Code Models
AI-powered coding assistants for development
Code-Generierungs-Modelle für Autovervollständigung, Review und Refactors
Code-Generierungs-Modelle sind große Sprachmodelle, die speziell auf Quellcode trainiert oder feinjustiert wurden. Sie treiben IDE-Autovervollständigung, PR-Review, automatisches Refactoring, Test-Generierung und sprachübergreifende Übersetzung an. Du greifst zu einem Code-Modell — statt zu einem allgemeinen Textmodell —, wenn du höhere Korrektheit bei Programmieraufgaben und strukturierte Ausgaben (Diffs, JSON) willst, die gut mit Entwickler-Tooling zusammenspielen.
21 models available
Codestral
Mistral's code-specialized model. Optimized for code generation, completion, and understanding across 80+ languages.
Code Llama 13B Instruct
Meta's 13B Code Llama tuned for instruction following. A faster mid-size option for code generation and completion, supporting infilling for inserting code at a cursor position. Served on Replicate per call.
Code Llama 34B Instruct
Meta's 34B Code Llama tuned for instruction following. A balance of size and quality for code generation, completion, and explanation, with strong coverage of Python, JavaScript, and other common languages. Runs on Replicate per call.
Code Llama 70B Instruct
Meta's largest Code Llama, a 70B Llama-2 derivative specialized for programming and tuned to follow instructions in chat form. Handles code generation, completion, and explanation across common languages. Served on Replicate as a per-call endpoint.
Code Llama 7B Instruct
Meta's smallest Code Llama at 7B parameters, tuned for instruction following. The cheapest and fastest member of the family for quick code generation, completion, and infilling. Served on Replicate per call.
CodeGen 350M Mono
350M autoregressive code generation model from Salesforce, the smallest of the original CodeGen family. The mono variant was further trained on Python so it is well suited for short Python completions and program synthesis from a natural-language or code prompt.
DeepSeek Coder 1.3B Instruct
1.3B instruction-tuned code model from DeepSeek, trained on 2 trillion tokens of code and natural language across 87 languages with a 16k context window. One of the strongest tiny coders for its size, handling generation, completion and short coding instructions.
DeepSeek Coder 33B Instruct (GGUF)
Quantized GGUF build of DeepSeek's 33B code model, trained on roughly 2T tokens that are about 87 percent code. Designed for repository-level completion and project-aware generation thanks to a 16k context window. Runs on Replicate as a per-call endpoint.
DeepSeek Coder V2
DeepSeek's specialized coding model. Excellent at code generation, debugging, and explanation.
Granite Code 20B
IBM Granite 20B Code Instruct. Larger Granite code model balancing quality and inference cost for enterprise CI/CD code-review automation.
Granite Code 8B
IBM Granite 8B Code Instruct. Trained on permissively-licensed code, strong on multi-language code completion and instruction-following.
Grok Build 0.1
xAI's Grok coding-focused model. Tuned for code generation and software development tasks with a 256k token context window for working over large codebases.
Magicoder S CL 7B
UIUC Magicoder S CL 7B. CodeLlama-7B fine-tuned with OSS-Instruct synthetic data. Strong HumanEval Plus and MBPP Plus performance per parameter.
Phind CodeLlama 34B v2
Phind CodeLlama 34B v2. Highly tuned CodeLlama variant focused on retrieval-augmented developer assistant workflows.
Qwen2.5-Coder 32B Instruct
Alibaba's largest open Qwen2.5-Coder model. Trained on a code-heavy corpus, it matches or beats much larger general models on code generation and repair benchmarks like HumanEval and MBPP, and supports over 40 programming languages with fill-in-the-middle completion.
Qwen2.5-Coder 7B Instruct
The 7B instruct member of Alibaba's Qwen2.5-Coder family. A lighter, faster option for code completion, generation, and bug fixing across 40+ languages, with a 128k context and fill-in-the-middle support. Good price-to-quality balance for everyday coding tasks.
Replit Code v1 3B
Replit's 3B code-completion model, trained on a permissively licensed code subset of the Stack across 20 programming languages. Built for low-latency autocomplete rather than chat. Served on Replicate per call.
Replit Code v1.5 3B
3B code completion model from Replit trained on roughly 1 trillion tokens of permissively licensed code across 30 programming languages, with a 4k context window. Designed for autocomplete-style code generation and fill-in-the-middle.
Stable Code Instruct 3B
Instruction-tuned 3B code model from Stability AI, fine-tuned from stable-code-3b for chat-style coding tasks. Handles code generation, explanation and fix-up across multiple languages and was competitive with larger code models on benchmarks at release.
StarCoder2 15B
BigCode StarCoder2 15B code-generation flagship. Trained on 4T tokens of Stack v2 data with grouped-query attention and 16k context.
WizardCoder 33B
WizardLM WizardCoder 33B v1.1. Evol-Instruct fine-tune of DeepSeek-Coder-33B with strong code-generation benchmark performance.
Top code models picks
Hand-picked across four common criteria — resolved against the live catalog so the picks track price and performance changes.
Mistral's code-specialized model. Optimized for code generation, completion, and understanding across 80+ languages.
Learn more350M autoregressive code generation model from Salesforce, the smallest of the original CodeGen family. The mono variant was further trained on Python so it is well suited for short Python completions and program synthesis from a natural-language or code prompt.
Learn moreMistral's code-specialized model. Optimized for code generation, completion, and understanding across 80+ languages.
Learn moreMistral's code-specialized model. Optimized for code generation, completion, and understanding across 80+ languages.
Learn moreDie Preisgestaltung bei Code-Generierung folgt demselben Per-Token-Modell wie allgemeiner Text. Flagship-Code-Modelle (GPT-5 Codex, Claude 4.6 Sonnet, Codestral) kosten 1–10 € pro Million Input-Tokens; Budget-Stufen (Codestral Mamba, DeepSeek Coder, Qwen Coder) kosten 0,05–0,50 € pro Million. Eine einzelne IDE-Autocomplete-Anfrage läuft selten über wenige Tausend Input-Tokens, der Aufruf-Preis liegt also bei Bruchteilen eines Cents. Die Rechnungen wachsen, wenn du Agenten ausspielst, die sich pro Aufgabe Dutzende Male selbst neu prompten.
Das Trade-off-Dreieck heißt Korrektheit, Geschwindigkeit und Kontext. Flagships lösen schwierigere Probleme und folgen Projektkonventionen zuverlässiger, antworten aber mit 30–80 Tokens/Sekunde — innerhalb einer engen Autocomplete-Schleife fühlt sich das langsam an. Schnelle Budget-Modelle (Codestral Mamba, GPT-5 Mini) streamen mit 200+ Tokens/Sekunde und fühlen sich im Editor nativ an. Für Batch-Aufgaben (refactor ein ganzes Repo, generiere Tests für fünfzig Dateien) gewinnt Flagship-Korrektheit. Für enge Autocomplete-Schleifen gewinnt die schnelle Stufe.
Achte auf datei-übergreifenden Kontext: Die meisten Autocomplete-Schleifen senden nur die aktuelle Datei. Für echtes codebasis-bewusstes Refactoring brauchst du eine Retrieval-Schicht, die verwandte Dateien in den Prompt zieht. Tools wie Cursor und Continue machen das automatisch; wenn du es selbst baust, embedde zuerst die Codebase und hol pro Anfrage die fünf bis zehn relevantesten Dateien.
Achte auf Lizenzkontamination: Einige Open-Weights-Code-Modelle wurden ausschließlich auf permissiv lizenziertem Code trainiert, andere haben GPL-Code mit unklaren Weiterverbreitungs-Bedingungen mitgenommen. Wenn du generierten Code in einem Closed-Source-Produkt ausspielst, bevorzuge kommerzielle Modelle mit expliziten Lizenz-Garantien für den Code.
Die Top-Picks oben decken das Korrektheits-Flagship, das günstigste Arbeitspferd, das Long-Context-Modell und die schnellste Autocomplete-Option ab.
Popular use cases
Common patterns built with code models on Railwail.
Frequently asked questions
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