Recommendation for High-volume

High-Volume Extraction

Our top recommendation for High-Volume Extraction, based on the public evidence we track, is OpenAI: GPT-6 Sol.[1] OpenAI: GPT-5.6 Sol is the next-ranked alternative. Strong LiveBench Data Analysis score makes it viable for table-heavy extraction where column alignment matters.

About this recommendation

Updated
Sep 25, 2026
Evidence through
Sep 25, 2026
Sources
3
Revision
v74

Decision audit

Why this result

Inspect the inputs and the computed order behind the recommendation.

Models screened

20

live candidates

Evaluation feeds

5

task-weighted

Winner coverage

45%

intended feed weight

Largest provider share

3 of 4

OpenAI

Provisional source breadth. 3 citation families and 0 practitioner families support the top result; 0 cautionary threads is retained. The largest citation family contributes 57%.

Sources evaluated

The task sets these weights before any model is scored.

winner: GPT-6 Sol
Evaluation feedWeightWinner resultField measured
Structured-output evalunavailable
30%
feed unavailable0/20
Route reliabilityunavailable
25%
feed unavailable0/20
price weight
20%
99/10020/20
LiveBench Data Analysis
15%
#220/20
OpenRouter usage
10%
85/10020/20

Provider concentration

Each exact model is scored separately; provider identity is not a ranking input.

OpenAI75%
  • OpenAI3 models
  • Anthropic1 model

Decision table

Every published model is shown in computed order. Practitioner sources are distinct community threads, not the citations repeated in the prose below.

RankModelRelative scoreCoveragePractitioner evidenceStrongest measured reason
01GPT-6 SolOpenAI
61
45%no linked practitioner threads#2 LiveBench Data Analysis
02GPT-5.6 SolOpenAI
60
45%no linked practitioner threads#6 LiveBench Data Analysis
03GPT-5.6 LunaOpenAI
59
45%1 threads · 1 families · 1 cautions#17 LiveBench Data Analysis
04Claude Fable 5Anthropic
56
45%no linked practitioner threads#3 LiveBench Data Analysis

Relative score combines normalized benchmark quality and signal coverage; independent practitioner evidence and freshness are bounded tie-breakers. It is an ordering score, not an absolute quality percentage. The writing model receives this order and cannot change it.

  1. 01

    OpenAI: GPT-6 Sol

    OpenAI: GPT-6 Sol scores 81.19% on LiveBench Data Analysis (#3 of 58), including table joining and reformatting tasks.

    Best when: Consider only after reviewing the cited caution.

  2. 02

    OpenAI: GPT-5.6 Sol

    Scores 79.84% on LiveBench Data Analysis (#7) with mid-tier LMArena standing (#13, Elo 1483), indicating solid but not exceptional extraction accuracy.

    Best when: Strong LiveBench Data Analysis score makes it viable for table-heavy extraction where column alignment matters.

    Tips

    • Strong LiveBench Data Analysis score makes it viable for table-heavy extraction where column alignment matters.
      Source 1
      “Scores 79.84% on LiveBench Data Analysis (#7 of 58), including table joining and reformatting tasks.”
      LiveBench Data AnalysisOpen original ↗
  3. 03

    OpenAI: GPT-5.6 Luna

    LiveBench Data Analysis score of 78.03% (#21) is undermined by production evidence showing ~15% failure rate on structured extraction workloads.

    Best when: Consider only after reviewing the cited caution.

    Watch out for

    • Production logs show 183 invalid structured outputs, 138 phantom tool calls, and 187 truncations in 3,724 completions for retain extraction with agentic reflection.
      Source 2
      “Contrast data point from the hindsight production host: the same workload (retain extraction + agentic reflect with tool chains) now runs on gemini-3.8-flash with zero failures while luna still shows its modes. Same-bridge comparison, identical prompts and tools: | model | structured output | tool-call protocol | production census | |---|---|---|---| | gpt-5.6-luna | - | - | ~15% failures (183 invalid structured, 138 phantom tools, 187 truncated, 68 malformed / 3724 completions) | | glm-5.3-fla…”
      mrwoguOpen original ↗
  4. 04

    Anthropic: Claude Fable 5

    Tops LMArena overall preferences (#1, Elo 1506) while scoring #4 on LiveBench Data Analysis at 80.54%, showing balanced capability.

    Best when: Highest human preference ranking suggests reliable instruction following for complex extraction prompts with nested schemas.

    Tips

    • Highest human preference ranking suggests reliable instruction following for complex extraction prompts with nested schemas.
      Source 3
      “Ranks #1 of 146 on LMArena's overall text arena (Elo 1506), based on blind human preference votes.”
      LMArena text arenaOpen original ↗

Frequently asked

What is an alternative to OpenAI: GPT-6 Sol?
OpenAI: GPT-5.6 Sol is the next-ranked option. Strong LiveBench Data Analysis score makes it viable for table-heavy extraction where column alignment matters.[1]

Sources

  1. 1

    “Scores 79.84% on LiveBench Data Analysis (#7 of 58), including table joining and reformatting tasks.”

    LiveBench Data Analysis · Benchmark · Jun 25, 2026
  2. 2

    “Contrast data point from the hindsight production host: the same workload (retain extraction + agentic reflect with tool chains) now runs on gemini-3.8-flash with zero failures while luna still shows its modes. Same-bridge comparison, identical prompts and tools: | model | structured output | tool-call protocol | production census | |---|---|---|---| | gpt-5.6-luna | - | - | ~15% failures (183 invalid structured, 138 phantom tools, 187 truncated, 68 malformed / 3724 completions) | | glm-5.3-fla…”

    mrwogu · GitHub · Sep 10, 2026
  3. 3

    “Ranks #1 of 146 on LMArena's overall text arena (Elo 1506), based on blind human preference votes.”

    LMArena text arena · Benchmark · Sep 13, 2026

Rankings synthesized from community evidence and open benchmarks. See methodology. Not driven by vendor marketing.