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BenchLM
Work across languages · Practice or learn a language

Best AI model for language learning — September 2026

On BenchLM's public evidence, Qwen3.7 Max has the highest multilingual score estimate among the models that meet this page's constraints (100.0). Knowledge across languages does not measure tutoring or spoken conversation quality.

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The shortlist for language learning

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 Qwen3.7 Max vs Claude Opus 4.5Full multilingual leaderboard

  1. 01Qwen3.7 MaxBest fit

    Alibaba · Proprietary

    100.0

    Multilingual score estimate

    • Highest multilingual estimate among models that meet every stated constraint
    • 1M context

    Model page and evidence

  2. 02Claude Opus 4.5

    Anthropic · Proprietary

    82.9

    Multilingual score estimate

    • Estimate 82.9 on the same multilingual evidence
    • $10.00 for the stated workload
    • 200K context

    Model page and evidenceCompare with Qwen3.7 Max

  3. 03Qwen3.7 Plus

    Alibaba · Proprietary

    78.9

    Multilingual score estimate

    • Estimate 78.9 on the same multilingual evidence
    • 1M context

    Model page and evidenceCompare with Qwen3.7 Max

  4. 04Qwen3.5 397B

    Alibaba · Open Weight

    69.7

    Multilingual score estimate

    • Estimate 69.7 on the same multilingual evidence
    • $1.32 for the stated workload
    • 128K context
    • Open weights, so it can run on your own hardware

    Model page and evidenceCompare with Qwen3.7 Max

  5. 05Qwen3.6 Plus

    Alibaba · Proprietary

    69.7

    Multilingual score estimate

    • Estimate 69.7 on the same multilingual evidence
    • 1M context

    Model page and evidenceCompare with Qwen3.7 Max

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

What to verify before choosing

  • Knowledge across languages does not measure tutoring or spoken conversation quality.
  • 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 language learning?

On BenchLM's public evidence, Qwen3.7 Max by Alibaba has the highest multilingual score estimate among models that meet the page's default constraints (100.0). Ordered by task score under the stated constraints. Knowledge across languages does not measure tutoring or spoken conversation quality.

What are the alternatives to Qwen3.7 Max for language learning?

Claude Opus 4.5 (82.9), Qwen3.7 Plus (78.9), Qwen3.5 397B (69.7), Qwen3.6 Plus (69.7) 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 language learning?

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 multilingual 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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