Coding agents are no longer experimental tools. Teams now rely on them to ship features, fix bugs, and scaffold full applications. However, the market has fragmented into three categories: agentic CLI tools, AI code editors embedded in IDEs, and cloud IDE agents. Each claims to automate development. Few comparisons show how they differ under identical workloads.
We benchmarked agentic CLIs and AI Code Editors on their agentic capabilities with 10 real-world web tasks. Evaluate their backend and frontend capabilities, see how they operate, how fast they are, and how expensive they are.
AI coding benchmark results
Agent | Type | Model | Backend Score | UI Score | Combined Score | Avg Time to Complete | *Cost ($) |
|---|---|---|---|---|---|---|---|
Cursor | IDE | claude-opus-4.6 | 0.64 | 1 | 0.75 | n/a | $27.90 |
Kiro Code | IDE | claude-opus-4.6 | 0.59 | 1 | 0.71 | n/a | ~5.5$ |
Antigravity | IDE | claude-opus-4.6 | 0.63 | 0.81 | 0.69 | n/a | Free |
Codex | CLI | gpt-codex-5.2 | 0.58 | 0.89 | 0.67 | 426.7 | ~$4.0 |
Roo Code | IDE / Cloud Ide | claude-opus-4.6 | 0.56 | 0.87 | 0.65 | n/a | $53.10 |
Replit | Cloud IDE | n/a | 0.56 | 0.78 | 0.62 | n/a | $55.00 |
Kiro CLI | CLI | gemini-3-pro-preview | 0.48 | 0.8 | 0.58 | 167.9 | ~$1.9 |
Windsurf | IDE | claude-opus-4.6 | 0.5 | 0.71 | 0.57 | n/a | ~$10.2 |
Claude Code | CLI | claude-opus-4.5 | 0.38 | 0.95 | 0.55 | 745.5 | ~$12.0 |
Aider | CLI | gemini-3-pro-preview | 0.41 | 0.8 | 0.52 | 257.5 | ~$1.6 |
For the cost reporting and evaluation visit methodology.
For detailed results and tool insights, you can visit Agentic CLI Benchmark, AI Code Editor Benchmark, and to see which LLM used agents better, visit Agentic LLM Benchmark.
Insights
We benchmarked both CLI agents and AI code editors under identical workloads. Both categories have clear strengths, but they behave differently during execution.
AI code editors generate code quickly and then spend a significant portion of their runtime debugging and correcting errors within the IDE environment. Their architecture encourages iterative refinement, which improves frontend reliability and overall task completion accuracy.
CLI agents follow a more linear execution pattern. They plan, implement, review, and pause to gather input rather than continuously debugging in an interactive workspace. This results in faster task completion and lower cost, especially in built-in configurations without additional orchestration layers.
Structurally, CLI tools are more configurable. Users can script their behavior, integrate them into pipelines, run parallel sessions, and modify routing strategies. AI code editors offer configuration options, but their workflows are constrained by IDE integration.
When comparing default, out-of-the-box setups, the pattern is consistent: CLI agents are faster and cheaper, while AI code editors achieve higher overall accuracy.
Accuracy
The highest combined score in the dataset belongs to Cursor with Claude Opus 4.6 at 0.751. Kiro IDE and Antigravity follow closely, both above 0.69. These systems consistently achieve perfect or near-perfect UI scores, often reaching 1.0.
The best CLI configuration, Codex CLI with GPT-Codex-5.2, reaches 0.677. The gap between the top IDE agent and the strongest CLI is roughly seven percentage points. This is meaningful but not dramatic. It indicates that AI code editors are more reliable in full-stack scenarios, especially when frontend behavior must strictly match the specification.
The reason is that, from our observations, AI code editors have more built-in debugging tools. For example, Antigravity can open a browser window and test each endpoint itself. Cursor did not interact with the browser window, but it also opens one. Also, structurally, they are coding fast, then spending a long time on debugging.
Cost
The cost gap is not marginal. High-performing CLI tools cost roughly $1.6 to $4 per run. Cursor costs $27.9 in this benchmark configuration. Roo-Code and Replit exceed $50.
The strongest CLI system costs about one-sixth as much as Cursor while delivering about 10 percent lower combined accuracy. This changes the economic framing. AI code editors provide incremental accuracy gains at substantial cost premiums.
The structural reason is that AI code editors incorporate browser automation, workspace indexing, IDE plugin orchestration, and persistent interactive loops. CLI agents operate closer to the execution layer and avoid heavy UI telemetry. Fewer orchestration layers translate into lower token consumption and shorter runtime.
Yet, since pay-as-you-go pricing is high for AI code editors, they are used with monthly subscriptions, which are cheaper than direct pay-as-you-go.
Runtime
Among measured tools, Kiro CLI completes tasks in 167.9 seconds. Aider follows with 257 seconds. Claude Code CLI requires 745.5 seconds. Gemini CLI exceeds 800 seconds.
Runtime for AI code editors is not shared, and they often request more confirmation. They generally have allowlists that let you add a command to the allowlist and run it automatically next time, yet, in practice, CLI agents are more autonomous than AI code editors because they spend more time debugging, such as opening a browser window and actually testing it.
Configurability and workflow control
CLI tools are structurally more configurable. They support parallel terminal sessions, custom orchestrators, model routing strategies, CI/CD integration, and distributed execution. Advanced users can chain agents, split tasks, or dynamically swap models.
AI code editors prioritize interactive collaboration. They expose intermediate steps, show diffs inline, allow manual intervention mid-execution, and operate within familiar development environments. They resemble a coding partner rather than a programmable subsystem.
This is not merely a UX distinction. It reflects two optimization philosophies. CLI tools optimize for system-level automation and scalability. AI code editors optimize for human-in-the-loop productivity.
AI Code Review Tools
As AI-generated code becomes more common, code review tools are essential for catching bugs and vulnerabilities. We evaluated the top tools on 309 PRs in our RevEval benchmark
Methodology
We developed a fully automated evaluation system to assess agentic coding systems objectively and reproducibly. The framework consists of three components: orchestration, backend smoke tests, and UI smoke tests.
For CLI-based agents, all three components are executed sequentially without human intervention. Tasks are injected, agents run autonomously, and results are computer-graded end-to-end.
For AI Code Editors, orchestration requires submitting tasks manually through the IDE. However, execution remains one-shot: the task is sent once, the agent operates without guidance, and only after completion are standardized smoke tests executed. No mid-run corrections or hints are provided. The task is to send to the IDE agent and then run the smoke tests.
Editor Versions (Late February, 2026)
- Cursor 2.5.25
- Kiro Code: 0.10.32
- Antigravity: 1.18.4
- Roo code: 3.50.0
- Replit: February 20, 2026
- Windsurf: 1.9552.25
CLI Versions (Mid February, 2026)
- Opencode: v1.2.10
- Cline: v3.41
- Aider: v0.86.0
- Gemini CLI: v0.29.0
- Forge: v1.28.0
- Codex: 0.104.0
- Goose: v1.25.0
- Claude Code: v2.1.62
- Kiro CLI: 1.26.0
1. Orchestration
Per agent × task:
- Workspace reset
- Prompt injected as TASK.md
- Agent-specific launch script
- Timeout watchdog applied
- Metrics captured:
- exit code
- duration
- backend presence
- frontend presence
- token usage
Dependency fairness policy
To prevent over-penalizing minor packaging errors, we automatically install commonly omitted runtime dependencies:
- bcrypt < 4.1
- python-multipart
- email-validator
- greenlet
Missing one library line in requirements.txt is treated as a packaging oversight, not a behavioral failure.
If the system still fails after compatibility bootstrapping, it is penalized normally.
2. Backend smoke benchmark
Each task includes:
- Canonical YAML scenario contract
- Base environment configuration
Execution model
- Behavior-first validation
- Infra readiness checks
- Happy path execution
- Negative validation (400/403/409)
- State transition verification
Both adaptive and strict modes are executed:
- Adaptive: behavior works even if route naming differs
- Strict: requires contract discipline and proper OpenAPI discovery
Backend score formula
- infra_score = ready_tasks / total_tasks
- behavior_score = 0.7 x adaptive + 0.3 x strict performance
- backend_overall = infra_score × behavior_score
3. UI smoke benchmark
Web evaluation consists of 8 steps:
- Backend preflight
- Frontend render
- Login form visibility
- Login submission
- 2xx response
- Auth signal
- Post-login behavior
- No runtime crash
We compute:
step_pass_rate = passed / (passed + failed + blocked)
And derive:
- ui_infra_score
- ui_behavior_score
- ui_overall_score
Integrity reports must return VALID for ranking inclusion.
4. Final aggregation
Final score:
0.7 × backend_overall + 0.3 × ui_overall
Backend receives higher weight because backend logic failures invalidate frontend success.
Cost reporting
Cost reporting differs across tools. Some editors provide dollar usage, others report token counts, and some use credit systems.
For token-based tools, we estimated cost using reported input/output tokens and the model’s published pricing. For credit-based tools, we converted consumed credits into approximate dollar values based on their credit pricing.
These figures are approximate and reflect only the benchmark execution cost.
For more on AI coding tools:
You can read our other benchmarks about AI coding tools:
- Top AI Website Generators Benchmarked
- Screenshot-to-Code Benchmark
- The Best AI Code Editor: Cursor vs. Windsurf
FAQ
AI coding benchmarks are standardized tests designed to evaluate and compare the performance of artificial intelligence systems in coding tasks.
Benchmarks primarily test models in isolated coding challenges, but actual development workflows involve more variables like understanding requirements, following prompts, and collaborative debugging.
Large language models (LLMs) are commonly used for code generation tasks due to their ability to learn complex patterns and relationships in code. Code LLMs are harder to train and deploy for inference than natural language LLMs due to the autoregressive nature of the transformer-based generation algorithm. Different models have different strengths and weaknesses in code generation tasks, and the ideal approach may be to leverage multiple models.
When most code is AI-generated, the quality of AI coding assistants will be critical.
Evaluation metrics for code generation tasks include code correctness, functionality, readability, and performance. Evaluation environments can be simulated or real-world and may involve compiling and running generated code in multiple programming languages. The evaluation process involves three stages: initial review, final review, and quality control, with a team of internal independent auditors reviewing a percentage of the tasks.
- Has 20 years of experience as a white-hat hacker and development guru, with extensive expertise in programming languages and server architectures.
- Is an advisor to C-level executives and board members of corporations with high-traffic and mission-critical technology operations like payment infrastructure.
- Has extensive business acumen alongside his technical expertise.
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