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 GPT 5 in the context of 32 models from ORCFLO Index — May 10, 2026 cohort · 32 total models tested · Anthropic, Google, OpenAI, Mistral.
GPT 5
GPT 5 holds the #1 overall quality rank in a 32-model field with a score of 95.04, ahead of Gemini 3 Pro Preview (93.17) and stablemate GPT 5.5 (93.02). It leads or near-leads across nearly every category measured, including a clean #1 in Refusal Calibration. The trade-off is severe: it ranks #31 of 32 on cost and dead last (#32) on response time at 36.4 seconds. Top-tier output, bottom-tier economics and latency.
Key Findings
- Highest overall quality in the cohort at 95.04, with a measurable lead of 1.87 points over the #2 model Gemini 3 Pro Preview and 2.02 points over the next OpenAI entry, GPT 5.5.
- Leader-tier across seven of eight categories, including #1 in Refusal Calibration (96.5), #2 in Analysis (95.3), Extraction (94.6), Summarization (96.0), and Hallucination (96.9).
- Worst speed profile in the field at 36.4 seconds per case (#32 of 32) and second-highest cost at $0.0267 per case (#31 of 32) — only GPT 5.5 is more expensive.
- Instruction Following is the relative soft spot at rank #12 (92.5), notably out of step with the model's leader-tier performance elsewhere.
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 | #2 | Leader | |
| ExtractionField accuracy, null handling, format compliance, zero fabrication | #2 | Leader | |
| SummarizationCompression quality, key-point retention, length compliance | #2 | Leader | |
| WritingTone, structure, persuasion, audience adaptation | #7 | Leader | |
| Behaviors — How the model acts under pressure: reliability, compliance, and restraint. | |||
| HallucinationFabrication detection, factual grounding, source fidelity | #2 | Leader | |
| Instruction FollowingConstraint adherence, format compliance, multi-part directives | #12 | Strong | |
| Refusal CalibrationAppropriate refusal vs. over-refusal on legitimate requests | #1 | Leader | |
| Stability — Repeatability and predictability across identical inputs. | |||
| Output ConsistencyRun-to-run reproducibility, format stability, score variance | #5 | Leader | |
Strengths and Cautions
Strengths
- Refusal Calibration #1 of 32 at 96.5 — the only category where GPT 5 leads outright, indicating disciplined judgment on when to answer versus decline.
- Hallucination control at 96.9 (rank #2) pairs with Analysis (95.3, #2) and Summarization (96.0, #2) to make this a defensible choice for high-stakes synthesis work.
- Output Consistency at 96.0 (rank #5) reinforces predictable behavior across repeated runs, supporting use in regulated or audit-sensitive contexts.
Cautions
- Latency is disqualifying for interactive use: 36.4 seconds per case is the slowest in the cohort, roughly 5x slower than GPT 5.1 (7.6s) at a similar quality tier.
- Cost per case is $0.0267 (#31 of 32), materially above peers like Gemini 3 Pro Preview ($0.0081) and GPT 5.1 ($0.0100) that deliver quality within ~2 points.
- Instruction Following ranks #12 at 92.5 — acceptable but the weakest dimension of the model and a flag for workflows with rigid formatting or procedural requirements.
Head-to-Head: Frontier Models
GPT 5 is OpenAI’s top performer 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 GPT 5
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