Best Free LLM APIs for Coding

Not all LLMs are equally good at coding. Here's what separates a coding model from a general-purpose one:

• Context window — The single most important spec for coding. Modern codebases easily exceed 50K tokens. A model with less than 32K context will struggle with multi-file edits, code review, or understanding project structure. Look for at least 128K tokens; 256K+ is ideal for monorepo work.
• Fill-in-the-Middle (FIM) — A specialized training objective where the model learns to fill a gap between prefix and suffix code. Essential for inline code completion in IDEs. Codestral and DeepSeek Coder variants are trained with FIM.
• Tool calling / function calling — Required for agentic coding workflows: "find all files that import X, then refactor them to use Y." Without tool calling, the model can only suggest code, not execute actions. Most OpenAI-compatible endpoints support tool calling if the underlying model does.
• Instruction following — Coding requires precise, unambiguous outputs. Models that drift or hallucinate will introduce bugs. DeepSeek V4 and Qwen3 score particularly well on instruction-following benchmarks.
• Max output tokens — Generating a full file or multiple functions in one shot requires high output limits. 8K output is the practical minimum; 16K+ lets the model generate entire modules at once.

Your pick depends on how you code:

• Using Claude Code or Cursor? → Prioritize context window and tool calling. Gemini 2.5 Flash (1M ctx) or DeepSeek V4 (256K) let the agent see your whole project. Both support tool calling via OpenAI-compatible endpoints.
• Inline completion in VS Code / JetBrains? → Look for FIM support. Codestral (Mistral) is purpose-built for this. DeepSeek Coder variants also support FIM.
• Code review / PR review? → Large context is critical — the diff + surrounding code + review guidelines all need to fit in one prompt. Gemini 2.5 Flash's 1M context handles this with room to spare.
• Learning to code? → Prioritize helpfulness and explanation quality. Qwen3 and Llama 3.3 70B are known for clear, educational code explanations.
• Rate limit sensitive? → NVIDIA NIM has 40 RPM with no daily cap, ideal for heavy coding sessions. Groq has 30 RPM / 14,400 RPD — enough for most solo developers.

Try models in the Playground with a real coding task before committing — the same benchmark scores don't always match your specific language or framework.