LLM Comparison: Key Concepts & Best Practices

Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling advanced applications in natural language understanding, content generation, and conversational agents. With the number of LLMs continuing to grow, organizations face increasing uncertainty when choosing the right model for their needs. This article explains the process and key factors to consider when comparing LLMs, as well as some best practices for added efficiency.

Summary of key factors to consider in LLM comparison 

What is LLM comparison?

LLM comparison is the systematic process of evaluating multiple large language models against one another to determine which best aligns with an organization’s strategic goals. Beyond mere technical curiosity, this exercise has a direct business impact: selecting the optimal model.

• Reduces operational costs
• Accelerates time-to-market as the LLM quickly delivers what your project needs
• Improves risk management by identifying models that minimize hallucinations or bias.
• Strengthens compliance by surfacing data‑handling differences.
• Increases ROI by aligning model capabilities to revenue‑critical tasks.

In practice, comparing LLMs involves running the same inputs through different APIs or self‑hosted instances, collecting outputs, and scoring them on dimensions such as accuracy, latency, and safety. However, the real value lies in tying those scores back to your business objectives—ensuring that the model you select isn’t just the “best” on a benchmark, but the one that drives the greatest value for your users and your bottom line.

Nexla provides a user-friendly environment for enabling side-by-side comparison of LLM outputs. Engineers can access the platform through its intuitive point-and-click user interface or leverage the Nexla Orchestrated Versatile Agent (NOVA), an agent interface familiar with natural language requests. 

Challenges in LLM comparison

However, the LLM comparison process presents some challenges, ranging from the sheer number of them competing for your attention to the lack of universal benchmarks. The following section details the typical challenges faced while evaluating LLMs.

Large number of LLMs

Many LLMs exist today, ranging from open-source models to closed-source ones. Each of these models has different strengths (e.g., reasoning vs. creativity) and computational requirements​. This sheer variety makes it hard to even know which ones to test.

Unpredictable LLM performance

LLMs perform best at tasks they are tuned for. A model that performs well in one domain may fail in another. In other words, everything should be focused on the use case. For instance, a finance-specialized model may outperform a general-purpose model on regulatory documents, even if its raw benchmark score is lower. 

Lack of output consistency

LLM outputs can be non-deterministic and vary with prompt phrasing, random seeds, or subtle context changes​. This inconsistency means one-off comparisons can be misleading. Teams often have to run multiple tests or use statistical measures of consistency to get a reliable comparison. 

Lack of universal benchmarks

No metric can be designed to cover the full spectrum of characteristics that define LLM performance. While standardized measures like BLEU and ROUGE are commonly used for tasks such as summarization or machine translation, they are based on exact word-overlap similarity, which is inappropriate for evaluating LLMs. 

LLMs can generate semantically equivalent responses in differing vocabulary, wording, or structure, yielding unrealistically low scores despite reasonable output. The majority of the key dimensions of assessment, like factual accuracy, coherence, bias, and safety, require a nuanced, subjective determination that can’t be provided by automated measures alone. 

Costs add up

Larger models require more GPU time for each test, and if testing many models across thousands of prompts, the compute cost adds up. This is especially true when models have large context windows or require a large number of generated tokens. Thus, enterprises must budget resources just to compare models.

Factors to consider while comparing LLMs

When evaluating LLMs for enterprise use, one must consider multiple dimensions of performance and integration. 

Factors to consider while comparing LLMs

Use case compatibility

Some LLMs are trained on broad internet data for general purposes. In contrast, others are further fine-tuned for domain-specific tasks, e.g, fine-tuned on legal, medical, or corporate data through supervised fine-tuning and reinforcement learning from human feedback (RLHF)​. A general model like GPT-4 may have broad knowledge, but a domain-tuned LLM can outperform it on specialized tasks. For example, Llama2 or BloombergGPT (trained on financial texts) answered finance queries more accurately than a general model.

The table below presents examples of real-world use cases and compatible top models 

LLM effectiveness

Key things to remember when analysing the effectiveness of LLM output include:

Relevance

LLM output may be correct but irrelevant if it is not aligned with the user’s intent. Check for relevance by experimentation with varied prompts or human relevance annotation.

Hallucinations

Metrics like the Hallucination Index measure the degree to which models fabricate information in the output​. You must cross-validate outputs against ground truth, especially for fact-based questions.

Bias

Both general and fine-tuned LLMs can produce biased and offensive responses because they are trained on unchecked or imbalanced data. Use fairness benchmarks and toxicity filters. Test your LLMs on demographic test sets or use bias-detection tools like DeepEval or LLM Guard.

Cost

LLM cost comes in several forms:

Inference cost

Most cloud APIs charge per token (input + output). This means longer prompts and responses directly increase your bill. According to one analysis, using GPT-4 can be significantly more expensive than a similar open-source model for tasks such as summarization. Keep in mind that prices change rapidly; newer models often drop costs each year.

Hardware cost

Running LLMs on your GPUs has a high upfront cost. A powerful server with many GPUs can cost tens of thousands of dollars. Operational expenses, such as electricity and cooling, add up too. However, if you have stable, high-volume usage, on-premises can be more cost-effective in the long term. A Dell study found that on-premises infrastructures could be 1.6–4 times more cost-effective than cloud IaaS for large-scale LLM inference.

Model fine-tuning

If you plan to fine-tune the model on your data, factor in training costs, which can be comparable to serving costs. Large models require more computing power to retrain; this can easily cost in the low tens of thousands of dollars for a single pass.

Context length impact

Longer context windows also raise the cost. Every additional token in the input or output requires computational cost. The more input and output you have, the more you pay. Using retrieval (RAG) can help by keeping inputs shorter.

In practice, compare the total cost of ownership. For token-based pricing, estimate costs with your expected usage. For self-hosting, calculate hardware amortization. Often, a mixed strategy works: prototype on a cloud API to control initial expenses, then, if usage is high, move to a self-hosted setup to reduce marginal costs.

The table below shows the inference cost of some top LLMs 

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Response times

The speed of generating output is critical in real-time applications. Some of the factors affecting the response time for LLMs include:

Model size

Larger models require more time to generate each token. Research shows that doubling model parameters does not halve the speed, but larger LLMs are notably slower. According to Databricks, a 30B model was approximately 2.5 times slower per token than a 7B model on the same hardware. In other words, a smaller model or a distilled version may be preferable if latency is a critical concern.

Batching & concurrency

If you serve many users concurrently, you can trade per-query speed for throughput by batching requests. However, large batch sizes can increase the average latency per user. Tune batch sizes according to your workload (the model’s throughput often improves with batching, but time per response can rise).

API rate limits

Cloud providers impose rate limits (requests per minute and tokens per minute). For example, OpenAI enforces token-per-minute quotas on each model. If you hit these limits, your service will slow down. Always check the specific rate limits and plan for retries or back-offs.

Geographic latency

If using a cloud API, the physical distance to the server can add network delay. Some providers (like Azure OpenAI) allow region selection. For global applications, consider multi-region deployments or on-prem solutions closer to users.

Context window

The context window is the size of the tokens the model can process at once. A larger context window enables the model to utilize more context (e.g., entire documents) in a single pass, which can enhance answers in long-document tasks. However, the computation cost grows quadratically with sequence length​, because attention must consider all token pairs. In practice, doubling the context often results in more than a doubling of inference time. Additionally, very large context LLMs require more GPU RAM. If on-premises, ensure that the hardware can handle the maximum context of your chosen model.

Compliance adherence 

There are various regulations, such as GDPR, HIPAA, and CCPA/CPRA, that establish guidelines on how LLMs can use personal and sensitive data. Choosing between cloud services and on-premise options requires careful consideration of where the data will be stored. It is also crucial to closely examine LLM provider policies regarding data usage. This includes how they train their models and what agreements they have in place for processing data or providing reports on how data is handled.

Deployment mechanism

Many cutting-edge LLMs (like OpenAI’s GPT series, Anthropic Claude, Google PaLM) are only available via cloud APIs. This simplifies setup, but it means that all data goes through the vendor’s systems. On-premises or private-cloud deployment, using an open-source model or a cloud vendor’s managed offering, provides more control over data and security. 

On-prem solutions offer higher control, security, and cost advantages at scale, while cloud solutions provide faster startup and lower initial investment. If data privacy or IP protection is vital, open-source LLMs (such as Llama, Mistral, and Falcon) can be hosted on your own infrastructure. This requires GPU servers but avoids vendor lock-in. Some businesses use hybrid approaches (fine-tuning an open model on-premises, then deploying via a private API).

When it comes to scaling, self-hosted (open-source LLMs) allow you to scale hardware as needed, but building that pipeline requires effort. Cloud APIs typically guarantee uptime and can handle large loads with adequate subscription tiers, but may have rate limits. Ensure the deployment option meets your expected query volume and availability requirements.

LLM benchmark data sets

General LLM benchmarks serve as a good starting point for a model’s comprehension. However, they are insufficient to provide a comprehensive assessment for a single real-world application. This deficiency has led to the creation of use-case-specific benchmark datasets. 

These tailored datasets enable a fine-grained assessment of LLM performance against the precise needs and expectations of an individual application. For example, Evidently AI provides 200 publicly available domain-specific LLM benchmarks and evaluation datasets. 

Custom gold reference datasets are higher-quality, carefully hand-labeled datasets specifically designed for testing LLM performance. Serving as the “ground truth,” these datasets are used as a benchmark against which the accuracy and correctness of LLM outputs can be rigorously tested.

Evaluation with use-case specific and custom gold reference test datasets focuses on the specific inputs, tasks, and scenarios of the intended application, providing a more accurate and meaningful assessment of the LLM’s real-world performance.

Advantages

Advantages include:

• Identification of domain-specific strengths and weaknesses — Datasets can be designed to probe specific areas relevant to the use case, revealing granular insights into where the LLM excels and where it needs improvement within a particular domain.
• Subjectivity and nuance — Datasets allow for context-aware judgments by human experts, aligning with desired outcomes, ethical standards, and safety requirements. 
• Evaluation-driven development (EDD) — Datasets provide clear benchmarks for measuring the impact of changes and improvements, guiding iterative refinement of the LLM. 
• Better alignment with business goals and user expectations — Datasets enable direct performance measurement on metrics that are most important for achieving business objectives and meeting user needs.

Limitations

Over‑fitting to benchmarks is rampant. Once a dataset is published, it often becomes part of subsequent model training corpora, enabling models to memorize or exploit statistical quirks rather than genuinely improve underlying capabilities. This effect means that high benchmark scores can be misleading, as models learn to excel on the test rather than the task as a whole.

The relevance of benchmark tasks to real-world applications is frequently weak. Many benchmarks lack rigorous justification for why their specific prompts accurately reflect practical needs, and users have reported that questions in standard suites (such as MMLU) often feel trivial or off-topic compared to domain-specific requirements.

Data contamination and quality control pose systemic risks. Static benchmark datasets may be inadvertently copied into training data, and analyses show that benchmarks suffer from mislabeling vague questions and narrow coverage, which fail to sample the full diversity of real-world inputs.

While newer benchmarks attempt dynamic question generation or domain‑expert questions, they still struggle with verifying that correct answers reflect true reasoning rather than superficial pattern-matching

No-code LLM comparison

No-code LLM comparison tools can be broadly classified into the following categories

Vendor-specific consoles 

Vendor-specific consoles (e.g., AWS Bedrock, Azure OpenAI Studio, Hugging Face Eval) make it easy to test models within a single ecosystem, offering built-in metrics dashboards and simple UI prompts. However, they lock you into that vendor’s models and metrics, making cross-vendor comparisons cumbersome or impossible. You can’t, for example, run a GPT-4 vs. Claude side-by-side without stitching together two separate interfaces and manually reconciling results.

Open-source frameworks 

Open-source frameworks (LangChain, LlamaIndex, OpenAI Evals, Giskard) give you flexibility to connect to nearly any LLM API and define custom tests. However, they still require significant coding tasks, such as assembling pipelines, writing adapter logic, and building dashboards from scratch. Engineering teams frequently spend weeks gluing together evaluation scripts, data ingestion, and metric collection before they can even start comparing outputs.

Low-code platforms

Low-code “universal” platforms (Cohere Command, Weaviate Hub) offer drag-and-drop workflows for RAG pipelines or model callers, but often focus solely on retrieval or embeddings, rather than full LLM evaluation. Their metrics tend to be limited to token usage and basic latency/throughput statistics, with little support for advanced quality checks (bias, factuality, coherence) or human-in-the-loop feedback loops.

How Nexla closes the gaps

Nexla provides an interface that allows for integrating multiple LLMs and comparing their output based on typical architectural patterns, such as RAG. Compared to other alternatives for LLM comparison, Nexla offers the following advantages. 

Nexla combines true vendor neutrality, data integration, and comprehensive evaluation into a single, no-code environment. With over 20 pre-built LLM connectors (including GPT-4, Claude, DeepSeek, GLM, Mistral, on-prem models, and more), you can orchestrate parallel calls to any combination of models in one pipeline—no SDKs, no manual API wrangling. 

Nexla’s visual pipeline designer automatically handles data ingestion, prompt templating, and result collection, then surfaces side-by-side quality metrics (customizable to your domain) along with human review assignments. Rather than writing bespoke scripts to compare BLEU scores or token counts, you configure a Nexla pipeline that logs accuracy, hallucination rates, bias flags, and response times for every model at once. 

If you need to switch from GPT-4 to Claude 2—or add a cutting-edge Chinese LLM—the change is a simple configuration update, not a code rewrite. By unifying data preparation, multi-model orchestration, automated metrics, and human feedback in a single, low-code platform, Nexla addresses the limitations of both closed and open evaluation tools, empowering enterprise teams to make rapid, evidence-based decisions about LLMs.

Best practices while comparing LLMs

Prioritize use cases

Start any LLM evaluation by cataloging and prioritizing your specific tasks. Document the exact functions you want the model to perform (e.g., summarization, code generation, customer Q&A) and rank them by business importance.

Evaluate candidate models by how well their documented capabilities match your highest-priority scenarios. For each use case, identify the key model qualities needed, such as reasoning depth, world knowledge, or language style, and score models against these. 

Create a short “use case checklist” to guide all comparisons. For each LLM, run a few representative tasks early and see if its outputs meet your needs. 

Combine evaluation methods

Utilize a combination of automated metrics and human judgment to gain a comprehensive view. 

Teams should supplement standard benchmarks with LLM-as-a-judge evaluations: a separate LLM scores candidate outputs on dimensions such as relevance, coherence, and factuality, providing more context-sensitive and flexible assessments than rigid word-overlap metrics. Although “LLM‑as‑judge” methods are not perfect, they help capture nuanced qualities—such as fluency or adherence to style guidelines—that BLEU/ROUGE simply cannot measure. 

By combining traditional benchmarks with LLM-as-a-judge and custom test sets, enterprises can mitigate benchmark limitations and gain a more comprehensive view of model performance.

Evolve gold reference datasets

Generic benchmarks are a start, but will always miss your domain’s nuances. Create a high-quality “gold” dataset for your use cases. For example, collecting real user queries, expert-written ideal responses, and complex examples is known to be problematic. These are your baselines by which all models are judged. 

A practical approach is to start with a synthetic baseline (i.e., auto-generated prompts) and then add in human-verified data. Use model-generated outputs to identify edge cases or blind spots, then correct or annotate those examples manually. 

Periodically audit your reference set on a schedule. Add new templates and failure cases. Track coverage and ensure all key features or query types are covered. 

Diversity and recency are crucial: if your LLM integration adds new functionality or your customers ask new questions, update the gold references accordingly. 

An evolving test set helps ensure that your evaluation pipeline remains relevant and that good performance on the golden dataset in offline evaluation highly correlates with user satisfaction. As a practical recommendation, version-control your gold data and treat it as part of the product – use it for regression testing when comparing models or tweaking prompts.

Address compliance requirements

Pre-deployment, enumerate the legal and regulatory constraints on your LLM application. Some sectors (healthcare, finance) are subject to strict regulations (HIPAA, GDPR, PCI), and even general regulations (GDPR, CCPA) can be relevant. Failing to address data privacy legislation can result in significant fines and a loss of trust. Identify whether your data is sensitive (patient data, personal identifiers, etc.) or if outputs must conform to requirements (e.g., no prohibited content). Involve legal and compliance groups early to consider any compliance frameworks or certifications that may be required. This could involve creating an AI usage policy, securing Business Associate Agreements (for HIPAA) with providers, or strategizing how to manage data subject requests under GDPR in light of LLM logging practices.

Take note of where the LLM processing will be done and what data the provider retains. If cross-border data transfer is forbidden by your compliance needs or if data deletion is required by law, you may need to self-host an open-source LLM or use a compliant cloud region.

If an on-premises deployment is necessary, prefer models that can be deployed on your premises. If cloud services are required, select providers that offer compliance guarantees. Enterprises operating in regulated industries must vet LLMs not only for capabilities but also for specific compliance requirements. 

For example, the table below shows LLM requirements for HIPAA compliance.

Nexla’s pipelines can enforce these controls via no-code settings: you simply toggle “HIPAA mode,” and the platform will

• Automatically strip or tokenize PHI fields before sending prompts,
• Route inference through a dedicated, BAA-certified endpoint (on-prem or in a compliant cloud region),
• Log every request and response in an immutable audit store, and
• Run real-time validation rules against outputs (e.g., medical-term verification) with configurable error thresholds.

By codifying these HIPAA-specific requirements into your data pipelines, Nexla ensures that deploying an LLM for healthcare use cases is not only technically feasible but also fully aligned with regulatory mandates, providing you with the confidence to transition from proof of concept to production in a compliant and auditable manner.

Evaluate output consistency

LLM outputs are inherently nondeterministic, especially when sampling or high temperature is used. For applications that require reliability (e.g., legal advice, healthcare), variability can pose a significant risk. 

Test consistency by running the prompt multiple times at your chosen settings. If answers differ in accuracy or style, document that variance. Inconsistent responses can lead to confusion or a loss of trust in critical domains, such as healthcare. When deterministic output is required, you may need to set the temperature to a very low value or use deterministic decoding. Note that setting the temperature value to a smaller value improves consistency, but it affects creativity.

Use parameters and prompt design to control consistency. If consistency is poor, consider ensemble checks (e.g., re-querying under multiple settings and voting). 

Consistent benchmarking

Ensure that every model you compare is evaluated under identical conditions. Use the same test prompts, datasets, and environment settings for all LLMs. By doing so, performance differences reflect the models themselves, not changes in the test. 

Document every detail of your benchmark setup. Use containerized or scripted pipelines so that running the same tests later (or on a new model) is automated. By enforcing a consistent benchmarking methodology, you turn the comparison into a valid, apples-to-apples test rather than a series of one-off experiments. 

Conclusion

No single LLM is best in all categories. Closed-source LLMs (like GPTs) excel at many tasks but have usage fees and data residency limitations. On the other hand, Open-source LLMs (like LLaMA) give flexibility and cost savings, but may require extra engineering and fine-tuning. Ultimately, the right choice depends on your use case, data, and constraints.

Therefore, the key insight is to match model capabilities to your domain; continually evaluate on realistic tasks; and plan for scale and privacy. Ensure to use benchmarks and custom tests to understand weaknesses (hallucinations, latency). Architect your system to allow for easy swapping of models or the addition of more as needed. You should use tools like Nexla to automate data preparation and side-by-side model evaluation​. With careful design and thorough testing, engineers can harness the immense power of LLMs while maintaining control over cost, compliance, and quality.