Asking "is GPT 5.5 good at coding?" is the wrong question. The right question is: for which coding tasks does it produce reliable, useful output, and when should you reach for a different tool? The answer is more granular than the marketing makes it sound, and the practical patterns matter more than the benchmark numbers.
This article is the practitioner's view, after using these models extensively for production engineering work.
Where It Wins
1. Implementing a clear function from a clear spec. Given a function signature, types, and a few sentences of behaviour, the model produces solid implementations consistently. The lift over earlier versions is mostly in handling subtle edge cases without explicit prompting.
"""
Implement deduplicateBy<T, K>(items: T[], key: (item: T) => K): T[]
- Returns items in input order
- For duplicates, keeps the first occurrence
- Uses Map for O(n) performance
- Handles undefined keys (treats as a single bucket)
"""
The model produces correct, idiomatic code with appropriate error handling. For utility functions, leaf-level CRUD handlers, and other well-bounded coding tasks, this is reliable production output.
2. Idiomatic translation between languages and frameworks. Python to TypeScript. Express to Hono. Class components to hooks. The model is excellent at preserving behaviour while adopting the target language's idioms.
3. Boilerplate generation. Test scaffolds, API client wrappers, type definitions from JSON examples, configuration files. the kind of code where the structure is mechanical but tedious. Big productivity multiplier.
4. Code review on isolated changes. Show it a diff and the surrounding context, ask for review. It catches a meaningful fraction of bugs, suggests reasonable refactors, and writes professional review comments. Best as a first-pass reviewer, not a sole reviewer.
5. Documentation generation. README sections, function-level docstrings, API reference from code. Quality is consistently usable.
6. Debugging well-isolated issues. "This function returns the wrong value for input X. Here's the function and the actual vs expected output." The model traces through the logic and identifies the bug accurately for non-trivial cases.
Where It Doesn't Win Reliably
1. Whole-codebase reasoning. Asking it to "refactor the auth system" given a 30-file codebase exceeds what 5.5 reliably handles. Even when it fits in context, the model struggles to track all the implications. Reasoning-tier models or models with very long context (like Claude Opus 4.7's 1M context) handle this category meaningfully better.
2. Subtle concurrency bugs. Race conditions, deadlocks, lost updates. The model often produces plausible-looking explanations that miss the actual issue. Treat any concurrency analysis as a starting point for human review, not a conclusion.
3. Performance optimisation that depends on profile data. Without seeing the actual profile output, the model suggests the usual optimisations rather than the ones that matter for your specific bottleneck. Provide profile data; the analysis improves dramatically.
4. Production-grade SQL on complex schemas. Simple queries are fine. Multi-join queries with subtle WHERE clauses, window functions, or CTEs against an unfamiliar schema produce queries that look right but have edge-case bugs. Always read the generated SQL before running.
5. Security-critical code. The model occasionally produces code with subtle security issues. string concatenation in SQL, weak crypto, unsafe deserialization. Always review security-sensitive output with a real security-focused tool or reviewer in the loop.
6. Code in niche languages or frameworks. Anything not heavily represented in training data. niche industry frameworks, internal DSLs, very recent library versions. produces lower-quality output. The model will confidently invent APIs that don't exist.
The Production Patterns
Three patterns that produce the best results for coding work:
1. Provide context aggressively. The single biggest quality lever. Include the file the change is happening in, the related types/interfaces, sample call sites, and the test framework conventions. The model uses all of it.
<file path="src/users/service.ts">
[full file content]
</file>
<test_pattern>
[example of how tests are structured in this codebase]
</test_pattern>
<task>
Add a method to UserService that...
</task>
2. Use tool use for code execution. When the task is "write code that does X," let the model write and run the code in a sandbox. It catches its own bugs by executing tests, and the iteration loop is dramatically more reliable than one-shot generation.
3. Use structured output for code reviews. When generating review comments, ask for them as JSON. file path, line number, severity, message. The output drops directly into a PR review tool without parsing.
const response = await openai.chat.completions.create({
model: "gpt-5.5",
response_format: {
type: "json_schema",
json_schema: {
name: "review",
schema: {
type: "object",
properties: {
comments: {
type: "array",
items: {
type: "object",
properties: {
path: { type: "string" },
line: { type: "number" },
severity: { type: "string", enum: ["error", "warning", "suggestion"] },
message: { type: "string" },
},
required: ["path", "line", "severity", "message"],
},
},
},
},
strict: true,
},
},
messages: [/* diff context */],
});
When to Choose Other Models
For coding work specifically, my current routing:
- GPT 5.5 for the bulk of standard coding tasks. implementations from specs, translations, boilerplate, isolated debugging.
- Claude Opus 4.7 for whole-codebase reasoning, complex refactors, and tasks where I want to load 100+ files in context.
- Reasoning-tier models for hard algorithmic problems, performance optimisation with profile data, complex bug diagnosis.
- Small-tier models for trivial tasks (renames, type imports, simple edits) where the cost of GPT 5.5 isn't justified.
A common misroute: defaulting to the most capable model for every coding task. The smaller model is faster, cheaper, and sufficient for most edits. Reserve the heavy hitters for the hard problems.
The Honest Assessment
GPT 5.5 is a productive coding assistant for engineers who already know what they want and can review what's produced. It's not a replacement for engineering judgment. it accelerates the typing, not the thinking.
The teams who get the most value from it:
- Treat its output as a senior-engineer first draft, not a finished product
- Maintain rigorous code review even (especially) for AI-generated code
- Use it for the categories where it's reliable and reach for other tools where it isn't
- Track which categories of bug slip through review when AI-assisted, and adjust workflows accordingly
The teams who get the least value:
- Treat its output as authoritative
- Skip code review on AI-generated changes
- Use it for everything regardless of task fit
That difference. between a tool used well and a tool used badly. is the same for AI coding assistants as it was for any productivity tool that came before. Pick the right tasks, review the output, ship the result.