How to Pick the Right LLM for the Job

Benchmarks You Can Actually Use

Imagine paying a premium for an LLM to write flawless code snippets, only to discover it’s about as useful as a damp paper towel when tasked with actual code editing.

Or worse, automating workflows with a model that haemorrhages budget because you overlooked a cheaper, equally capable option.

Benchmarks exist to stop that.

But not all benchmarks are created equal.

If you want to know which LLM excels at what, without burning time or money, this is your cheat sheet.

Why Benchmarks Matter (Especially for Security Automation)

Choosing the right LLM isn’t just about raw performance—it’s about fit for purpose. Some models are reasoning wizards; others ace code editing but crumble when handed plain-language tasks. Benchmarks help you:

• Save time: Get results faster by using the right tool for the job.

• Reduce costs: Optimize spend by choosing models that balance capability and pricing.

• Minimize frustration: Avoid trial-and-error guesswork on which LLM performs best.

• Improve security assessments: Know where vulnerabilities might arise when implementing AI in sensitive systems.

Whether you’re evaluating LLMs for automated tasks, development workflows, or general reasoning, these trusted benchmarks can help you make smarter, data-driven decisions.

1. Chatbot Arena: The Standard for Real-World Performance

Previously known as LMSYS, the Chatbot Arena benchmark has carved out a reputation for reliability and rigor. Its focus is on differentiation - testing models in ways that mimic real-world challenges. Read more about the origins here.

• Key Strength: Robust methodology that stays relevant as models evolve.

• Best For: General-purpose performance evaluation.

• Why It’s Useful: Chatbot Arena doesn’t get caught up in overly narrow tasks. Instead, it paints a holistic picture of a model’s strengths and weaknesses.

Use this benchmark to ensure models are robust across various inputs, reducing risks of unpredictable behavior when AI is deployed in security-sensitive environments.

2. Kagi LLM Benchmarking Project: Unpolluted, Ever-Changing Tests

The Kagi Benchmarking Project is unique because its tests constantly evolve. This prevents models from gaming the system or overfitting to benchmarks—a common pitfall in static tests.

• Key Strength: Dynamic, unpolluted tasks that reflect real-world reasoning, coding, and instruction-following challenges.

• Best For: Evaluating raw reasoning power and adaptability.

• Why It’s Useful: You get a more honest view of performance since models can’t memorize the test.

Adaptive benchmarks like Kagi are ideal for identifying models that may break down under unusual or adversarial prompts - a critical factor in cyber roles.

3. Aider Code Editing Benchmarks: Benchmark and Test LLMs for Code Tasks

Aider is more than a handy tool for developers–it’s also an effective way to benchmark LLMs for domain-specific coding tasks. Aider evaluates models against two key activities:

1. Code Reasoning: The ability to understand complex coding challenges, logic, and requirements.

2. Code Editing: The practical skills needed to edit, refactor, and optimize code.

• Key Strength: Precision benchmarking for both reasoning and editing capabilities.

• Best For: Developers assessing LLMs for code-heavy workflows (e.g., debugging, refactoring, or implementing features).

• Why It’s Useful: Aider runs the latest and greatest LLMs through its benchmarks, giving developers clear, comparative insights into which models perform best for these tasks.

Pro Tip: While Aider doesn’t include pre-built security prompts, its GitHub repo lists the tasks it benchmarks. These can be trivially customized to focus on security-specific challenges, like identifying vulnerabilities or optimizing code for secure architectures. Run Aider benchmarks against security-oriented repos to assess how well an LLM handles secure coding scenarios.

Aider doesn’t replace security audits or static analysis tools, but it provides a lightweight, domain-specific benchmark to identify the most capable LLM for coding workflows.

4. Simple Bench: Why Humans Still Win (Sometimes)

Simple Bench is a breath of fresh air because it reminds us that AI doesn’t always outperform human intuition and common sense.

• Key Strength: Highlights tasks where humans with unspecialized knowledge can still outshine AI.

• Best For: Identifying tasks where AI struggles (e.g., ambiguous or nuanced problem-solving).

• Why It’s Useful: If you’re relying on LLMs for critical decisions, this benchmark shows you where human oversight is still invaluable.

When automating security workflows, cross-reference findings with Simple Bench to ensure human oversight is applied where AI may fall short.

5. Tag-Based GitHub Issues: Your LLM Knowledge Hub

This isn’t a traditional benchmark, but it’s worth noting. By using GitHub Issues as a tagging and bookmarking system, developers can efficiently organize their LLM resources. Learn more about effective tagging systems here.

• Key Strength: Keeps your LLM research organized and searchable.

• Best For: Developers and teams managing multiple models or automated workflows.

• Why It’s Useful: Think of it as your own living benchmark—a place where you track real-world results and compare models.

Use tagged GitHub issues to document security-related tasks, benchmarks, and vulnerabilities encountered during LLM projects.

The Takeaway

Benchmarks exist to help you make better choices faster. Whether you’re automating a task, scaling up LLM usage, or just tinkering, these tools save time, money, and sanity:

• LMSYS: General real-world performance (robustness for sensitive environments)

• Kagi: Dynamic, reasoning-heavy tasks (resilience to adversarial prompts)

• Aider: Precision in code editing and reasoning (security-focused development)

• Simple Bench: Areas where humans still shine (oversight for critical decisions)

• Tag-Based Systems: Your personalized LLM repository (tracking vulnerabilities and findings)

So, next time you’re evaluating an LLM, consult these benchmarks first. You’ll get the best results in the shortest time, and your wallet will thank you.

AI benchmarks can be a compass in a fast-moving, foggy landscape. Use them wisely, and you’ll avoid walking off an AI efficacy cliff edge—especially in security-critical projects. But don’t rely on 3rd party benchmarks for mission-critical or sensitive applications.

I hope you find this useful. What’s your take on current benchmarking? Or do you rely more on “vibes” tests?

Cheers, Craig