GPT-5.2 is a capable general-purpose coding model with the broadest language coverage and best function-calling support of any LLM. Here's how it performs on real development tasks and where alternatives pull ahead.
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GPT-5.2 is a strong, reliable choice for everyday software development. It covers more programming languages than any competitor, produces clean code on the first pass, and its function-calling capabilities make it the best model for tool-augmented coding workflows. It falls short of Claude Sonnet 4.5 on large multi-file refactors and trails DeepSeek V3 on pure algorithmic challenges, but for most production coding tasks it delivers consistent, dependable results.
GPT-5.2 supports more programming languages than any other frontier model, including niche languages like Elixir, Haskell, and COBOL. If your stack is uncommon, GPT-5.2 is often the only model that produces idiomatic code.
GPT-5.2's structured function-calling API is the most mature in the industry. It reliably generates valid tool invocations, making it ideal for agentic coding workflows that interact with linters, test runners, and deployment tools.
When you need code generation to produce JSON schemas, typed interfaces, or configuration files, GPT-5.2 follows output format instructions more consistently than competitors, reducing the need for post-processing.
GPT-5.2 excels at turning plain-English specifications into working code. It infers intent accurately and asks fewer clarifying questions, which speeds up prototyping and spec-driven development.
When tasked with multi-file refactors across a large codebase, GPT-5.2 sometimes loses track of cross-file dependencies. Claude Sonnet 4.5's 200K context window handles these scenarios more reliably.
On competition-level algorithmic problems and complex data structure implementations, DeepSeek V3 consistently outperforms GPT-5.2 at a lower cost.
GPT-5.2 is one of the more expensive frontier models. For high-volume code generation tasks, DeepSeek V3 or Gemini 3 Flash can deliver comparable quality at a fraction of the price.
Use GPT-5.2's function-calling mode to integrate with your CI/CD pipeline, letting it run tests and fix failures in a loop.
Provide explicit type signatures or interface definitions in your prompt to get more accurate code on the first attempt.
For large refactors, break the task into file-level chunks rather than sending the entire codebase in one prompt.
Pair GPT-5.2 with LLMWise Compare mode to benchmark it against Claude and DeepSeek on your actual codebase before committing.
Use system prompts to specify your team's coding conventions, linting rules, and preferred patterns for more consistent output.
How GPT-5.2 stacks up for coding workloads based on practical evaluation.
Claude Sonnet 4.5
Compare both models for coding on LLMWise
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