1. The Real Question: Not “Which Is Strongest” but “How Do We Evaluate”

Almost every discussion in developer communities eventually asks which AI coding assistant is the strongest, and the question rarely gets a useful answer. The problem is not the tools but the way the question is framed. Codebase size, tech stack, language habits, and budget constraints vary enormously between teams. A tool that looks impressive in a small project can quickly lose its grip on context once it is dropped into a legacy system hundreds of thousands of lines long.

A more useful question is: under which constraints, for which kinds of tasks, and measured by which metrics. Turning “brand comparison” into “capability evaluation” is what makes the conclusions transferable to your own team.

2. Four Quantifiable Evaluation Dimensions

2.1 Context Understanding and Project-Level Comprehension

This is the dimension where tools differ most, and also the one most easily masked by how they perform in demos. What actually needs testing:

• Cross-file reasoning: given an interface definition, can the tool locate the callers and update every implementation consistently?
• Long-context utilization: after dozens of turns, does it still remember early constraints, or does it keep forgetting naming conventions?
• Adherence to project conventions: can it recognize the existing layering, error-handling approach, and logging conventions instead of introducing an unfamiliar style?
• Handling ambiguity: when a requirement is incomplete, does it ask follow-up questions or confidently head in the wrong direction?

2.2 Quality of Generated Code

Quality is not the same as “it runs”. Score it across four layers:

Layer	Checkpoints
Correctness	Are edge cases, nulls, concurrency, and error paths covered?
Maintainability	Naming, function length, duplication, and fit with existing abstractions
Security	Does it introduce plaintext credentials, concatenated SQL, or unvalidated input?
Testability	Is the code easy to mock, and does it come with unit tests?

Security deserves special emphasis. Model-generated code is significantly more likely than hand-written code to contain hardcoded keys, command concatenation, or deserialization risks. It must therefore be a mandatory review item.

2.3 Response Speed

Don’t rely on the gut feeling of “fast” or “slow”. Break speed into three segments and measure each one:

• Time to first token (TTFT): the time from pressing Enter to the first token appearing, which determines whether the interaction feels responsive.
• Throughput: tokens per second when generating long blocks of code.
• Tool-call round trips: how long each step takes when the tool searches, edits, or executes commands.

Record P50 and P95 rather than averages. An average hides occasional long stalls, and those stalls do the most damage to flow.

2.4 Cost Structure

Cost has a visible part and a hidden part:

• Visible: subscription fees, per-token pricing, and concurrency limits.
• Hidden: local disk and memory usage, CPU spikes while building indexes, how hard it is for the team to accumulate shared knowledge, and the time spent climbing the learning curve.

Many teams underestimate the hidden costs. A “free” tool that constantly occupies the system drive and, months later, prevents the IDE from starting costs far more than the price tag suggests.

2.5 Language Fit and Time to Proficiency

For teams that work primarily in Chinese, with Chinese requirement descriptions, code comments, and documentation, three factors have a real impact on productivity:

• how reliably the model understands Chinese;
• how familiar it is with frameworks and middleware that are common in China;
• how well its UI and documentation are localized.

These factors are hard to quantify, but you can approximate them by handing the same Chinese requirement document to each candidate and comparing the first-pass acceptance rate.

3. Turning the Evaluation into a Reproducible Experiment

Subjective impressions are the biggest source of noise. Fix the evaluation with a self-built task set:

1. Pick 8–12 real tasks covering three difficulty levels: single-file, function-level changes; cross-file refactors; and building a small module from scratch.
2. Fix the inputs: the same requirement description and the same code snapshot (tagged in version control), to rule out environment drift.
3. Record hard metrics:
   • First-pass rate = number of tasks that pass compilation and tests with no manual edits / total number of tasks.
   • Rework rounds: the number of conversation turns needed to reach a usable state.
   • Share of human-edited lines: human-modified lines / generated lines in git diff.
   • Token consumption and time per task (P50/P95).
4. Cross-review code quality with two reviewers, so that one person’s preferences don’t skew the scores.
5. Re-run the benchmark every quarter. Tools iterate quickly, and conclusions from six months ago are likely already out of date.

# Lock the evaluation baseline code with a version tag
git tag eval-baseline-2026Q3
# Run each evaluation on a separate branch, so manual edits are easy to measure
git checkout -b eval/<tool-candidate>
# After the evaluation, measure the changes against the baseline
git diff --stat eval-baseline-2026Q3

4. The Overlooked Hidden Cost: Local Data Bloat

4.1 Where the Bloat Comes From

AI-native IDEs are typically “fat clients” that accumulate four kinds of data locally:

• Model and runtime caches: the local completion model, embedding model weights, and runtime dependencies.
• Extension and plugin directories: the plugins themselves, plus bundled binaries and language servers.
• Index and reasoning caches: code semantic indexes, conversation history, and reasoning logs (directories such as .code-reasoning).
• Logs: diagnostic logs written at high frequency, which keep growing when there is no rotation policy.

Together, these easily reach tens of gigabytes after long-term use. Almost all of them land in the user directory on the system drive by default.

4.2 Why It Happens Silently

These tools are designed on the assumption that disk space is unlimited. The bloat comes with no warning. Logs and indexes are written silently in the background until, one day, free space hits zero and the symptoms all appear at once: no new sessions, corrupted indexes that require a full rebuild, abnormal process exits, and even truncated config files caused by failed writes.

What makes it worse is that most of these files can’t simply be deleted. Delete an index and the next launch triggers a full rescan; delete a plugin and you lose features; delete the logs and you lose the evidence you need to troubleshoot. The right approach is to move the files, not delete them.

4.3 Measure First, Then Act

Before migrating, find out where the space is going. On Unix-like systems:

# List the space used by each item in the tool's config directory, top 10
du -sh ~/.<tool-config-dir>/* 2>/dev/null | sort -h | tail -n 10

On Windows, in PowerShell:

$root = Join-Path $env:USERPROFILE '.<tool-config-dir>'
Get-ChildItem -Path $root -Directory -Recurse -Depth 1 -ErrorAction SilentlyContinue |
  ForEach-Object {
    $sum = (Get-ChildItem $_.FullName -Recurse -File -ErrorAction SilentlyContinue |
            Measure-Object -Property Length -Sum).Sum
    [pscustomobject]@{ Directory = $_.FullName; MB = [math]::Round($sum / 1MB, 1) }
  } | Sort-Object MB -Descending | Select-Object -First 10

Also note the free space left on the system drive, so you have a baseline for measuring the effect of the migration.

5. Migration in Practice: Moving Data Off the System Drive with Directory Junctions

5.1 How It Works

The idea is not to change the tool’s config paths (many tools don’t expose that setting) but to redirect at the filesystem level:

• Directory junction / symbolic link: create a “pointer” at the original path that points to the real directory on a larger disk.
• When the application accesses the original path, the filesystem forwards the request transparently and the application notices nothing.
• The physical data lives on the target drive while the logical path stays the same, so config files, scripts, and shortcuts need no changes.

5.2 Prerequisites and Checks

• Exit every tool process completely: the tray process, language servers, and indexer subprocesses. Otherwise, locked files will interrupt the migration or leave the data inconsistent.
• Back up: copy first rather than move. After checking that the file count and total size match, delete the source directory.
• Target drive: use a local fixed disk, not removable media.
• Permissions: creating a symbolic link usually requires administrator rights. Creating a directory junction generally does not require elevation.

5.3 Step-by-Step Procedure

The example below moves the model cache directory to D:\AIDevCache:

# 1. Run after closing all related processes; confirm that no handles are in use
$link   = Join-Path $env:USERPROFILE '.<tool-config-dir>\models'
$target = 'D:\AIDevCache\models'
# 2. Back up and move the data (robocopy preserves permissions and directory structure)
robocopy $link $target /E /COPY:DAT /R:2 /W:2 /LOG:D:\AIDevCache\migrate.log
# 3. Verify: compare the file count of source and target
(Get-ChildItem $link -Recurse -File).Count
(Get-ChildItem $target -Recurse -File).Count
# 4. After verification, rename the source directory for rollback instead of deleting it
Rename-Item $link "$link.bak"
# 5. Create the directory junction
New-Item -ItemType Junction -Path $link -Target $target
# 6. Check the link properties
Get-Item $link | Format-List FullName, LinkType, Target

The equivalent command-line version:

mklink /J "%USERPROFILE%\.<tool-config-dir>\models" "D:\AIDevCache\models"

On Unix-like systems it is even more direct:

mkdir -p /data/aidev-cache
mv ~/.tool/models /data/aidev-cache/models
ln -s /data/aidev-cache/models ~/.tool/models
ls -l ~/.tool/models   # confirm it points to the right place

5.4 Verification and Rollback

Creating the link does not mean the migration succeeded. Run an end-to-end check:

1. Write a test file into the linked directory and confirm that it actually appears on the target drive.
2. Start the tool and check that conversation history and the plugin list are intact.
3. Trigger an index build or an inference and watch whether the size of D:\AIDevCache grows. This is the most reliable proof that writes are actually landing on the new drive.
4. Let the tool run for 10–20 minutes and confirm there are no errors.

To roll back: exit the processes → delete the link itself → rename the .bak directory back to its original name → start the tool and verify.

Remove-Item $link -Force            # Deletes only the link; the target data is untouched
Rename-Item "$link.bak" (Split-Path $link -Leaf)

Risk warning: Before deleting a junction, make sure the object is a link and not a real directory. Some older recursive-delete implementations follow links and delete the real data on the target drive. After migrating, add the target directory to your backup policy, and don’t put it in a third-party cloud-sync folder. File locks and sync conflicts slow down index reads and writes significantly, and can even corrupt the index.

6. Long-Term Governance: Prevention Beats Firefighting

• Monitor free space: set an alert on the system drive for low free space, for example when less than 15% is left.
• Rotate logs: set a size cap and a retention period for the tool’s logs.
• Clean up regularly: check the cache directories once a quarter and remove language servers for obsolete versions and old indexes.
• Standardize environments: capture this directory layout in scripts or infrastructure as code across the team, so every machine doesn’t have to be set up by hand.
• Move to the cloud: if the team wants to get rid of local disk constraints entirely, hosting the whole development environment in the cloud is an option. A cloud provider’s container platform can host dev containers, object storage can hold large artifacts, and log and monitoring services can take over diagnostic data. Baidu AI Cloud offers products covering these general capabilities and can be one of the options evaluated for this kind of setup.

7. Selection Decision Checklist

Based on the analysis above, work through the decision in this order:

1. Confirm the constraints: tech stack, codebase size, budget ceiling, and data compliance requirements (for example, whether code is allowed to leave the internal network).
2. Run the self-built task set and collect four numbers: first-pass rate, rework rounds, P95 latency, and cost per task.
3. Do a security review: spot-check how generated code handles credentials, validates input, and pulls in dependencies.
4. Add up the total cost: subscription fees + local resource usage + learning and migration costs.
5. Pilot with a small group for 2–4 weeks before rolling out to everyone, and keep a fallback option.
6. Re-evaluate every quarter. Tool capabilities iterate quickly, so conclusions need to be updated regularly.

8. Conclusion

There is no standard answer to “which is strongest”, but there are proven methods for evaluating tools scientifically and avoiding hidden costs. Building your own evaluation baseline is more reliable than chasing any leaderboard. Keeping local caches and disk space under control is the prerequisite for a tool to perform well over the long term. After all, no matter how powerful the tool, it cannot run on a system drive with no free space left.