Choose a base model by testing candidate checkpoints on the coding work you need to improve—not by picking the model with the biggest name or benchmark score. Compare task performance on held-out examples, checkpoint type, license, context limits, fine-tuning access, compute requirements, and deployment cost. There is no universal best model without knowing your task and operating constraints.
First establish a strong prompt-only baseline and reliable evaluation checks. OpenAI’s fine-tuning guidance puts evaluation before training: fine-tuning is worth considering when you can define and measure the behavior you want, not as a substitute for giving a model changing or private information as context.
Define the coding task before comparing models
“Code” is not one evaluation target. A model that writes a short function from a prompt may not be suited to continuing code at a cursor, explaining an unfamiliar module, fixing a bug, or changing a multi-file repository. Decide what input the model will receive and what a successful output looks like in production.
Match the evaluation to the job
- Code completion or fill-in-the-middle: Evaluate the same continuation format, surrounding code, and editing context your product will use. Instruction-to-code scores alone do not establish autocomplete quality.
- Code generation from instructions: Test the target languages, libraries, conventions, and constraints; check whether outputs compile and pass relevant tests.
- Explanation or review: Judge correctness and usefulness against known code and expected findings, rather than treating generated code benchmarks as a proxy.
- Repair or repository maintenance: Use representative bugs or issues with the same repository context, tools, and constraints available at deployment. HumanEval and MBPP are small Python code-generation benchmarks; they do not establish repository-level competence.
Fine-tuning is most defensible when you can create examples of the desired input/output behavior and test whether the model learned it. If the needed information changes frequently—such as current internal documentation—provide it at inference time rather than expecting training to keep it current.
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Compare a shortlist against the constraints that matter
Record the exact checkpoint and revision, not just the model family. The same family can include different parameter sizes, training formats, context limits, licenses, and supported fine-tuning methods. Compare candidates using one evaluation protocol and the infrastructure you can actually use.
| Decision factor | What to check | Why it matters |
|---|---|---|
| Task and domain fit | Target task, programming languages, frameworks, and code style; whether the model supports the input format you need. | A general code-generation result may not predict completion, repair, or repository performance. |
| Held-out task performance | Functional correctness, compilation or test pass rate, instruction adherence, and failure types on representative examples. | A benchmark score is useful only to the extent that its tasks and checks resemble your workload. |
| Checkpoint type and data format | Whether the checkpoint is pretrained or instruction-tuned, and whether its expected format matches your training examples. | The format affects how directly the model can handle your target behavior. |
| License and usage terms | The exact repository’s license, revision, and any model-specific conditions for your intended use. | Do not assume deployment or commercial rights from a family name. |
| Training and inference access | Whether your provider or environment supports the checkpoint and your intended tuning method; context limits and serving options. | A model you cannot train or deploy under your constraints is not a viable candidate. |
| Compute and operating cost | Memory, throughput, latency, and cost for the planned training recipe and production traffic. | Parameter count alone does not determine the full cost of training and serving. |
| Maintenance | How you will update, evaluate, and serve the model as code, dependencies, and requirements change. | A fine-tuned checkpoint adds a model artifact and evaluation process to maintain. |
For a concrete license example, the Qwen2.5-Coder-32B-Instruct repository lists Apache-2.0. That repository-level fact is not a license guarantee for every Qwen checkpoint; check the exact revision and current terms. AWS’s JumpStart guide lists multiple Code Llama variants, but provider availability and supported workflows can change.
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Choose pretrained or instruction-tuned based on the target behavior
Neither checkpoint type is a universal winner. Include both in your comparison when feasible, using examples formatted for the behavior you want to teach.
| Checkpoint type | Potential fit | What to verify |
|---|---|---|
| Pretrained | A plausible starting point when the goal is code continuation or completion. | Whether it handles the intended prompt and editing format, and whether the training data can teach the needed behavior. |
| Instruction-tuned | A plausible starting point for instruction-response tasks, especially when conversational task formatting is already useful. | Whether its existing instruction behavior helps or conflicts with the target format and examples. |
An ICLR 2025 code-evaluation study selected instruction-tuned models for higher zero-shot compatibility and more accurate evaluation. That was the study’s rationale for its setup, not proof that instruction-tuned models always fine-tune better.
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Build an evaluation that can detect real improvement
OpenAI’s supervised fine-tuning guide recommends setting up evals before training and comparing results with a holdout set whose diversity is roughly similar to the collected task data. Keep the holdout separate from training examples so it measures generalization rather than memorization.
- Collect representative cases. Include the languages, task types, input lengths, edge cases, and failure modes that matter in production.
- Define success checks. Use execution-based tests where appropriate, alongside checks for instruction adherence and output format. For repository work, evaluate the repository task with the relevant context and tools.
- Run the prompt-only baseline. Use the candidate’s original checkpoint with the intended inference setup. This shows whether fine-tuning beats a capable non-training approach.
- Fine-tune and rerun the same holdout. Keep prompts, decoding settings, tests, and other evaluation conditions fixed so the comparison is meaningful.
- Track operational measures too. Record latency and cost alongside correctness; a small quality gain may not justify a much more expensive serving setup.
HumanEval and MBPP are common code-generation references, but neither alone settles a production choice. The ICLR 2025 paper describes using 164 HumanEval problems and 378 MBPP problems. EvalPlus describes HumanEval+ as expanding HumanEval’s test suite with 80 times more test cases; this improves coverage of those problems but does not make the benchmark a measure of every production coding task. Test-suite choice, decoding, harness, and task definition can change reported results, so record the full protocol and use checks suited to your application.
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Plan data and compute without treating rules of thumb as guarantees
OpenAI’s current supervised fine-tuning guide describes improvements with 50–100 examples and recommends starting with 50 well-crafted demonstrations, while noting that the suitable amount varies substantially by use case. Treat that as a provider’s practical starting suggestion—not a guarantee, a code-specific threshold, or a substitute for held-out evaluation.
Training feasibility depends on the actual recipe: model size, context length, precision, batch size, optimizer, and whether you use full fine-tuning or a parameter-efficient method. Estimate memory and runtime for the chosen checkpoint and method, then validate on the target environment. An ICLR 2025 experiment reported using four NVIDIA A100 GPUs; that describes the paper’s setup, not a minimum hardware recommendation.
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Verify access, context limits, and terms before committing
Check the provider or repository for the precise model ID, revision, supported fine-tuning methods, context limit, and deployment conditions. OpenAI’s fine-tuning best-practices documentation lists different context limits for different model IDs and warns that oversized examples are truncated at the end. Inspect example lengths and truncation behavior before training, especially when inputs include long files or repository context.
Provider status is also part of the decision. OpenAI’s model-optimization page, as reflected in documentation retrieved in 2026, says the company is winding down its fine-tuning platform: new users can no longer access it, while existing users may create jobs for the coming months. Treat that as time-sensitive platform information and confirm current access before choosing a provider-dependent workflow. AWS JumpStart documents multiple Code Llama variants, but its listing is not a promise that every variant or tuning method will remain available.
Use a practical selection rule
- Pick the candidate that passes your task-specific holdout best if quality is the primary constraint and its license, access, and cost are acceptable.
- Prefer a smaller or cheaper candidate when its measured quality is close enough and its latency or operating cost better fits the product.
- Do not fine-tune yet if a prompt-only baseline already meets the target, the desired behavior cannot be evaluated, or the main gap is missing up-to-date context rather than behavior.
- Keep more than one candidate in the running when trade-offs differ—for example, one model leads in correctness while another is materially easier to serve.
There is no current cross-provider leaderboard or single score in these sources that identifies the best checkpoint for every coding workload. The defensible choice is the checkpoint that improves your defined task on held-out tests and remains feasible under your actual license, compute, access, and deployment constraints.
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