Top picks for Code Completion (2026)
Inline IDE-style autocomplete that has to feel instant. Ranked from 335 live models on the OpenRouter catalog, weighted for low latency, low cost, context window.
| # | Model | Score | In / 1M | Out / 1M | Context | |
|---|---|---|---|---|---|---|
| 1 | Meta: Llama 4 Maverickmeta-llama/llama-4-maverick | 132 | $0.15 | $0.60 | 1,048,576 | Details → |
| 2 | Google: Gemini 2.5 Flashgoogle/gemini-2.5-flash | 132 | $0.30 | $2.50 | 1,048,576 | Details → |
| 3 | OpenAI: GPT-4.1 Nanoopenai/gpt-4.1-nano | 132 | $0.10 | $0.40 | 1,047,576 | Details → |
| 4 | Xiaomi: MiMo-V2.5xiaomi/mimo-v2.5 | 131 | $0.14 | $0.28 | 1,048,576 | Details → |
| 5 | Qwen: Qwen3.5-Flashqwen/qwen3.5-flash-02-23 | 131 | $0.07 | $0.26 | 1,000,000 | Details → |
| 6 | Google: Gemini 2.5 Flash Lite Preview 09-2025google/gemini-2.5-flash-lite-preview-09-2025 | 131 | $0.10 | $0.40 | 1,048,576 | Details → |
| 7 | OpenAI: GPT-5 Nanoopenai/gpt-5-nano | 131 | $0.05 | $0.40 | 400,000 | Details → |
| 8 | Google: Gemini 2.5 Flash Litegoogle/gemini-2.5-flash-lite | 131 | $0.10 | $0.40 | 1,048,576 | Details → |
| 9 | OpenAI: GPT-4.1 Miniopenai/gpt-4.1-mini | 131 | $0.40 | $1.60 | 1,047,576 | Details → |
| 10 | DeepSeek: DeepSeek V4 Flashdeepseek/deepseek-v4-flash | 131 | $0.10 | $0.20 | 1,048,576 | Details → |
| 11 | MiniMax: MiniMax M3minimax/minimax-m3 | 131 | $0.30 | $1.20 | 1,048,576 | Details → |
| 12 | Google: Gemini 3.1 Flash Litegoogle/gemini-3.1-flash-lite | 131 | $0.25 | $1.50 | 1,048,576 | Details → |
| 13 | Qwen: Qwen3.6 Flashqwen/qwen3.6-flash | 131 | $0.19 | $1.12 | 1,000,000 | Details → |
| 14 | OpenAI: GPT-5.4 Nanoopenai/gpt-5.4-nano | 131 | $0.20 | $1.25 | 400,000 | Details → |
| 15 | Google: Gemini 3.1 Flash Lite Previewgoogle/gemini-3.1-flash-lite-preview | 131 | $0.25 | $1.50 | 1,048,576 | Details → |
How we ranked these
For Code Completion, we weight models on low latency, low cost, context window. Scores combine each model's public specs with independent benchmark results (Aider Polyglot coding scores, Artificial Analysis intelligence/coding/agentic indices) and live pricing. See full methodology →
About Code Completion
Code completion is inline autocomplete that predicts and suggests the next tokens, methods, or code blocks as you type in an IDE or editor. You need it when you want to reduce typing friction, catch syntax errors early, and maintain flow without breaking context. A good model understands language semantics, respects your project's style and imports, and returns suggestions in under 100ms. Poor models hallucinate invalid syntax, suggest outdated APIs, or lag noticeably-both kill adoption. The main tradeoff is latency: local models run fast but lack context depth, while cloud models are smarter but add network delay.
When to use: Use this when you're writing code in a text editor or IDE and want AI to intelligently suggest what you should type next, saving you keystrokes and helping you write faster without leaving your development environment.
Common questions
Which AI models are best for real-time code completion?
GitHub Copilot (built on Codex/GPT-4) and Codeium are industry leaders for latency and accuracy. For local-only deployment, Starcoder and Llama-Code offer reasonable quality at smaller model sizes, though they're slower than cloud-based systems. The choice depends on whether you prioritize speed (cloud) or privacy (local).
How much does latency matter for code completion, and what's acceptable?
Latency under 100ms feels instant; anything over 500ms breaks typing flow and becomes annoying. Network round-trip time is the biggest factor, which is why many developers prefer locally-run completions or edge-cached models, even if they're slightly less accurate than full cloud inference.