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BenchLM
Write or improve something · Marketing copy and business content

Best AI model for copywriting — September 2026

On BenchLM's public evidence, MAI-Thinking-1 has the highest instruction following score estimate among the models that meet this page's constraints (95.4). No measured conversion or brand-voice outcome is available. Compare brief adherence and test your actual brief.

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The shortlist for copywriting

Constraints on this page: a builder choosing by accuracy, hosted processing allowed, any price, ordinary input size. Change any of them under Refine. A small score gap does not establish a reliably better model.

Compare MAI-Thinking-1 vs Grok 4.3Full instruction following leaderboard

  1. 01MAI-Thinking-1Best fit

    Microsoft · Proprietary

    95.4

    Instruction following score estimate

    • Highest instruction following estimate among models that meet every stated constraint
    • 256K context

    Model page and evidence

  2. 02Grok 4.3

    xAI · Proprietary

    93.2

    Instruction following score estimate

    • Estimate 93.2 on the same instruction following evidence
    • $1.75 for the stated workload
    • 1M context

    Model page and evidenceCompare with MAI-Thinking-1

  3. 03GLM-5.1

    Z.AI · Open Weight

    92.4

    Instruction following score estimate

    • Estimate 92.4 on the same instruction following evidence
    • $2.28 for the stated workload
    • 203K context
    • Open weights, so it can run on your own hardware

    Model page and evidenceCompare with MAI-Thinking-1

  4. 04GPT-5.2-Codex

    OpenAI · Proprietary

    92.4

    Instruction following score estimate

    • Estimate 92.4 on the same instruction following evidence
    • $4.55 for the stated workload
    • 400K context
    • Provider notice: retired on the first-party API around July 23, 2026. Provider-named replacement: GPT-5.6 Sol (equivalence not asserted). Receipt.

    Model page and evidenceCompare with MAI-Thinking-1

  5. 05MiMo-V2.5-Pro

    Xiaomi · Proprietary

    92.4

    Instruction following score estimate

    • Estimate 92.4 on the same instruction following evidence
    • 1M context

    Model page and evidenceCompare with MAI-Thinking-1

Refine for your situation

Each link opens the selector with one answer changed. The address carries the answers, so your version is as shareable as this page.

A decision model maps your sentence to the selector’s questions. The shortlist comes from public evidence only, and nothing is stored.

What this shortlist rests on

The instruction following surface. The category score is a weighted average of these public benchmarks.

What to verify before choosing

  • No measured conversion or brand-voice outcome is available. Compare brief adherence and test your actual brief.
  • 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 AI model is best for copywriting?

On BenchLM's public evidence, MAI-Thinking-1 by Microsoft has the highest instruction following score estimate among models that meet the page's default constraints (95.4). Ordered by task score under the stated constraints. No measured conversion or brand-voice outcome is available. Compare brief adherence and test your actual brief.

What are the alternatives to MAI-Thinking-1 for copywriting?

Grok 4.3 (93.2), GLM-5.1 (92.4), GPT-5.2-Codex (92.4), MiMo-V2.5-Pro (92.4) 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 ai model for copywriting?

The page runs the LLM Selector with fixed answers: a builder choosing by accuracy, hosted processing allowed, any price, ordinary input size. The selector uses the instruction following 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-v5.5-2026-09-04. Read the methodology and benchmark confidence pages for how scores and verification statuses are produced.

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