LLM·Dex
Newsletter
Use caseTop 6 picks

Best LLM for Code Completion in 2026

Inline autocomplete inside an IDE, latency, accuracy, and cost matter equally.

Updated

How we ranked

  • P99 latency under 250ms for inline suggestions
  • Fill-in-the-middle (FIM) capability
  • Accuracy on next-token completion in mid-function context
  • Cost per million tokens for high-volume editor traffic
  • On-prem / self-host availability for IP-sensitive teams

Read the full methodology for our sourcing and ranking standards.

Inline completion is a different beast from chat coding. The model has to feel instant, anything north of 250ms and your fingers outrun it. That makes raw quality less important than fast, accurate, fill-in-the-middle suggestions on the codebase you've actually shipped.

Open-weight, code-specialised models dominate here for a simple reason: you can host them on dedicated hardware and tune the serving stack for tail latency. Qwen-2.5-Coder, DeepSeek-V3, and Codestral all support FIM natively and run comfortably on a single H100, which is why every serious autocomplete product (and a lot of homebrew Cursor configs) uses one of them under the hood.

If you don't want to self-host, the small frontier models, Claude Haiku, GPT-5-mini, GPT-5-nano, are the next-best pick. They're slightly behind the specialists on FIM accuracy but make up for it with sheer breadth of training data and predictable APIs.

The ranking

  1. #1AlibabaOpen weights

    Qwen2.5-Coder-32B

    Open-weight code specialist, frequently the top open option for self-hosted code completion.

    Full specs
    Context
    128K tokens
    Output · 1M
    Pricing not published
    Modalities
    text

    Why it ranks here. Top open-weight coder. Apache-2.0. Tracked weakness: Coding-focused, not a general chat model.

  2. #2DeepSeekOpen weights

    DeepSeek-V3

    DeepSeek's flagship 671B-parameter MoE, frontier-level quality at a tiny fraction of frontier prices.

    Full specsTry DeepSeek-V3
    Context
    128K tokens
    Output · 1M
    $1.10 / 1M tokens
    Modalities
    text

    Why it ranks here. Frontier-level quality at open-weight prices. MIT license, clean commercial use. Tracked weakness: No native vision support.

  3. #3Anthropic

    Claude Haiku 4

    Anthropic's smallest 4-tier model, fast and cheap with the family's signature tone.

    Full specsTry Claude Haiku 4
    Context
    200K tokens
    Output · 1M
    Pricing not published
    Modalities
    text, vision

    Why it ranks here. Fast and cheap for an Anthropic model. Inherits Claude's sensible defaults. Tracked weakness: Quality gap visible on creative tasks.

  4. #4OpenAI

    GPT-5 mini

    GPT-5's mid-tier sibling, most of the quality at a fraction of the price, ideal for high-volume production workloads.

    Full specsTry GPT-5 mini
    Context
    400K tokens
    Output · 1M
    $2.00 / 1M tokens
    Modalities
    text, vision, audio

    Why it ranks here. Excellent price-quality ratio for production workloads. Fast first-token latency. Tracked weakness: Quality gap vs. flagship visible on hard reasoning.

  5. #5MistralOpen weights

    Codestral 2

    Mistral's code-specialized model, fast inline completion and strong fill-in-the-middle support.

    Full specs
    Context
    256K tokens
    Output · 1M
    $0.90 / 1M tokens
    Modalities
    text

    Why it ranks here. Fast inline completion. FIM support. Tracked weakness: Non-production license restricts commercial deployment.

  6. #6OpenAI

    GPT-5 nano

    OpenAI's smallest GPT-5 variant, built for ultra-low-cost classification, routing, and high-volume inference.

    Full specsTry GPT-5 nano
    Context
    400K tokens
    Output · 1M
    $0.40 / 1M tokens
    Modalities
    text, vision

    Why it ranks here. Lowest-cost OpenAI model with vision support. Fast P99 latency. Tracked weakness: Visible quality gap on open-ended tasks.

How to choose

Don't pick on the headline ranking alone. Run your top two picks on a representative sample of your own workload and compare. The numbers in this list are sound, but task-specific quality varies in ways no benchmark fully captures. The criteria above are the right axes to evaluate on, but the weighting depends on your stack.

  • Cost-sensitive workloads, start with the cheapest of the top three; escalate only if quality is the bottleneck.
  • Privacy-sensitive workloads, filter to open-weight picks above. They're labeled with a green badge.
  • Latency-sensitive workloads, see the Fastest LLMs list, which can override task-specific picks.

Frequently asked

  • What is the best model for code completion?
    Our #1 pick is Qwen2.5-Coder-32B from Alibaba. Open-weight code specialist, frequently the top open option for self-hosted code completion.
  • How are these rankings determined?
    We rank by the criteria listed at the top of this page: P99 latency under 250ms for inline suggestions; Fill-in-the-middle (FIM) capability; Accuracy on next-token completion in mid-function context. Where two models are close, we prefer the one with stronger production deployment evidence at the time of writing. Read the full methodology for our standards.
  • Qwen2.5-Coder-32B or DeepSeek-V3?
    Both are top-tier picks. Qwen2.5-Coder-32B edges ahead on the criteria most relevant to this task. DeepSeek-V3 is the strongest alternative, see the head-to-head comparison page for full deltas.
  • Are open-source models on this list?
    Yes where they're competitive. Each entry below shows whether the model ships open weights and under what license.
  • How often is this list updated?
    Weekly. New launches that affect the ranking get reflected within seven days. The "last updated" stamp at the top of the page reflects the most recent dataset commit.

Related guides

Friday digest

The week's AI launches, in your inbox.

One short email every Friday, new models, leaks, and quietly-shipped APIs you missed.