Choose a Model for Code Translation in 8 Steps

Choose a code translation model by the behavior its output preserves, not by how convincing the rewritten code looks. For a small utility, prioritize a clean translation with minimal intervention. For a service migration, require evidence across dependencies, failure paths, and operational constraints. Use this walkthrough to make the choice against your own codebase.

1. Define what must survive translation

Write a short acceptance contract before comparing models. Specify the source and target language versions, supported inputs, public interfaces, error behavior, and dependency restrictions. Separate required compatibility from intentional changes. If the migration should replace exceptions with explicit error values, say so; otherwise, ask for equivalent failure behavior rather than an architectural redesign.

2. Build a representative trial set

Select real modules that exercise different translation problems: a pure transformation, a dependency-heavy component, and a stateful or asynchronous workflow. Include a difficult boundary case from your issue history. Reserve some tests for evaluation rather than including every expected answer in the prompt. Start with a reviewable slice instead of submitting the entire repository.

3. Compare two candidates without assuming a winner

For an initial trial, you could compare Claude Sonnet 5.5 and DeepSeek V4 Pro. Treat these as candidates, not a claimed performance ranking. Give both the same source files, instructions, tool access, and repair budget. Choose the winner separately for each language pair and workload; do not automatically reuse a result from one migration for another.

CodeRelay Hub provides a pay-as-you-go endpoint for switching candidates without rebuilding your integration. Keep the surrounding harness fixed so the experiment measures translation quality rather than differences in orchestration.

4. Require an explicit semantic review

Ask each candidate to identify behavior that needs special treatment before generating the translation. Require it to distinguish confirmed mappings from unresolved assumptions. For example, when targeting JavaScript, explicitly review large integer handling: Number only guarantees safe integer representation through Number.MAX_SAFE_INTEGER, while BigInt supports larger integers. Do not accept an unexplained numeric type substitution. ([developer.mozilla.org](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Number/MAX_SAFE_INTEGER?utm_source=openai))

• Specify numeric ranges, rounding rules, and serialization expectations.
• Define how missing values, empty values, and errors should map.
• Require an explanation of cancellation, resource cleanup, and shared state.
• Ask for dependency mappings and flag any invented APIs for verification.

5. Use a translation prompt with clear boundaries

Provide the source module, relevant interfaces, dependency versions, build command, and a few representative tests. Request target-language code, required dependency changes, and a concise compatibility report. Explicitly prohibit unrelated refactoring. Ask the model to flag missing context instead of silently choosing business rules.

A useful instruction is: Preserve observable behavior, use idiomatic target-language constructs where compatible, and list every intentional deviation. Keep this instruction unchanged across candidates.

6. Evaluate execution before presentation

Compile or type-check the translation, then run unit and integration tests. Where practical, execute the source and translated implementations against identical inputs and compare outputs, errors, and side effects. Define any permitted normalization before running the comparison. For time-dependent behavior, use controlled clocks; for network interactions, use recorded fixtures or isolated test services.

Inspect the generated tests too. Do not let a candidate redefine expected behavior merely to make its own implementation pass. Keep the evaluation environment isolated from production credentials and data.

7. Count repair work and total cost

Record first-attempt acceptance, failures after a fixed repair allowance, reviewer time, response latency, and actual API charges. Calculate cost per accepted module, including unsuccessful attempts. Reject candidates that miss mandatory correctness requirements before comparing convenience or cost. Among those that pass, favor the one that produces the least total work for your team.

8. Turn the result into a routing rule

Route only the task categories that passed your trial to the selected candidate. Escalate unfamiliar dependencies, unresolved semantic questions, or repeated failures to another candidate or human review. Save the prompt, model identifier, settings, and test results with each migration. Rerun the trial when changing models, language versions, or major dependencies.

Open a free workspace to start your translation trial.