Recommendation for High-volume
High-Volume Extraction
Our top recommendation for High-Volume Extraction, based on the public evidence we track, is OpenAI: GPT-5.6 Luna.[1][2] Watch out: Expect roughly 1.8 percentage points lower accuracy on table joining tasks compared to GPT-5.6 Sol, which may compound error rates at million-item scale. Anthropic: Claude Sonnet 4.6 is the next-ranked alternative. Its currently supported evidence is cautionary: Account for nearly two percentage points lower LiveBench Data Analysis accuracy versus GPT-5.6 Sol, which may require validation sampling at scale.
About this recommendation
- Updated
- Sep 6, 2026
- Evidence through
- Sep 6, 2026
- Sources
- 4
- Revision
- v55
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
2 of 4
Anthropic
Sources evaluated
The task sets these weights before any model is scored.
| Evaluation feed | Weight | Winner result | Field measured |
|---|---|---|---|
| Structured-output evalunavailable | 30% | feed unavailable | 0/20 |
| Route reliabilityunavailable | 25% | feed unavailable | 0/20 |
| price weight | 20% | 100/100 | 20/20 |
| LiveBench Data Analysis | 15% | #15 | 18/20 |
| OpenRouter usage | 10% | 99/100 | 20/20 |
Provider concentration
Each exact model is scored separately; provider identity is not a ranking input.
- Anthropic2 models
- minimax1 model
- OpenAI1 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.
| Rank | Model | Relative score | Coverage | Practitioner evidence | Strongest measured reason |
|---|---|---|---|---|---|
| 01 | GPT-5.6 LunaOpenAI | 59 | 45% | no linked practitioner threads | #15 LiveBench Data Analysis |
| 02 | Claude Sonnet 4.6Anthropic | 58 | 45% | no linked practitioner threads | #16 LiveBench Data Analysis |
| 03 | MiniMax M3minimax | 58 | 45% | no linked practitioner threads | #20 LiveBench Data Analysis |
| 04 | Claude Fable 5Anthropic | 57 | 45% | no linked practitioner threads | #2 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.
GPT-5.6 Luna scores 78.03% on LiveBench Data Analysis, placing 18th of 52, trailing the Sol variant by nearly two percentage points on the same benchmark.
Best when: Consider only after reviewing the cited caution.
Watch out for
- Expect roughly 1.8 percentage points lower accuracy on table joining tasks compared to GPT-5.6 Sol, which may compound error rates at million-item scale.
Claude Sonnet 4.6 ranks 24th on LMArena and 19th on LiveBench Data Analysis with 77.95%, showing consistent mid-tier performance across evaluation types.
Best when: Consider only after reviewing the cited caution.
Watch out for
- Account for nearly two percentage points lower LiveBench Data Analysis accuracy versus GPT-5.6 Sol, which may require validation sampling at scale.
MiniMax M3 scores 76.17% on LiveBench Data Analysis as an open-weight option, placing 23rd of 52 with availability across five deployment routes.
Best when: Consider only after reviewing the cited caution.
Watch out for
- Validate output carefully on table reformatting tasks, as its 76.17% LiveBench Data Analysis score sits below the 79%+ range of leading closed-weight alternatives.
Claude Fable 5 leads all candidates with 80.54% on LiveBench Data Analysis (#3 of 52) and tops LMArena's human preference rankings, though cost positioning for high-volume extraction is unverified.
Best when: Select when extraction accuracy is paramount, as its 80.54% LiveBench Data Analysis score is the highest among all candidates on table joining and reformatting tasks.
Tips
- Select when extraction accuracy is paramount, as its 80.54% LiveBench Data Analysis score is the highest among all candidates on table joining and reformatting tasks.
Frequently asked
- What should I watch out for with OpenAI: GPT-5.6 Luna?
- Expect roughly 1.8 percentage points lower accuracy on table joining tasks compared to GPT-5.6 Sol, which may compound error rates at million-item scale.[1]
Sources
- 1
“Scores 78.03% on LiveBench Data Analysis (#18 of 52), including table joining and reformatting tasks.”
LiveBench Data Analysis · Benchmark · Jun 25, 2026 - 2
“Scores 77.95% on LiveBench Data Analysis (#19 of 52), including table joining and reformatting tasks.”
LiveBench Data Analysis · Benchmark · Jun 25, 2026 - 3
“Scores 76.17% on LiveBench Data Analysis (#23 of 52), including table joining and reformatting tasks.”
LiveBench Data Analysis · Benchmark · Jun 25, 2026 - 4
“Scores 80.54% on LiveBench Data Analysis (#3 of 52), including table joining and reformatting tasks.”
LiveBench Data Analysis · Benchmark · Jun 25, 2026
Rankings synthesized from community evidence and open benchmarks. See methodology. Not driven by vendor marketing.