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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallThere is no defensible universal winner. A coding agent is faster only if it reaches a verified, usable result sooner on the work you care about; it is smarter only relative to the kinds of tasks you need it to solve. Published scores measure different models, harnesses and task sets. For a meaningful choice, compare agents on the same representative work in your own repository, using the same tests and timing rules.
Which coding agent is faster?
Measure end-to-end time to verified completion, not just how quickly a model generates tokens. The clock should include service delays, model inference, tool runs, context construction, and any developer review or corrections needed to get a usable result. OpenAI describes its Codex loop as spending time in API services, model inference, and client-side work such as running tools and building context (OpenAI, “Speeding up agentic workflows with WebSockets in the Responses API”).
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That distinction matters because a faster model response can still lead to a slower overall task if the agent makes more mistakes, retries commands, or needs more supervision. Conversely, an agent that takes longer to produce its first answer may finish sooner if it gets the change right and passes verification with fewer interventions.
What vendor speed claims do—and do not—show
OpenAI says GPT-5.3-Codex is 25% faster than GPT-5.2-Codex. That is a vendor-reported comparison between those named models; it is not evidence that a complete Codex setup is faster than every competing agent on every repository (OpenAI, “Introducing GPT-5.3-Codex”).
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OpenAI also reports a 20% reduction in end-to-end serving costs and more than 15% improvement in token-generation efficiency for system optimizations described in its GPT-5.6 article. These are serving and inference-stack measures, not user-level completion-time results or a ranking of rival agents (OpenAI, “How GPT-5.6 fuses frontier intelligence with frontier efficiency”).
In a separate WebSocket-mode report, OpenAI says alpha users saw up to 40% workflow-latency improvements; it cites Cline multi-file workflows as 39% faster and OpenAI models in Cursor as up to 30% faster. These are attributed implementation claims, not head-to-head comparisons between coding agents (OpenAI, “Speeding up agentic workflows with WebSockets in the Responses API”).
Which coding agent is smarter?
“Smarter” is best treated as task performance: does the agent produce a correct, acceptable change for the work at hand? A score on one benchmark does not settle that question for every task. Tests differ in what they ask agents to do, how they verify success, and which models and agent harnesses they use.
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Benchmarks measure different work
CCBench evaluates real-world tasks in codebases under 10,000 lines that are not part of the models’ training data. Its results page, last updated February 12, 2026, reports about 180 tasks: Codex CLI with GPT-5.2-codex at 75.4% and Claude Code with Opus 4.6 at 72.7%. The same page says Gemini 3 Pro Preview exceeded a 20-minute timeout on about 25% of tasks (CCBench, “The coding benchmark”).
Those CCBench results should not be read as a direct substitute for SWE-Bench scores. CCBench uses its own task and verification setup, including private user-submission codebases and official CodeCrafters tests. A score answers how a particular model-and-harness configuration performed on that evaluation, not how it will perform on a different benchmark or your codebase.
Task type can change the apparent leader
A 2026 study analyzing 7,156 pull requests across five agents found that acceptance varied substantially by task category: documentation changes were accepted at 82.1%, compared with 66.1% for new features. The authors report Claude Code led on documentation (92.3%) and features (72.6%), Cursor led on fixes (80.4%), and OpenAI Codex was consistently strong across nine categories, ranging from 59.6% to 88.6% (“Comparing AI Coding Agents: A Task-Stratified Analysis of Pull Request Acceptance,” 2026).
This is observational evidence from that study’s dataset, not a guaranteed ordering for another team. It does show why an aggregate score can conceal useful differences: the agent that looks strongest for fixes may not lead on documentation or feature work.
Best Value
Is a faster coding agent actually better?
Only if its speed improves the outcome you value. A fast first response is not useful if it fails tests, introduces regressions, or leaves a developer with more repair work. For most teams, the practical comparison is time to a correct, reviewed result, alongside quality, cost and supervision effort—not raw token-generation speed in isolation.
- Completion time: elapsed time until the change meets the team’s verification and review criteria, including tool runs and required human fixes.
- Task success: pass rate or acceptance rate, reported separately for meaningful task types such as fixes, documentation and features.
- Quality: test results, regressions and review findings under the same standards used for normal work.
- Cost: total usage for successful and failed attempts, including retries; specify whether the comparison uses subscription credits or API units.
- Supervision: how often a developer must redirect the agent or take over.
- Fit: repository size and languages, terminal or IDE workflow, permissions and deployment constraints.
How do I compare coding agents on my own codebase?
Use tasks your team actually encounters, and give each agent the same repository state, instructions, allowed tools and verification criteria. This helps separate differences in the model or agent setup from differences in the work being tested.
- Choose a representative task set. Include the task categories your team needs, rather than relying on one easy or unusually difficult example.
- Hold the starting conditions constant. Use the same repository snapshot, task description, permissions and tool access for each run. Record the model and agent harness, since a result belongs to that combination.
- Set the finish line before starting. Define which tests, review checks or other criteria count as verified completion. Apply the same rules to every run.
- Record elapsed time and interventions. Start at the same point in the workflow and stop only when the result meets the finish line. Include tool execution, retries and required human corrections; note redirections separately.
- Track quality and cost by task type. Record pass or acceptance rates, regressions and total usage, including failed attempts. Do not combine unlike task categories into a single score without showing the breakdown.
- Repeat enough tasks to see a pattern. A single run can be shaped by task difficulty or a chance failure. Compare results across the same task set before making a purchasing or workflow decision.
AWS’s sample agent-cost-bench framework is one option for side-by-side cost, duration and quality comparisons across CLIs and models on real repositories, with test or custom-scoring options. Its presence does not make its results universal: use verification and scoring that reflect your own team’s definition of acceptable work.
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