The ORCFLO Indexis an independent benchmark that evaluates large language models the way business professionals actually use them — across real-world tasks spanning analysis, writing, extraction, summarization, and behavioral reliability. Each model is scored on three dimensions (quality, cost, and speed) by a panel of four independent judges. This report evaluates Claude Opus 4.6 in the context of 32 models from ORCFLO Index — May 10, 2026 cohort · 32 total models tested · Anthropic, Google, OpenAI, Mistral.
Claude Opus 4.6
Claude Opus 4.6 ranks #4 of 32 overall with a quality score of 92.78, trailing only GPT 5 (95.04), Gemini 3 Pro Preview (93.17), and GPT 5.5 (93.02). It claims outright #1 finishes in four categories — Analysis, Summarization, Writing, and Hallucination — making it the most consistent category-leader in the field. The trade-off is operational: at $0.0221 per case (#29) and 18.9s response time (#26), it is among the slowest and most expensive options available.
Key Findings
- Four category wins: Opus 4.6 takes #1 in Analysis (95.8), Summarization (96.0), Writing (94.8), and Hallucination (97.4) — more category leadership than any other model in the cohort.
- Lowest hallucination rate measured: at 97.4 it leads all 32 contestants, making it the safest choice when factual reliability is paramount.
- Extraction is the lone soft spot: 81.8 places it at #22 of 32, a striking gap given its leader-tier scores everywhere else.
- Premium cost profile: at $0.0221 per case (#29) and 18.9s latency (#26), Opus 4.6 is priced and paced for high-stakes work, not high-volume pipelines.
Model Performance: Quality & Cost
The chart below plots quality against cost for all 32 models in the ORCFLO Index. Each dot represents the average quality score a model achieved across the full basket of real-world business tasks, alongside the cost in credits to complete the entire test suite. Models in the upper-left quadrant deliver the highest quality at the lowest cost.
Model Performance: Quality & Time Elapsed
Quality alone doesn’t tell the full story — response time determines whether a model is viable for time-sensitive workflows. The chart below plots each model’s quality score against the total time required to complete the test suite. Models in the upper-left deliver the best quality with the least delay.
Category Scorecard
The ORCFLO Indexevaluates models using real-world business tasks — not academic puzzles or synthetic benchmarks. Each test case is designed to expose specific differences in how models handle the work professionals actually do. Scores are averaged across each category and ranked independently across all 32 models.
| Category | Score | Rank | Tier |
|---|---|---|---|
| Abilities — Core language tasks: what the model can produce when given a well-formed prompt. | |||
| AnalysisReasoning, strategic judgment, disqualifying-factor detection | #1 | Leader | |
| ExtractionField accuracy, null handling, format compliance, zero fabrication | #22 | Contender | |
| SummarizationCompression quality, key-point retention, length compliance | #1 | Leader | |
| WritingTone, structure, persuasion, audience adaptation | #1 | Leader | |
| Behaviors — How the model acts under pressure: reliability, compliance, and restraint. | |||
| HallucinationFabrication detection, factual grounding, source fidelity | #1 | Leader | |
| Instruction FollowingConstraint adherence, format compliance, multi-part directives | #5 | Leader | |
| Refusal CalibrationAppropriate refusal vs. over-refusal on legitimate requests | #12 | Strong | |
| Stability — Repeatability and predictability across identical inputs. | |||
| Output ConsistencyRun-to-run reproducibility, format stability, score variance | #7 | Leader | |
Strengths and Cautions
Strengths
- Long-form quality leadership: #1 in Writing (94.8), Summarization (96.0), and Analysis (95.8) — a clean sweep of the categories that matter most for executive deliverables and research synthesis.
- Factual reliability: #1 in Hallucination (97.4) paired with #5 in Instruction Following (93.8) makes it the strongest combination of accuracy and directive compliance in the cohort.
- Predictable outputs: Output Consistency of 95.3 (#7) means repeated runs yield similar results — important for regulated workflows and audit trails.
Cautions
- Extraction underperforms badly: rank #22 (81.8) is out of step with the rest of its profile and disqualifies it for structured data parsing where peers like GPT 5.1 and Gemini 2.5 Pro are stronger.
- High cost per case: $0.0221 (#29 of 32) is roughly 2x more expensive than similarly-ranked GPT 5.1 ($0.0100) and Gemini 2.5 Pro ($0.0083).
- Slow response times: 18.9s (#26) is more than 2x slower than GPT 5.1 (7.6s) at comparable quality, limiting suitability for interactive or real-time applications.
Head-to-Head: Frontier Models
Claude Opus 4.6 is Anthropic’s top-tier premium pick in the ORCFLO Index. The table below compares it against the top-performing models from each major provider. Tier assignments use 25% quartiles across the full 32-model field.
| Model | Quality Avg | Quality Rank | Cost Rank | Speed Rank |
|---|---|---|---|---|
| GPT 5 | 95.0 | #1 | #31 | #32 |
| Gemini 3 Pro (Preview) | 93.2 | #2 | #18 | #28 |
| GPT 5.5 | 93.0 | #3 | #32 | #29 |
| Claude Opus 4.6 | 92.8 | #4 | #29 | #26 |
| GPT 5.1 | 92.7 | #5 | #22 | #13 |
| Gemini 2.5 Pro | 92.0 | #6 | #19 | #27 |
| GPT 5.2 | 91.9 | #7 | #23 | #19 |
When to Use Claude Opus 4.6
The ORCFLO Index
This evaluation covers 40 cases across 8 categories. All tasks are text-only and English-only. Code generation, multi-turn conversation, multimodal tasks, and agentic workflows are not tested. Each contestant is scored by a panel of four independent judges — Gemini 2.5 Pro, Claude Opus 4.7, GPT 5.5, and Mistral Large — with final scores averaged across all four. Cost and speed measurements reflect API pricing and latency as of the test date (May 10, 2026) and will change as providers update their offerings.
How We Test
The ORCFLO Indexevaluates large language models across three independent dimensions — quality, cost, and speed — using real-world business tasks designed to expose the differences that matter for model selection. Each model is scored by a panel of four independent judges to reduce single-model bias.
- Test Cases
- 40 cases across 8 categories spanning Abilities (Analysis, Extraction, Summarization, Writing), Behaviors (Hallucination, Instruction Following, Refusal Calibration), and Stability (Output Consistency).
- Judge Panel
- Gemini 2.5 Pro, Claude Opus 4.7, GPT 5.5, and Mistral Large. Each judge scores independently. Final score is the average across all four.
- Scoring
- Three independent ranks: quality (higher is better), cost (lower is better), speed (faster is better). No composite score — composites hide the tradeoffs that drive model-selection decisions.
- Tier Definitions
- LeaderQuality ≥ 90.8Ranks 1–8Strong≥ 85.9Ranks 9–16Contender≥ 80.9Ranks 17–24Trailing< 80.9Ranks 25–32