Code Models

Models tuned for writing, explaining and reviewing code.

Available: 11of 26
Providers: 2
Price range: US$0.0030 – US$1.188per default run

Quick picks

/

11 models

15 models currently unavailable

Their pages stay online, but they can’t be run at the moment.

Code generation models for autocomplete, review, and refactors

Code-generation models are large language models trained or fine-tuned specifically on source code. They power IDE autocomplete, PR review, automated refactoring, test generation, and cross-language translation. Reach for a code model — over a general text model — when you want stronger correctness on programming tasks and structured outputs (diffs, JSON) that play well with developer tooling.

Pricing, trade-offs and pitfalls

Pricing in code generation follows the same per-token model as general text. On Railwail, flagship models such as Claude Sonnet 4.6 ($3.60) or GPT-5.4 ($3.00) cost a few dollars per million input tokens, while smaller code models such as Granite Code 8B start at $0.06 per million. A single IDE autocomplete request rarely runs more than a few thousand input tokens, so per-call cost is fractions of a cent. The bills grow when you ship agents that re-prompt themselves dozens of times per task.

The trade-off triangle is correctness, speed, and context. Flagships solve harder problems and follow project conventions more reliably but respond at 30-80 tokens/second, which feels slow inside a tight autocomplete loop. Fast budget models (Codestral Mamba, GPT-5 Mini) stream at 200+ tokens/second and feel native in the editor. For batch tasks (refactor a whole repo, generate tests for fifty files), flagship correctness wins. For tight autocomplete loops, fast tier wins.

Watch out for cross-file context: most autocomplete loops only send the current file. For real codebase-aware refactoring, you need a retrieval layer that pulls related files into the prompt. Tools like Cursor and Continue do this automatically; if you're rolling your own, embed the codebase first and retrieve the top 5-10 most relevant files per request.

Watch out for license contamination: a few open-weights code models were trained on permissively licensed code only; others swept GPL code with unclear redistribution terms. If you're shipping generated code in a closed-source product, prefer commercial models with explicit code-license guarantees.

Top picks above cover the most correct flagship, the cheapest workhorse, the longest-context model, and the fastest autocomplete option.

Typical tasks

IDE autocomplete and tab-completion
Pull-request review and feedback
Automated refactoring across files
Test generation
Code translation between languages
Documentation and comment generation

Guides by use case

AI Coding & Code Generation Models

Frequently asked questions

Which code model writes the most correct code?

GPT-5 and Claude 4.6 Sonnet currently lead on HumanEval+, SWE-bench, and Codeforces-style problem sets. For domain-specific languages (SQL, regex, infrastructure-as-code), specialized models sometimes outperform flagships on the narrow task while losing on general reasoning.

Which is cheapest?

On Railwail, Granite Code 8B ($0.06) and Granite Code 20B ($0.12 per million input tokens) are among the cheapest code models. Smaller open-weights models handle routine autocomplete and refactoring well; reach for a flagship when correctness matters more than cost.

What about codebase-aware context?

Most code models work on single-file context out of the box. For multi-file reasoning, you need a retrieval layer that embeds your repo and pulls in related files. Cursor and Continue.dev do this automatically; in your own agents, use an embedding model from /models/embedding to build the retriever.

Can I use these for autocomplete in my IDE?

Tools that let you set a custom OpenAI-compatible base URL can call Railwail's chat endpoint. Streaming is not supported yet, so autocomplete-style plugins that rely on streamed tokens may not work; chat and batch-style tasks do.

What programming languages do they support?

Flagship models handle 80+ languages with strong performance on the top 20 (Python, TypeScript, JavaScript, Java, Go, Rust, C++, C#, Ruby, PHP, Swift, Kotlin, SQL, Bash, etc.). Niche languages (Erlang, Elixir, Crystal, Zig) still work but with lower correctness — verify on your own snippets before integrating.

Can they generate tests?

Yes, and this is one of the best ROI use-cases today. Feed a function and ask for unit tests; the model produces 5-15 test cases including edge cases and error paths. Pair with a coverage tool to validate the suite before merging.

How is generated code licensed?

Commercial models grant unrestricted commercial use of the output. A few open-weights checkpoints trained on GPL-licensed code carry license-contamination ambiguity — the model card lists the training-data license disclosure. For closed-source products, prefer commercial models with explicit copyright indemnity.

Is there a JSON-mode for structured output?

The providers' own APIs support it, but the Railwail chat endpoint does not forward `response_format` yet. Ask for JSON in the prompt and validate the result; for multi-file edit plans, a schema with file paths and per-file diff actions described in the prompt gives the most reliable results.

Build with one API

Every available model through one OpenAI-compatible API. Prepaid credits in USD, no subscription.

Create account API docs