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You cannot honestly declare a winner from the information available here. A coding agent “understood the assignment” only when it satisfies the same requirements in the same repository, runs the required checks, avoids unauthorized changes, and leaves an auditable trail. Product descriptions and public benchmark scores can explain why Claude Code, Google Antigravity, or a local model might behave differently, but they do not prove which one will finish your project correctly. The defensible answer requires a controlled, same-project run with recorded prompts, permissions, model settings, commands, diffs, tests, and costs.
This guide shows how to run that comparison and how to interpret the result without confusing a polished demo, a large diff, or a vendor benchmark with genuine task understanding.
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What does it mean for a coding AI to “understand the assignment”?
Understanding is broader than producing code that looks plausible. An agent has understood the assignment when its final repository state meets the written requirements, preserves unrelated behavior, handles stated edge cases, and provides evidence that it checked its work.
Score each run against the same questions:
- Requirement coverage: Did every explicit acceptance criterion get implemented?
- Repository comprehension: Did the agent inspect the files, configuration, tests, and interfaces that actually govern the change?
- Plan quality: Did it identify dependencies and risks before editing, or start changing files blindly?
- Correctness: Do tests and manual checks pass, including negative and boundary cases?
- Scope control: Did it avoid unrelated refactors, generated-file churn, new dependencies, and formatting changes?
- Security and dependency choices: Did it validate input, preserve authentication boundaries, and choose maintained packages with suitable licenses?
- Recovery: After a failed command or bad assumption, did it diagnose the cause and repair the work?
- Communication: Did it ask a useful clarification when the specification was genuinely ambiguous?
A passing test suite alone is not sufficient: tests may miss the requirement you care about. Conversely, a small diff is not automatically better if it omits a necessary migration or test.
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How do I make a Claude Code, Antigravity, and local-LLM comparison fair?
Fairness comes from controlling everything except the agent. Start from one immutable repository snapshot and give each contender the same task statement, environment, permissions, and stopping rule.
Freeze the starting state
- Create a fresh clone or worktree from a named commit. Record the commit hash, operating-system version, language/runtime versions, package-manager version, and available test commands.
- Remove uncommitted changes, cached build artifacts, private notes, and hidden hints. If the project needs secrets, provide identical test-only values to every run.
- Prepare a task brief containing the requirements, non-goals, acceptance tests, constraints, and any allowed dependency policy. Do not improve the brief between agents.
Equalize access and budgets
Use the same repository visibility, network policy, shell permissions, browser access, environment variables, and time limit. If one agent can install packages or browse documentation while another cannot, record that difference rather than calling the result a model comparison.
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Use one canonical prompt
Give each system the same wording. Ask it to inspect the repository before editing, state a short plan, implement only the requested change, run the relevant checks, and summarize remaining risks. Do not provide one agent with a file path that another would have to discover unless that path is part of the assignment.
What are the practical differences between the three approaches?
The systems differ in workflow as well as model capability, so a comparison should separate harness features from reasoning quality.
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| System | Documented workflow emphasis | What to record in your run | Important caveat |
|---|---|---|---|
| Google Antigravity | Its documentation describes a shared agent harness with synchronous editing and tool execution, asynchronous background subagents, scheduled cron sidecars, automated verification loops, parallel subagents, visual artifacts, workspace and Git worktree isolation, sandboxed Chromium automation, MCP clients, and scheduled tasks. | Which model and mode were selected; number of subagents; worktree or workspace used; browser and MCP permissions; verification reports; and every merged or discarded change. | Parallelism and automation can increase coverage, but they can also enlarge the diff or obscure which agent made a decision. |
| Claude Code | Anthropic describes effort as controlling the total work Claude performs, including files read, tools used, and steps taken before it checks back. Its guidance positions Sonnet for most coding, Opus for difficult debugging, large cross-cutting refactors, and architecture, and Haiku for quick lookups, simple edits, and high-volume scripted runs. | Exact model name, effort level, tool permissions, files inspected, commands run, and when the agent checked back for confirmation. | Changing effort changes the experiment: a higher setting may read more context and verify more, so keep it fixed or report separate runs. |
| Local LLM | Behavior depends on the model and the serving stack you operate rather than on a single standardized coding-agent product. | Model and revision, quantization, context window, backend, GPU/CPU and memory, temperature, concurrency, retrieval method, tool adapter, latency, and power or hosting cost. | “Local” is not one capability level. Two installations with the same model name can produce different results because of quantization, context limits, or tool integration. |
These are workflow characteristics, not a verdict. A model with excellent code generation can still fail if its harness never runs the project’s tests; a slower system can win if it verifies a subtle migration that a faster system misses.
How should I run the same coding project through each agent?
Use a reproducible sequence and preserve the evidence after every run.
- Tag the baseline. Save the commit hash and a directory listing. Run the project’s existing tests once so you know whether the baseline is already green.
- Launch an isolated workspace. Use a separate Git worktree or clone for each contender. Never let one agent read another’s uncommitted diff.
- Paste the identical brief. Include requirements, non-goals, acceptance checks, and the instruction to ask before making an irreversible or out-of-scope change.
- Capture the plan. Save the initial response, files the agent says it will inspect, and any clarification question. A useful question about an ambiguous API is evidence of judgment, not failure.
- Observe repository discovery. Record which files, tests, configuration, and documentation the agent actually reads. Note skipped files that later prove relevant.
- Let it implement within the budget. Do not silently repair commands or edit files yourself. If a command fails, allow the agent the same recovery opportunity you would give the others.
- Run verification. Require the documented unit, integration, type, lint, build, and security checks that apply. Add targeted edge-case checks from the brief, but do not invent a new test only after seeing one agent’s solution.
- Freeze the result. Stop at the agreed limit, save the complete diff, command transcript, test output, token or cost usage, elapsed time, and final explanation.
- Review independently. A human reviewer who did not operate the agent should compare each result against the acceptance checklist and inspect risky code paths.
- Repeat when variance matters. If sampling or tool timing makes outcomes unstable, run multiple trials with fresh workspaces and report the distribution rather than selecting the luckiest attempt.
What should the scorecard measure?
A compact scorecard prevents a dramatic demo from dominating the decision. Use pass/fail for hard requirements and numeric or ordinal ratings only where reviewers can explain their judgment.
| Category | Evidence to attach | Suggested decision rule |
|---|---|---|
| Acceptance criteria | Checklist linked to files, tests, or screenshots | Any mandatory miss is a failed run until corrected. |
| Tests and verification | Exact commands, exit codes, logs, and coverage or type-check results where available | Prefer a run that proves the change, not one that merely claims it. |
| Diff quality | Stat, changed-file list, and review comments | Penalize unrelated churn, unnecessary dependencies, and generated artifacts. |
| Edge cases | Inputs, expected behavior, and observed output | Require every edge case named in the brief; do not award credit for unrequested features. |
| Security and maintenance | Dependency manifest changes, permission use, validation, and error handling | Reject shortcuts that create an exploitable or unmaintainable result. |
| Efficiency | Elapsed time, tool calls, tokens, cost, and retries | Compare only within the same budget and hardware assumptions. |
| Recovery | Failure transcript and subsequent patch | Reward diagnosis that fixes the root cause without widening scope. |
Keep raw artifacts even if you publish only a summary. The claim that an agent “understood” should be reproducible from the diff and logs, not dependent on a reviewer’s memory of a chat transcript.
What do public benchmarks tell me—and what can’t they tell me?
Benchmarks provide context about capabilities under a defined protocol, but they cannot answer which system understood your repository. Android Bench’s September 17, 2026 archive reports pass rate, completion rate, average latency, and average cost for multiple coding-agent entries, including Claude Code and Antigravity SDK model-agent combinations. Those projects and protocols are different from a private repository comparison, so treat the numbers as background rather than a ranking for your task.
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Anthropic’s Sonnet 5.5 report gives another example of why protocol details matter. It reports the following vendor-provided results:
| Benchmark | Sonnet 5.5 | Sonnet 5 | Opus 5.5 | Other reported result |
|---|---|---|---|---|
| Terminal-Bench 4.0 | 70.6% | 10.3% | 66.4% | Not stated |
| FrontierCode 1.1 (Main) | 46.2% at Max effort | 42.4% | 54.4% | GPT-6 Sol: 49.3% |
| CursorBench 4.0 | 55.5% | 34.1% | 57.8% | Not stated |
These are vendor-reported benchmark results, and the differing tasks, harnesses, effort settings, and scoring rules mean they do not establish a winner on your project. Record the benchmark protocol alongside any number you cite; otherwise a percentage looks more universal than it is.
Why might one agent go off track while another stays in scope?
Off-track behavior usually comes from a mismatch between task boundaries and the agent’s available actions, not from a single “intelligence” score.
Insufficient context
A low effort setting, short context window, or aggressive retrieval filter can omit a test, configuration file, or interface contract. The result may compile while violating an assumption elsewhere. Check the file-read log before blaming the implementation.
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Background subagents, browser automation, MCP clients, and unrestricted network access can solve more problems, but they also create more opportunities for unsolicited changes. Limit permissions to what the brief requires and inspect each worktree or generated artifact.
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Weak stopping criteria
“Make it better” invites refactors. State what must change and what must not change, then stop when acceptance checks pass. A larger patch is not evidence of a deeper understanding.
Unverified confidence
An agent may summarize a test without running it or may run only a fast subset. Require command transcripts and exit codes. If a check is unavailable, label it unverified rather than treating the claim as proof.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should I choose after the experiment?
Choose the system that delivers the highest requirement coverage and verification quality within your real constraints, not the one with the most impressive prose. For a terminal-heavy workflow with strict human control, Claude Code’s configurable effort and direct tool use may fit well. For work that benefits from parallel agents, isolated workspaces, browser checks, or scheduled verification, Antigravity’s documented harness may reduce orchestration work. A local LLM can be the right choice when code must remain on your hardware, predictable per-run cost matters, or you can accept the operational work of serving and integrating it.
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If two systems tie on correctness, use latency, cost, privacy, reproducibility, and maintenance burden as tie-breakers. If a local run fails because of context or hardware limits, report that configuration-specific result; do not generalize it to every local model.
What would make a “only one understood” claim credible?
Publish the starting commit, exact prompt, model and effort settings, permissions, budget, raw diff summaries, test commands and outputs, edge-case results, and the reason each failed requirement failed. Explain whether the winner passed on its first attempt or after allowed recovery. Without those records, “only one understood the assignment” is a story about impressions rather than an auditable technical conclusion.
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Frequently Asked Questions
Can I compare hosted agents with a local model if they use different tools?
Yes, but call it a workflow comparison and disclose the tool differences. A second, stricter comparison can equalize tools by giving every system the same shell wrapper, test command, and file-access policy.
Should I give an agent a higher effort setting when it fails?
Treat that as a new trial, not a silent repair of the original result. Anthropic says effort changes the amount of repository reading, tool use, and checking, so record the new setting and budget separately.
How many repeated runs are enough for a reliable result?
There is no universal number; repeat until the outcome is stable enough for your decision and report the spread. Deterministic prompts with fixed sampling may need fewer trials than stochastic local inference with variable tool timing.
What is the most important artifact to publish from the comparison?
Publish the complete acceptance checklist beside the final diff and verification transcript. That pairing lets readers distinguish a genuinely complete implementation from a confident explanation or a visually large patch.
Quick Recap
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