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Best AI assistant — September 2026

On BenchLM's public evidence, Claude Fable 5.1 has the highest overall score estimate among the models that meet this page's constraints (84.7). The underlying model is only part of an assistant. Plan limits, browsing, memory, and integrations need app-level evidence.

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The shortlist for AI assistant

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 Claude Fable 5.1 vs GPT-6 AstraFull overall leaderboard

  1. 01Claude Fable 5.1Best fit

    Anthropic · Proprietary

    84.7

    Overall score estimate

    • Highest overall estimate among models that meet every stated constraint
    • $20.00 for the stated workload
    • 1M context

    Estimate sources: LMArena Text Overall, Artificial Analysis, Epoch Capability Index

    Model page and evidence

  2. 02GPT-6 Astra

    OpenAI · Proprietary

    82.8

    Overall score estimate

    • Estimate 82.8 on the same overall evidence
    • $20.00 for the stated workload
    • 1.05M context

    Estimate sources: Artificial Analysis, Epoch Capability Index

    Model page and evidenceCompare with Claude Fable 5.1

  3. 03Claude Opus 5

    Anthropic · Proprietary

    81.9

    Overall score estimate

    • Estimate 81.9 on the same overall evidence
    • $10.00 for the stated workload

    Estimate sources: LMArena Text Overall, Artificial Analysis, Epoch Capability Index

    Model page and evidenceCompare with Claude Fable 5.1

  4. 04Claude Fable 5

    Anthropic · Proprietary

    81.4

    Overall score estimate

    • Estimate 81.4 on the same overall evidence
    • $20.00 for the stated workload
    • 1M context

    Estimate sources: LMArena Text Overall, Artificial Analysis, Epoch Capability Index

    Model page and evidenceCompare with Claude Fable 5.1

  5. 05GPT-5.6 Sol

    OpenAI · Proprietary

    80.5

    Overall score estimate

    • Estimate 80.5 on the same overall evidence
    • $8.00 for the stated workload
    • 1.05M context

    Estimate sources: LMArena Text Overall, Artificial Analysis, Epoch Capability Index

    Model page and evidenceCompare with Claude Fable 5.1

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

What to verify before choosing

  • The underlying model is only part of an assistant. Plan limits, browsing, memory, and integrations need app-level evidence.
  • 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 AI assistant?

On BenchLM's public evidence, Claude Fable 5.1 by Anthropic has the highest overall score estimate among models that meet the page's default constraints (84.7). Ordered by task score under the stated constraints. The underlying model is only part of an assistant. Plan limits, browsing, memory, and integrations need app-level evidence.

What are the alternatives to Claude Fable 5.1 for AI assistant?

GPT-6 Astra (82.8), Claude Opus 5 (81.9), Claude Fable 5 (81.4), GPT-5.6 Sol (80.5) 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 assistant?

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 overall 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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