essentially renting access to the provider’s infrastructure and expertise rather than running the model yourself.

Key Benchmarking Criteria for LLMs

When comparing open-source and closed LLMs, several key criteria provide the foundation for meaningful benchmarking:

Performance Metrics

Performance benchmarking for LLMs extends beyond simple accuracy measures to include:

MMLU (Massive Multitask Language Understanding): Measures knowledge across 57 subjects including mathematics, history, law, and more. This benchmark tests the breadth of model knowledge.

HELM (Holistic Evaluation of Language Models): A comprehensive framework that evaluates models across multiple dimensions including accuracy, calibration, robustness, and fairness.

GSM8K and MATH: Mathematical reasoning benchmarks that test the model’s ability to solve multi-step math problems.

TruthfulQA: Measures a model’s ability to avoid generating false information when responding to questions.

Reasoning benchmarks: Tests like the Big-Bench Hard subset and MTEB evaluate the model’s logical reasoning capabilities.

Operational Considerations

Beyond raw performance, practical operational factors play a crucial role in benchmarking:

Inference speed: How quickly the model generates responses, measured in tokens per second.

Resource requirements: The computational resources (GPU memory, VRAM) needed to run the model effectively.

Cost structure: The financial implications of deployment, including API costs for closed models or infrastructure costs for open-source models.

Reliability and uptime: Service stability, particularly important for closed models that depend on third-party APIs.

Customization and Control

Fine-tuning capabilities: The ability to adapt models to specific domains or tasks.

Architectural modification: Whether the model’s architecture can be altered for specific requirements.

Integration flexibility: Ease of incorporating the model into existing systems and workflows.

Deployment options: On-premises, cloud, or hybrid deployment possibilities.

Performance Comparison: Open-Source vs Closed LLMs

When comparing performance, we need to look at both standardized benchmarks and real-world performance characteristics.

Benchmark Results

Current benchmark data reveals several important patterns:

General knowledge and reasoning: Closed models like GPT-4 and Claude 3 Opus consistently outperform open-source alternatives on comprehensive benchmarks like MMLU and HELM. GPT-4 scores approximately 86.4% on MMLU versus Llama 3 70B’s 78.5%.

Mathematical reasoning: The gap narrows in mathematical tasks, with models like Llama 3 70B scoring within 5-10 percentage points of GPT-4 on GSM8K benchmark.

Coding abilities: Closed models maintain a performance edge in code generation and understanding tasks, though recent open-source models like DeepSeek Coder have significantly narrowed this gap.

Multilingual capabilities: Proprietary models typically offer stronger performance across non-English languages, though this gap is closing with newer open-source releases.

Context Window Comparison

The context window—how much text a model can process at once—varies significantly:

Closed LLMs: Models like Claude 3 Opus (200K tokens) and GPT-4 Turbo (128K tokens) offer expansive context windows, enabling them to process entire documents or conversations at once.

Open-source LLMs: Many open-source models have traditionally had smaller context windows (typically 4K-8K tokens), though newer models like Llama 3 70B (8K tokens) and specialized models like Claude-to-Llama (100K tokens) are closing this gap.

The practical implication is that closed models currently offer superior performance for tasks requiring long-context understanding, such as document analysis or summarization of lengthy content.

Inference Speed and Latency

Speed comparisons reveal interesting tradeoffs:

Closed LLMs: Offer optimized infrastructure that typically delivers low latency for initial responses (250-500ms for first token), but generation speed is often throttled to manage resources across all users.

Open-source LLMs: When properly deployed on dedicated hardware, can achieve faster generation speeds without throttling, especially when using optimization techniques like vLLM or TensorRT-LLM. Smaller open-source models can generate 30-100+ tokens per second on consumer-grade hardware.

For applications requiring real-time interaction or processing large volumes of text, properly deployed open-source models often provide speed advantages despite their generally lower performance on academic benchmarks.

Cost and Resource Analysis

The financial implications of choosing between open-source and closed LLMs extend beyond simple API pricing.

Closed LLM Pricing Models

Proprietary LLMs typically follow consumption-based pricing:

Token-based billing: Charges accumulate based on the number of tokens processed, with separate rates for input (prompts) and output (generations). For example, GPT-4 costs approximately $0.01-$0.03 per 1K tokens, while Claude 3 Sonnet costs around $0.003-$0.015 per 1K tokens.

Volume discounts: Many providers offer reduced rates for high-volume customers, though these often require contractual commitments.

Feature premiums: Additional capabilities like longer context windows or higher rate limits often come with premium pricing.

The predictability of these costs makes budgeting straightforward for steady usage patterns, but costs can escalate quickly for high-volume applications.

Open-Source LLM Infrastructure Costs

Open-source models shift costs from API consumption to infrastructure:

Hardware requirements: Running state-of-the-art open-source models requires significant GPU resources. For example, Llama 3 70B needs at least 140GB of GPU memory for efficient inference.

Cloud computing costs: Using cloud GPU instances (like NVIDIA A100 or H100) can cost $4-20 per hour depending on the provider and instance type.

Operational overhead: Managing model deployments requires DevOps expertise and ongoing maintenance, adding indirect costs.

For high-volume applications, open-source models typically become more cost-effective after reaching certain usage thresholds, despite the higher upfront and operational costs.

Cost-Efficiency Crossover Point

Analysis shows that the cost-efficiency crossover point—where open-source becomes cheaper than closed models—typically occurs at different thresholds depending on the model size and usage pattern:

For smaller models (7B-13B parameters): The crossover often happens at relatively low volumes, making open-source deployment cost-effective even for medium-scale applications.

For larger models (70B+ parameters): The substantial infrastructure requirements mean the crossover occurs at higher usage volumes, making closed APIs more economical for many use cases.

This analysis excludes development costs, which can be substantial when building expertise to optimize open-source model deployments.

Customization and Fine-tuning Capabilities

The ability to adapt models to specific domains and tasks represents one of the most significant differentiators between open-source and closed approaches.

Open-Source Customization Depth

Open-source models offer unparalleled customization options:

Full fine-tuning: Complete retraining of model weights on domain-specific data to optimize performance for particular tasks or industries.

Architectural modifications: The ability to alter model architecture, attention mechanisms, or embedding approaches to address specific requirements.

Quantization control: Precision in determining how models are compressed (4-bit, 8-bit, etc.) to balance performance and resource usage.

Integration flexibility: Direct integration with any system or pipeline without intermediary APIs.

These capabilities enable highly specialized implementations but require substantial machine learning expertise to execute effectively.

Closed LLM Adaptation Options

Proprietary models offer more constrained but increasingly powerful customization:

Fine-tuning APIs: Services like OpenAI’s fine-tuning or Anthropic’s Claude fine-tuning allow limited model adaptation within the provider’s ecosystem.

Retrieval Augmented Generation (RAG): Enhancing models with external knowledge bases without modifying the underlying model.

Prompt engineering: Sophisticated prompting techniques to guide model behavior without changing weights.

Custom models: Some providers offer enterprise-level custom model development, though at premium price points.

While more limited than open-source options, these approaches offer practical customization without the technical overhead of managing model infrastructure.

Privacy and Data Security Considerations

Data privacy represents a critical concern when benchmarking LLM approaches, particularly for applications handling sensitive information.

Data Handling in Closed LLMs

Closed LLM providers have varying approaches to data privacy:

Data usage policies: Most major providers have shifted toward not using customer inputs for training by default, though policies vary by provider and subscription tier.

Enterprise offerings: Services like Azure OpenAI or Anthropic’s enterprise tier provide enhanced privacy guarantees and dedicated infrastructure.

Residency challenges: Data residency requirements in regulated industries may conflict with the distributed nature of closed API infrastructure.

Vendor lock-in: Dependency on a single provider’s privacy practices creates potential risk exposure.

These considerations make closed LLMs potentially challenging for highly regulated industries or applications handling sensitive personal data.

Open-Source Privacy Advantages

Open-source deployment offers significant privacy benefits:

Complete data isolation: When deployed on private infrastructure, data never leaves your environment.

Auditable code: The ability to inspect model code and weights for potential vulnerabilities or biases.

Compliance flexibility: Deployments can be configured to meet specific regulatory requirements across jurisdictions.

Air-gapped operation: Models can run in completely isolated environments with no external network connections.

For healthcare, financial services, government, or other privacy-sensitive applications, these advantages often outweigh the performance benefits of closed models.

Use Case Scenarios and Decision Framework

Different scenarios favor different approaches based on their specific requirements and constraints.

When to Choose Closed LLMs

Closed models typically offer advantages in these scenarios:

Performance-critical applications: When state-of-the-art capabilities are essential and outweigh other considerations.

Rapid development needs: Projects with tight timelines that benefit from immediate API integration without infrastructure setup.

Low to medium volume usage: Applications where usage volume falls below the cost-efficiency crossover point.

Specialized capabilities: Use cases requiring specific features like advanced vision-language processing that aren’t yet available in open-source alternatives.

Teams lacking ML expertise: Organizations without the internal capability to manage complex model deployments.

When to Choose Open-Source LLMs

Open-source approaches shine in these contexts:

High privacy requirements: Applications handling sensitive data that cannot be shared with third-party APIs.

High volume applications: Use cases where API costs would become prohibitive at scale.

Specialized domain adaptation: Applications requiring deep customization to specific industries or knowledge domains.

Offline or edge deployment: Scenarios requiring operation without reliable internet connectivity.

Long-term strategic investment: Organizations building long-term AI capabilities who want to avoid vendor dependency.

Hybrid Approaches

Many organizations are finding value in hybrid strategies:

Tiered implementation: Using closed models for high-value, complex tasks and open-source models for simpler, high-volume operations.

Development-to-production pipeline: Prototyping with closed APIs before transitioning to optimized open-source deployment for production.

Domain-specific segregation: Deploying open-source models for privacy-sensitive domains while using closed APIs for general capabilities.

This balanced approach often delivers the best combination of performance, cost-efficiency, and risk management.

Implementing Either Approach with Estha

Regardless of whether you choose open-source or closed LLMs for your applications, Estha’s no-code AI platform provides a unified way to build powerful applications without technical barriers.

Building with Closed LLMs on Estha

Estha simplifies closed LLM integration:

API connection: Connect to providers like OpenAI, Anthropic, or Google through simple configuration settings without writing any code.

Prompt management: Design and optimize prompts through an intuitive visual interface rather than through complex programming.

Cost control: Manage API usage and set limits to prevent unexpected costs while maintaining application functionality.

Version management: Easily switch between different closed LLMs or model versions as new capabilities become available.

This approach allows you to leverage the cutting-edge capabilities of closed models without the technical complexity typically associated with API integration.

Leveraging Open-Source LLMs through Estha

For those preferring open-source models, Estha removes traditional barriers:

Simplified deployment: Connect to your existing open-source model deployments through standardized interfaces.

Managed services integration: Easily connect to managed open-source providers that handle the infrastructure complexity for you.

Custom model support: Integrate fine-tuned or specialized open-source models that match your specific domain requirements.

Infrastructure independence: Maintain control over where and how your models run while using Estha’s intuitive interface for application building.

This capability democratizes access to open-source LLM technology, allowing domain experts to build sophisticated applications without diving into the technical complexities of model deployment.

Future-Proofing with Estha’s Flexible Architecture

The LLM landscape continues to evolve rapidly. Estha’s approach provides important advantages:

Model switching: Change underlying models without rebuilding applications as better options emerge.

Multi-model applications: Create applications that leverage different models for different functions based on their respective strengths.

Progressive enhancement: Start with simpler implementations and gradually incorporate more sophisticated capabilities as needs evolve.

This flexibility ensures your AI investment remains valuable as technology advances, regardless of whether open-source or closed models ultimately dominate specific use cases.

Future Outlook and Trends

The competitive landscape between open-source and closed LLMs continues to evolve rapidly.

Narrowing Performance Gap

Recent trends suggest the performance gap is closing:

Accelerating open-source progress: Models like Llama 3, Mixtral, and DeepSeek have dramatically narrowed the capability gap with proprietary leaders.

Specialized excellence: Open-source models are achieving parity or superiority in specific domains while closed models maintain an edge in general capabilities.

Research democratization: Techniques once exclusive to leading labs are being rapidly implemented in the open-source community.

This trend suggests that performance differences may become less decisive in the selection process as both approaches continue to advance.

Regulatory Influences

Emerging regulations will likely impact this landscape:

AI governance requirements: Regulations like the EU AI Act may favor open-source models due to their transparency and auditability.

Data sovereignty concerns: Increasing requirements for local data processing may accelerate open-source adoption in certain jurisdictions.

Competition policy: Regulatory pressure on large AI providers could influence pricing and access policies for closed models.

Organizations should consider these regulatory trends in their long-term AI strategy to avoid costly adjustments later.

Ecosystem Evolution

The broader ecosystem continues to develop in interesting ways:

Specialized providers: The emergence of vendors offering optimized, managed open-source deployments that bridge the gap between traditional approaches.

Hardware acceleration: Advances in specialized AI hardware making larger open-source models more accessible to smaller organizations.

Foundational model diversity: Proliferation of specialized foundation models optimized for specific tasks or domains rather than general-purpose models.

These ecosystem developments may ultimately blur the traditional boundaries between open and closed approaches, creating a more nuanced spectrum of options.

Conclusion

The choice between open-source and closed LLMs represents a multidimensional decision that extends far beyond simple performance benchmarks. While closed models currently maintain a performance edge in most general benchmarks, open-source alternatives offer compelling advantages in customization, privacy, and long-term cost efficiency for many use cases.

Key takeaways from our analysis include:

Performance vs. Control tradeoff: Closed models generally offer superior out-of-the-box performance, while open-source models provide greater control and customization potential.

Volume-based economics: Low-volume applications often favor closed APIs, while high-volume use cases typically benefit from open-source deployment’s economics.

Privacy implications: Privacy-sensitive applications gravitate toward open-source solutions, though enterprise closed API options are improving their privacy guarantees.

Skill requirements: Leveraging open-source models effectively typically requires greater technical expertise, though platforms like Estha are reducing this barrier.

The good news is that with platforms like Estha, the technical implementation barriers for either approach have been dramatically reduced. Domain experts across industries can now build sophisticated AI applications using either open-source or closed LLMs without deep technical expertise, focusing instead on the unique value they bring to their specific field.

As the LLM landscape continues to evolve, maintaining flexibility in your approach will be key to maximizing the value of these powerful technologies while managing associated costs and risks. The best strategy may not be choosing one approach exclusively, but rather developing the organizational capability to leverage both open-source and closed models appropriately based on specific use case requirements.