I Benchmarked 9 AI Models for Candidate Screening—Then Switched from GPT-4o to Grok-4

I Benchmarked 9 AI Models for Candidate Screening—Then Switched from GPT-4o to Grok-4

Estimated reading time: 7 minutes

• GPT-4o’s Inconsistency: Despite its speed, GPT-4o’s unpredictable accuracy in candidate screening led to significant bottlenecks and manual overrides, highlighting its unsuitability for critical tasks.
• Crucial Benchmarking: Continuous and rigorous benchmarking with real-world, nuanced test cases is essential to identify the optimal AI models as the landscape rapidly evolves.
• Prioritize Accuracy: For critical applications like candidate screening, accuracy and predictability should be prioritized above speed and cost, as inconsistent results can lead to costly errors.
• Grok-4’s Superiority: xAI’s Grok-4 Fast Reasoning emerged as the top performer, achieving 100% accuracy, a respectable response time, and significantly lower cost compared to GPT-4o and other models tested.
• Test for Nuance: Effective AI evaluation requires prompts that demand contextual understanding and complex reasoning, moving beyond simple keyword matching to grasp professional hierarchies and role responsibilities.

• The Unreliability of Familiar Tools: Why GPT-4o Needed a Replacement
• The Setup: Stress-Testing AI with a “Problem Candidate”
• The Verdict Is In: Grok-4 Rises as the New Champion
  • Performance at a Glance:
• Actionable Steps for Your AI Strategy
• Conclusion: The Future of Candidate Screening Is Here
• Frequently Asked Questions

“At Topliner, we use AI to assess candidate relevance for executive search projects. Specifically, we rely on GPT-4o, because, well… at the time it was among the sharpest knives in the drawer. And to be fair, it mostly works. Mostly. The problem? Every now and then, GPT-4o goes rogue. It decides that a perfectly relevant candidate should be tossed aside, or that someone utterly irrelevant deserves a golden ticket. It’s like flipping a coin, but with a fancy API. Predictability is out the window, and in our line of work, that’s unacceptable. So, I started wondering: is it time to move on? Ideally, the new model should be available on Microsoft Azure (we’re already tied into their infrastructure, plus shoutout to Microsoft for the free tokens – still running on those, thanks guys). But if not, any other model that gets the job done would do.

Our core criteria for any new model are straightforward:

• Accuracy – Top priority. If we run the same candidate profile through the system twice, the model should not say “yes” once and “no” the next time. Predictability and correctness are everything.
• Speed – If it thinks too long, the whole pipeline slows down. GPT-4o’s ~1.2 seconds per response is a pretty good benchmark.
• Cost – Ideally cheaper than GPT-4o. If it’s a lot cheaper, even better.

Recently, I stumbled upon xAI’s new Grok-4 Fast Reasoning model, which promised speed, affordability, and smart reasoning. Naturally, I put it to the test.

The Unreliability of Familiar Tools: Why GPT-4o Needed a Replacement

For months, GPT-4o served as our go-to AI for candidate screening, primarily due to its perceived sophistication and widespread adoption. While its rapid processing speed was undeniably impressive, its occasional lapses in judgment created significant bottlenecks and introduced an unacceptable level of risk into our executive search process. Imagine sifting through hundreds of profiles, only for the AI to arbitrarily dismiss a highly qualified individual or, conversely, greenlight someone completely off-target. This inconsistent behavior forced manual double-checks, negating the efficiency gains AI was supposed to deliver.

The necessity for a more dependable AI became glaringly obvious. We needed a model that could consistently interpret nuanced information, apply complex criteria without deviation, and maintain high performance metrics across multiple dimensions. The existing integration with Microsoft Azure was a plus, offering potential for seamless migration. However, the paramount concern was finding an AI that could deliver unwavering accuracy, even if it meant exploring options beyond our current ecosystem. The search for a model that truly understood and applied context was on.

The Setup: Stress-Testing AI with a “Problem Candidate”

To rigorously evaluate potential replacements, I designed a specialized test case around a “problem candidate profile”—a scenario where GPT-4o consistently faltered. The objective was to ascertain if a candidate had held a role equivalent to “CFO / Chief Financial Officer / VP Finance / Director Finance / SVP Finance” at SpaceX, encompassing all anticipated variations in title, scope, and seniority. This wasn’t just about keyword matching; it was about contextual understanding.

Here’s the specific prompt I utilized for this challenging evaluation:

Evaluate candidate's eligibility based on the following criteria. Evaluate whether this candidate has ever held a role that matches or is equivalent to 'CFO OR Chief Financial Officer OR VP Finance OR Director Finance OR SVP Finance' at 'SpaceX'. Consider variations of these titles, related and relevant positions that are similar to the target role(s). When making this evaluation, consider: - Variations in how the role title may be expressed. - Roles with equivalent or similar or close or near scope of responsibilities and seniority level. - The organizational context, where titles may reflect different levels of responsibility depending on the company's structure. If the candidate's role is a direct or reasonable equivalent to the target title(s), set targetRoleMatch = true. If it is unrelated or clearly much below the intended seniority level, set targetRoleMatch = false. Return answer: true only if targetRoleMatch = true. In all other cases return answer: false. Candidate's experience:
[here is context about a candidate]

This prompt, deceptively simple, proved to be an effective differentiator. It demanded more than superficial pattern recognition; it required genuine comprehension of professional hierarchies and role responsibilities. A model that could ace this test demonstrated an ability to grasp nuance—a critical skill for accurate candidate screening.

I deployed this test across 9 distinct AI models, including the latest iterations from OpenAI (GPT-4o, GPT-4.1, GPT-5 Mini, GPT-5 Nano, GPT-5 (August 2025), plus o3-mini and o4-mini), and xAI’s promising Grok-3 Mini and Grok-4 Fast Reasoning. The goal was to leave no stone unturned in our quest for the optimal AI partner.

The Verdict Is In: Grok-4 Rises as the New Champion

The benchmarking results were eye-opening, revealing significant disparities in performance across the models. While GPT-4o initially seemed like the fastest contender, its accuracy proved to be its Achilles’ heel, making it largely unsuitable for our needs.

Performance at a Glance:

• Azure OpenAI GPT-4o: Fastest at 1.26s average response, but a dismal 1/10 correct (10%) and most expensive at $12.69 per 1000 req. It was like a race car that couldn’t stay on the track.
• xAI Grok-4 Fast Reasoning: Stood out with a perfect 10/10 correct (100%), a respectable 2.83s average response time, and an incredibly low cost of $0.99 per 1000 requests. This model truly hit the sweet spot of our requirements.
• Azure OpenAI o4-mini: Also achieved 100% accuracy with a fast 2.68s average response, but came with a higher price tag of $5.47 per 1000 requests. A strong performer, but less cost-efficient than Grok-4.
• OpenAI GPT-5 Nano: Offered unparalleled cost-efficiency at $0.29 per 1000 requests and 100% accuracy. However, its average response time of 8.04s made it too slow for our real-time pipeline, which prioritizes speed second only to accuracy.
• OpenAI GPT-4.1: The least accurate model, failing all 10 tests (0% correct), making it completely unusable despite a decent speed.

The overall leaderboard confirmed our suspicions and provided a clear path forward. Grok-4 Fast Reasoning emerged as the undisputed winner with an impressive 93.1/100 overall score. Its balance of 100% accuracy, good speed (88/100 relative to the fastest), and outstanding cost-effectiveness (94/100) made it the ideal choice.

To illustrate the real-world impact of these findings, consider a scenario from last month: GPT-4o incorrectly flagged a candidate with extensive experience as “Head of Financial Operations” at a major aerospace company as irrelevant to a “VP Finance” role. The title wasn’t an exact match, but the scope and seniority were clearly aligned. This error nearly led us to overlook a top-tier candidate, requiring a tedious manual review to correct the AI’s oversight. With Grok-4, which consistently demonstrated a deeper understanding of role equivalency, such a misclassification would likely have been avoided, saving valuable time and ensuring no qualified candidate slipped through the cracks.

Actionable Steps for Your AI Strategy

Based on this rigorous benchmarking, here are three crucial steps for any organization relying on AI for critical tasks:

1. Benchmark Consistently and Rigorously: Do not assume your current AI models remain optimal. The AI landscape evolves rapidly. Set up a regular, structured benchmarking process using real-world test cases that challenge your models’ ability to handle nuance and edge cases. Complacency can lead to costly errors and missed opportunities.
2. Prioritize Accuracy and Predictability Above All Else: While speed and cost are important, an AI that confidently delivers incorrect or inconsistent results is detrimental. For critical applications like candidate screening, ensure your chosen model provides reliable, repeatable outcomes. A slightly slower, more expensive model that is always correct is far more valuable than a lightning-fast, cheap one that frequently errs.
3. Define and Test for Nuance: Simple keyword matching is often insufficient. Develop prompts and test cases that require models to understand context, infer meaning from variations, and apply complex reasoning. This will help you identify models that truly “understand” your data versus those that merely “guess” based on superficial patterns.

Conclusion: The Future of Candidate Screening Is Here

A year ago, GPT-4o was one of the most advanced and reliable options. We built big chunks of our product around it. But time moves fast in AI land. What was cutting-edge last summer looks shaky today. This little experiment with Grok-4 was eye-opening. Not only does it give us a better option for candidate evaluation, but it also makes me want to revisit other parts of our application where we blindly trusted GPT-4o.

The moral of the story: don’t get too attached to your models. The landscape shifts, and if you don’t keep testing, you might wake up one day realizing your AI is confidently giving you the wrong answers… in record speed.

So yes, GPT-4o, thank you for your service. But it looks like Grok-4 Fast Reasoning is taking your seat at the table. We’re excited to integrate Grok-4 and elevate the precision and efficiency of our candidate screening processes.

Ready to re-evaluate your AI stack and ensure you’re using the best tools available? Share your benchmarking experiences or questions in the comments below!

Frequently Asked Questions

Why did Topliner switch from GPT-4o to Grok-4 for candidate screening?

Topliner switched from GPT-4o due to its inconsistent accuracy. Despite its speed, GPT-4o occasionally misclassified highly relevant or irrelevant candidates, leading to significant manual double-checks and undermining the efficiency AI was supposed to provide.

What were the key criteria for selecting a new AI model?

The core criteria were accuracy (top priority, ensuring consistent and correct evaluations), speed (to maintain pipeline efficiency, with GPT-4o’s ~1.2s as a benchmark), and cost (ideally cheaper than GPT-4o).

How was the AI benchmarking test designed?

A “problem candidate profile” was used, focusing on evaluating if a candidate held a role equivalent to “CFO / Chief Financial Officer / VP Finance / Director Finance / SVP Finance” at SpaceX. The prompt required contextual understanding, considering variations in titles, scope, and seniority, rather than just keyword matching.

Which AI models were included in the benchmark?

The benchmark included 9 distinct AI models: OpenAI’s GPT-4o, GPT-4.1, GPT-5 Mini, GPT-5 Nano, GPT-5 (August 2025), o3-mini, o4-mini, and xAI’s Grok-3 Mini and Grok-4 Fast Reasoning.

What were the main findings regarding Grok-4’s performance?

xAI’s Grok-4 Fast Reasoning achieved a perfect 10/10 correct (100%) in the tests, with a respectable average response time of 2.83 seconds and a very low cost of $0.99 per 1000 requests. It was deemed the ideal choice due to its balance of accuracy, speed, and cost-effectiveness.