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Best local LLM for coding — November 2026

On BenchLM's public evidence, DeepSeek V4.1 Flash 1017 has the highest coding score estimate among the models that meet this page's constraints (63.3). Open weights are only the first requirement. Hardware, quantization, license, and tool access need verification.

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The shortlist for local LLM for coding

Constraints on this page: a builder choosing by accuracy, running on their own hardware, ordinary input size. Change any of them under Refine. A small score gap does not establish a reliably better model.

Compare DeepSeek V4.1 Flash 1017 vs Kimi K3Full coding leaderboard

  1. 01DeepSeek V4.1 Flash 1017Best fit

    DeepSeek · Open Weight

    63.3

    Coding score estimate

    • Highest coding estimate among candidates passing the measured filters
    • 1M context
    • Open weights; check hardware before treating local use as feasible

    Estimate sources: LMArena Code Overall, Vals AI coding-task composite

    Model page and evidence

  2. 02Kimi K3

    Moonshot AI · Open Weight

    58.9

    Coding score estimate

    • Estimate 58.9 on the same coding evidence
    • 1.05M context
    • Open weights; check hardware before treating local use as feasible

    Estimate sources: LMArena Code Overall, Artificial Analysis, LiveBench Coding, Vals AI coding-task composite

    Model page and evidenceCompare with DeepSeek V4.1 Flash 1017

  3. 03Ling 3.1 Flash

    InclusionAI · Open Weight

    58.3

    Coding score estimate

    • Estimate 58.3 on the same coding evidence
    • 262K context
    • Open weights; check hardware before treating local use as feasible

    Model page and evidenceCompare with DeepSeek V4.1 Flash 1017

  4. 04MiMo-V2.6-Pro

    Xiaomi · Open Weight

    57.6

    Coding score estimate

    • Estimate 57.6 on the same coding evidence
    • 1M context
    • Open weights; check hardware before treating local use as feasible

    Estimate sources: LMArena Code Overall

    Model page and evidenceCompare with DeepSeek V4.1 Flash 1017

  5. 05Hy4 preview

    Tencent · Open Weight

    56.6

    Coding score estimate

    • Estimate 56.6 on the same coding evidence
    • 1M context
    • Open weights; check hardware before treating local use as feasible

    Estimate sources: LMArena Code Overall, Vals AI coding-task composite

    Model page and evidenceCompare with DeepSeek V4.1 Flash 1017

Refine for your situation

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What this shortlist rests on

The coding surface. Each model’s estimate names its own sources above; these are the public weighted benchmarks for the category.

What to verify before choosing

  • Open weights are only the first requirement. Hardware, quantization, license, and tool access need verification.
  • Composite scores are estimates. A small score gap does not establish a reliably better model.

Try three representative examples of your own work. Compare errors, time, cost, and the tools available in your actual setup.

Questions

Which is the best local LLM for coding?

On BenchLM's public evidence, DeepSeek V4.1 Flash 1017 by DeepSeek has the highest coding score estimate among models that meet the page's default constraints (63.3). Ordered by task score under the stated constraints. Open weights are only the first requirement. Hardware, quantization, license, and tool access need verification.

What are the alternatives to DeepSeek V4.1 Flash 1017 for local LLM for coding?

Kimi K3 (58.9), Ling 3.1 Flash (58.3), MiMo-V2.6-Pro (57.6), Hy4 preview (56.6) follow on the same evidence. A small gap does not establish a reliably better model; compare them on three representative examples of your own work.

How does BenchLM pick the best local llm for coding?

The page runs the LLM Selector with fixed answers: a builder choosing by accuracy, running on their own hardware, ordinary input size. The selector uses the coding evidence surface, filters by the stated constraints, and orders by that evidence. It never adds a hidden fit score or a bonus for open weights or reasoning style.

Can I change the constraints?

Yes. Every link under "Refine" opens the selector with one answer changed, and the address carries the answers so a result can be shared or reopened against the current dataset.

Method: bench-align-v5.8-2026-09-30. Read the methodology and benchmark confidence pages for how scores and verification statuses are produced.

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