180 models ranked for testing and QA. Scored with bonuses for reasoning (test logic), large context (codebase analysis), large output (test suite generation), JSON mode (structured fixtures), function calling, and streaming.
| # | Model | Score |
|---|---|---|
| 1 | Claude Opus 4.7Anthropic | 95 |
| 2 | GPT-5.5OpenAI | 93 |
| 3 | Gemini 3.1 Pro Preview Custom ToolsGoogle | 92 |
| 4 | Gemini 3.1 Pro PreviewGoogle | 92 |
| 5 | GPT-5.4 ProOpenAI | 92 |
| 6 | GPT-5.4OpenAI | 92 |
| 7 | GPT-5.5 ProOpenAI | 91 |
| 8 | GPT-5.2 ProOpenAI | 91 |
| 9 | Claude Opus 4.6 (Fast)Anthropic | 90 |
| 10 | Claude Opus 4.6Anthropic | 90 |
| 11 | GPT-5.2-CodexOpenAI | 90 |
| 12 | GPT-5.2OpenAI | 90 |
| 13 | GPT-5.3-CodexOpenAI | 89 |
| 14 | GPT-5 ProOpenAI | 89 |
| 15 | Gemini 3 Flash PreviewGoogle | 88 |
| 16 | GPT-5.1-Codex-MaxOpenAI | 88 |
| 17 | GPT-5 CodexOpenAI | 88 |
| 18 | GPT-5OpenAI | 88 |
| 19 | GPT-5.1OpenAI | 87 |
| 20 | GPT-5.1-CodexOpenAI | 87 |
| 21 | GPT-5.1-Codex-MiniOpenAI | 87 |
| 22 | DeepSeek V4 ProDeepSeek | 87 |
| 23 | o3 Deep ResearchOpenAI | 87 |
| 24 | o3 ProOpenAI | 87 |
| 25 | o3OpenAI | 87 |
| 26 | Claude Sonnet 4.6Anthropic | 85 |
| 27 | Claude Opus 4.5Anthropic | 85 |
| 28 | Grok 4.20xAI | 89 |
| 29 | Gemini 2.5 ProGoogle | 84 |
| 30 | Gemini 2.5 Pro Preview 06-05Google | 84 |
Generate comprehensive unit, integration, and e2e tests from source code. Reasoning models understand edge cases, boundary conditions, and error paths.
Analyze code for potential bugs, race conditions, and security vulnerabilities. Large context handles full codebases for cross-module analysis.
Generate realistic test data, mock objects, and API fixtures. JSON mode produces structured data compatible with testing frameworks.
Write Selenium, Playwright, and Cypress scripts. Function calling enables test orchestration and CI/CD pipeline integration.
Reasoning models analyze code to identify edge cases, boundary conditions, and failure modes that manual testing often misses. They generate unit tests, integration tests, and end-to-end test scenarios. Models with large context understand the full codebase for better test coverage.
AI generates test code that runs in existing frameworks (Jest, pytest, JUnit). Traditional tools execute tests. They are complementary - AI creates the tests, frameworks run them. AI also helps maintain tests by updating them when code changes break existing assertions.
Models generate failing tests from requirements before implementation, following the red-green-refactor cycle. Reasoning ensures tests capture the intended behavior, not just the current implementation. They suggest test improvements as code evolves.
Reasoning for identifying edge cases and error paths. Large context for understanding test dependencies across modules. Function calling for running tests and analyzing results. JSON mode for structured test reports. Models here rank highest on benchmark tests that evaluate code correctness.