This benchmark report provides a side-by-side comparison of Google’s Gemini 2.5 Pro and OpenAI’s o1 models in AI-driven software testing. Across both unit test generation (UTG) and API test generation (ATG), Gemini 2.5 Pro demonstrated clear superiority in key areas . In summary, Gemini 2.5 Pro outperformed OpenAI o1 by:

• Producing deeper and more robust test suites (especially on large, complex applications) .

• Showing greater adaptability and learning over repeated test runs .

• Consistently handling multi-step test scenarios that mirror real user flows, a capability crucial for enterprise-grade testing

OpenAI’s o1 model had strengths in smaller-scale applications, but overall it generated shallower tests and struggled to match Gemini on complex projects.

TLDR;

We benchmarked Google Gemini 2.5 Pro against OpenAI o1 on two fronts:

• UTG (Unit Test Generation)

• ATG (API Test Generation)

Across multiple applications, environments, and repeated test cycles, following were the major parameters to check performance:

• Suite quality and depth

• Multi-run adaptability

• Test coverage in large, enterprise-grade codebases

👉 Gemini 2.5 Pro consistently outperformed OpenAI o1 in test depth, adaptability, and coverage, especially for complex and large-scale applications.

The following sections present the comparative highlights, methodology, detailed results with visual aids suggestions, and strategic recommendations for choosing the right model.

Comparative Highlights

Test Depth & Complexity:

Gemini 2.5 Pro generated significantly more comprehensive tests per target component. It frequently created multi-step test sequences (e.g. complex API call flows), whereas OpenAI o1’s tests were mostly single-step and shallow.

In one API benchmark, Gemini produced dozens of suites with 5+ test steps while o1 produced almost none. This indicates Gemini’s strength in crafting deep, thorough test cases covering complex logic paths.

Learning Adaptability:

Gemini 2.5 Pro exhibited a strong learning curve across multiple test generation attempts. When re-running tests on the same system, Gemini refined its strategy – reducing redundant tests, targeting uncovered areas, and improving the pass rate. By the third iteration on a complex API, Gemini reached a balanced 50% pass rate with fewer, higher-quality tests.

OpenAI o1, in contrast, showed little adaptation – its approach remained largely static, and its pass rates actually declined in later attempts (dropping below one-third by the third run) as it kept generating similar or excessive tests without learning.

Scalability (Large vs. Small Codebases):

Gemini 2.5 Pro scaled effectively to large enterprise codebases, achieving high coverage by focusing on critical code paths. For a large 88-file service backend, Gemini covered about 67% of code, far more than o1’s 48%, by testing core modules deeply .

OpenAI o1, however, excelled on a small project, reaching 74% coverage on a lightweight module vs Gemini’s 44%. This suggests o1 takes a breadth-first approach (covering many files superficially), which works for small apps but struggles on big systems .

Gemini’s targeted “depth-first” strategy makes it better suited for large, complex applications, while o1’s strength in small apps may benefit microservice-scale projects.

Benchmarking Methodology

Overview: We evaluated both models using Keploy’s automated testing tools across a range of applications. The goal was to measure each model’s effectiveness at generating unit tests and API tests under consistent conditions .

Our analysis covered two modes: UTG (Unit Test Generator) for backend unit tests, and ATG (API Test Generator) for end-to-end API tests . Each model was integrated into these tools to generate tests for several target applications.

TLDR;

Benchmark Setup

Applications Tested:

We selected 3 open-source, 1 closed source applications of varying size and complexity to ensure a well-rounded comparison :

• PetClinic

  A Java/Spring monolith (~5.3 k LOC, 83 files) often used as a reference implementation for testing frameworks. Its well-structured, enterprise-style endpoints make it perfect for gauging baseline API-generation quality. Gemini generated 143 suites (135 pass), with 32 multi-step suites (≥ 5 calls) against a 12 k-token OpenAPI schema and 3.4 k-token example set.

• Keploy API Server

  A Go micro-service (~4 k LOC, 88 files) mirroring a real production API gateway. It stresses unit-test depth across many modules. Gemini achieved 67.24 % line coverage (2 718 / 4 042) with 42 files ≥ 50 % covered, showing its strength on large Go back-ends.

• Listmonk

  An open-source Go mailing-list manager (~17.5 k LOC, 81 files) featuring complex, inter-dependent REST calls. Its 20.8 k-token schema and 789-token cURL examples push API generators to reason over long, branching flows. In the final run Gemini produced 185 suites, 34 multi-step, demonstrating adaptive learning on a hefty codebase.

• FastHttp-Postgres

  A tiny Go demo app (117 LOC, 4 files) used to test how models behave on micro-services. Despite its size, Gemini still wrote meaningful tests, covering 44.44 % of lines with 2 files ≥ 50 %, while o1 chased broad coverage (74.49 %) with many shallow assertions.

Test Procedure

For each application, we ran both Gemini 2.5 Pro and OpenAI o1 under identical conditions to generate tests.

• In API testing (ATG), we conducted multiple consecutive test generation attempts (particularly on Listmonk) to observe learning effects – i.e. whether the model improves its test suite when run repeatedly on the same app.

• In Unit testing (UTG), we measured code coverage achieved by the generated tests on each codebase. All test executions and outcomes (pass/fail rates, coverage, etc.) were recorded for analysis.

Environment

To ensure fairness, all tests were executed in a controlled environment on Ubuntu 24.04. We allocated consistent compute resources: for UTG runs, each model had 12 CPU cores and 16 GB RAM, and for ATG runs, 8 cores and 16 GB RAM were used .

This standardized environment eliminated hardware variance, so any performance differences can be attributed to the models themselves rather than the runtime conditions.

Results and Analysis

API Testing (ATG) Results

For API test generation, we focused on how each model created end-to-end test suites for complex user flows. We observed their performance on Listmonk (the complex API system) over three successive attempts, and on PetClinic in a single attempt (since PetClinic is a well-understood benchmark).

Listmonk (Complex API) – Iterative Attempts: On the first run, Gemini 2.5 Pro generated 238 API test suites for Listmonk, of which 134 passed and 104 failed, while OpenAI o1 generated more suites (343) with 172 passes and 171 fails.

Notably, Gemini produced 36 complex multi-step test suites (each containing 5 or more API calls in sequence) in that first attempt, whereas o1 produced 0 such multi-step suites . This indicates that Gemini immediately began testing more complicated workflows that o1 did not attempt.

By the third attempt on Listmonk, the difference in approach became even more pronounced. Gemini deliberately reduced the total number of test suites in the second attempt and then settled at 185 suites in attempt 3, improving the focus and quality of tests.

In that third run, Gemini had 94 passing tests vs 91 failing, essentially a ~50% pass rate, and managed to create 34 multi-step test flows .

In contrast, OpenAI o1 by the third run generated 384 suites (even more than it did initially) but saw only 116 pass while 268 fail, yielding a much lower success rate . It managed just 2 multi-step flows by attempt 3, indicating minimal improvement in handling complex sequences.

Gemini’s pattern – fewer total tests with higher success in later attempts – suggests a form of optimization and learning. In fact, Gemini adjusted its strategy after each run: it cut down redundant or ineffective tests in attempt 2 (dropping to 126 total suites) and then re-expanded strategically in attempt 3 with improved results.

This iterative refinement led to more balanced and effective test coverage on Listmonk’s intricate API. OpenAI o1, on the other hand, did not exhibit a similar learning curve; it tended to regenerate a large volume of tests without addressing the failures from previous runs, resulting in diminishing returns (lower pass rates) over time.

PetClinic (Structured API) – Single Attempt: On the PetClinic application (a more structured, familiar domain), both models performed strongly in a one-shot test generation. Gemini 2.5 Pro created 143 API test suites, with 135 passing and only 8 failing, while OpenAI o1 created 128 suites, with 127 passing and just 1 failing .

This means both achieved high success rates on PetClinic’s relatively straightforward API. However, even here, Gemini showed an edge in test depth: it included 32 multi-step test scenarios covering complex user flows, compared to 18 multi-step scenarios from o1 .

In other words, Gemini not only matched o1’s accuracy on this well-understood app, but it also produced more comprehensive end-to-end sequences (covering longer chains of API calls).

This reinforces that Gemini consistently aims for more thorough testing, even when the app under test is one where o1 can perform well.

Key ATG Takeaways

Overall, Gemini 2.5 Pro proved more effective for API testing in enterprise contexts. It can iteratively refine its tests to improve coverage and reduce failures, demonstrating a capability to learn from previous attempts.

Gemini’s tests tend to mirror real user workflows with multiple steps, which is crucial for catching issues in complex integrations.

OpenAI o1, while capable in simpler or well-structured APIs (as seen in PetClinic), tended to saturate the test space with quantity over quality on more complex systems, and it showed little improvement when re-run.

For decision-makers, this means Gemini could provide more reliable and evolving API test suites for complex, evolving systems, whereas o1 might be less suited for iterative test improvements.

Unit Testing (UTG) Results

In the unit test generation benchmarks, we measured how well each model could cover the code by generating unit tests for two projects of different scales: a large codebase (API Server) and a small codebase (FastHttp-Postgres).

We examined code coverage percentage and the distribution of that coverage across files.

Large Codebase – API Server: In a service-oriented API backend with dozens of modules (88 source files), Gemini 2.5 Pro’s generated unit tests achieved 67.2% code coverage, substantially higher than OpenAI o1’s 48.0% coverage .

This indicates that Gemini’s tests hit a majority of the code paths in this complex project, whereas o1’s tests covered less than half. The way each model achieved these results differed notably.

Gemini’s strategy was to focus on a somewhat smaller subset of files but test them deeply: out of 88 files, 42 files had at least 50% of their code covered by Gemini’s tests .

OpenAI o1, by contrast, spread its effort across more files – it had 65 files with ≥50% coverage , but many of those were just barely above 50%. This breadth-first approach left a lot of code only lightly tested, resulting in the lower overall coverage percentage.

In fact, o1 generated nearly double the volume of test code (over 8,000 lines of test code vs Gemini’s ~4,000) for this project , yet it still missed more critical logic – a sign that simply writing more tests didn’t translate to better coverage for the important parts of the system.

Gemini’s “intelligent targeting” of core logic and business-critical paths is reflected in the higher density of coverage in key files .

Small Codebase – FastHttp-Postgres: In the small Go web server (only 4–5 source files), OpenAI o1 actually achieved broader coverage: about 74.5% of code covered, compared to 44.4% for Gemini on this project .

This was expected given o1’s tendency to spread tests widely; in a tiny codebase, covering many files broadly essentially means covering most of the code.

OpenAI o1 wrote tests for 5 out of 5 files, with 3 files crossing 50% coverage , effectively exercising a large portion of this simple application.

Meanwhile, Gemini wrote tests for 4 of the files with 2 files above 50% coverage , focusing on what it deemed the most important functions.

Interestingly, despite the lower aggregate coverage, Gemini maintained a consistent testing strategy even on this small app – it targeted functional “hotspots” similarly to how it handled larger code, rather than chasing coverage for every line .

The result was a lower coverage number, but potentially a more meaningful test set (hitting the core logic of the app).

OpenAI o1’s strong performance here demonstrates its strength in quickly covering straightforward code; it’s effective for small apps, albeit with a more scattershot approach .

Key UTG Takeaways

The unit testing results reveal a trade-off between depth and breadth. Gemini 2.5 Pro clearly scales better to complex, large-scale codebases – it intelligently prioritizes the areas of code that matter most, yielding higher overall coverage where it counts .

This makes it well-suited for enterprise applications with thousands of lines of code, where a deep understanding is needed to generate meaningful tests.

OpenAI o1, on the other hand, shows strength in smaller projects by quickly covering many components (achieving high raw coverage on a tiny codebase) .

However, that broad strategy doesn’t translate into high coverage on big systems, and it potentially leaves critical complex logic under-tested.

In practice, this suggests that teams with large, complex codebases will benefit more from Gemini’s depth, whereas o1 might be considered for quick wins on simple services – though its lack of depth remains a limitation.

Strategic Conclusion and Recommendations

Our evaluation clearly shows that Gemini 2.5 Pro leads OpenAI o1 in advanced software testing capabilities.

Gemini proved superior in delivering thorough and adaptable test generation for complex, real-world applications. It consistently produced deeper test coverage on large codebases, learned from iterative runs, and handled intricate multi-step scenarios that are essential for enterprise QA workflows.

OpenAI o1 showed some advantage in small-scale scenarios, but those gains are outweighed by its shortcomings in depth and adaptability when scaling up.

Given these findings, our team (Keploy) has decided to adopt Gemini 2.5 Pro as the preferred AI model powering our unit and API test generation tools. The rationale is straightforward: Gemini offers better scalability to handle larger APIs and more complex codebases, and improved adaptability across diverse application architectures.

In an enterprise setting with heterogeneous systems and evolving code, these qualities are critical. Gemini’s ability to refine its tests over time means it can keep up with iterative development, continually improving test depth without human intervention – a significant strategic advantage for QA automation.

Recommendations

For organizations considering AI-driven testing solutions, Gemini 2.5 Pro is highly recommended as the go-to model for robust test generation. Its strengths align with enterprise needs: it will provide more value on large and complex projects by uncovering edge cases and ensuring core functionalities are well-tested.

We suggest integrating Gemini into your CI/CD pipeline for continuous testing, especially if your codebase is sizable or your API workflows are complex. OpenAI o1 may still be useful for quick testing on smaller applications or prototypes, where broad coverage in a short time is needed.

However, be mindful that o1’s tests might require additional vetting for depth and relevance. In contrast, Gemini’s richer test output can reduce the manual effort in reviewing and augmenting test suites, thanks to its higher accuracy and multi-step logic coverage.

The Future of AI Testing Is Deeper, Smarter, and Adaptive

In conclusion, Google’s Gemini 2.5 Pro currently stands out as the superior choice for AI-powered software testing. Its performance in our benchmarks – higher coverage on large systems, adaptive learning, and generation of complex test scenarios – positions it as a strategic asset for quality assurance in modern software projects.

Enterprise decision-makers planning for long-term test automation should favor solutions built on Gemini’s capabilities to achieve more reliable and scalable testing outcomes.

As AI models continue to evolve, we will keep evaluating new contenders, but for now, Gemini 2.5 Pro sets the benchmark for excellence in this domain.