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Work with documents or data · Extract structured data from files

Best AI model for data extraction — November 2026

On BenchLM's public evidence, Grok 4.3 has the highest instruction following score estimate among the models that meet this page's constraints (97.1). Brief adherence is a proxy; exact schema validity, extraction accuracy, and retries need workload tests.

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The shortlist for data extraction

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 Grok 4.3 vs FlintFull instruction following leaderboard

  1. 01Grok 4.3Best fit

    xAI · Proprietary

    97.1

    Instruction following score estimate

    • Highest instruction following estimate among candidates passing the measured filters
    • $1.75 for the stated workload
    • 1M context
    • API access is not verified. Check the provider documentation before relying on it.
    • Provider API offer: xAI API (Grok). No free tier documented. Limits and source.

    Model page and evidence

  2. 02Flint

    Recursive · Open Weight

    94.7

    Instruction following score estimate

    • Estimate 94.7 on the same instruction following evidence
    • 256K context
    • Open weights; check hardware before treating local use as feasible
    • API access is not verified. Check the provider documentation before relying on it.

    Model page and evidenceCompare with Grok 4.3

  3. 03MAI-Thinking-1

    Microsoft · Proprietary

    94.7

    Instruction following score estimate

    • Estimate 94.7 on the same instruction following evidence
    • 256K context
    • API access is not verified. Check the provider documentation before relying on it.

    Model page and evidenceCompare with Grok 4.3

  4. 04Qwen3.8 Max

    Alibaba · Open Weight

    94.7

    Instruction following score estimate

    • Estimate 94.7 on the same instruction following evidence
    • 1M context
    • Open weights; check hardware before treating local use as feasible
    • API access is not verified. Check the provider documentation before relying on it.

    Model page and evidenceCompare with Grok 4.3

  5. 05GLM-5.1

    Z.AI · Open Weight

    93.8

    Instruction following score estimate

    • Estimate 93.8 on the same instruction following evidence
    • $2.28 for the stated workload
    • 203K context
    • Open weights; check hardware before treating local use as feasible
    • API access is not verified. Check the provider documentation before relying on it.

    Model page and evidenceCompare with Grok 4.3

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.

Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.

We keep what you type here, drafts included, for 180 days without your IP address or account, to see which jobs people need. Leave out names and other personal details. Privacy

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

  • Brief adherence is a proxy; exact schema validity, extraction accuracy, and retries need workload tests.
  • Composite scores are estimates. A small score gap does not establish a reliably better model.

Use a schema-and-field test on your own documents. Check both the JSON shape and whether each field matches its source.

Questions

Which AI model is best for data extraction?

On BenchLM's public evidence, Grok 4.3 by xAI has the highest instruction following score estimate among models that meet the page's default constraints (97.1). Ordered by task score under the stated constraints. Brief adherence is a proxy; exact schema validity, extraction accuracy, and retries need workload tests.

What are the alternatives to Grok 4.3 for data extraction?

Flint (94.7), MAI-Thinking-1 (94.7), Qwen3.8 Max (94.7), GLM-5.1 (93.8) 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 data extraction?

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

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