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 Gemini 2.5 Pro in the context of 32 models from ORCFLO Index — May 10, 2026 cohort · 32 total models tested · Anthropic, Google, OpenAI, Mistral.
Gemini 2.5 Pro
Gemini 2.5 Pro lands at #6 of 32 with a quality score of 91.99, trailing GPT 5, Gemini 3 Pro, GPT 5.5, Claude Opus 4.6, and GPT 5.1. It holds leader-tier positions in four categories — Analysis, Extraction, Hallucination, and Instruction Following — while costing just $0.0083 per case (#19), undercutting every quality peer in the top 10. The trade-off is speed: at 19.7 seconds per response, it ranks #27 of 32. A strong analytical workhorse for teams that can tolerate latency.
Key Findings
- Top-tier factual reliability: Hallucination score of 95.5 ranks #4 of 32, the highest among non-frontier-priced models in the top 10.
- Most cost-efficient of the top 6: At $0.0083 per case, it is cheaper than GPT 5.5 ($0.0419), Claude Opus 4.6 ($0.0221), and GPT 5.1 ($0.0100) while sitting one rank below each on quality.
- Leader tier in four of eight categories: Analysis (#5), Extraction (#5), Hallucination (#4), and Instruction Following (#7) all place in the leader band.
- Latency is the bottleneck: 19.7-second average response time ranks #27 of 32, slower than every quality peer except GPT 5.5 (22.8s).
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 | #5 | Leader | |
| ExtractionField accuracy, null handling, format compliance, zero fabrication | #5 | Leader | |
| SummarizationCompression quality, key-point retention, length compliance | #12 | Strong | |
| WritingTone, structure, persuasion, audience adaptation | #9 | Strong | |
| Behaviors — How the model acts under pressure: reliability, compliance, and restraint. | |||
| HallucinationFabrication detection, factual grounding, source fidelity | #4 | Leader | |
| Instruction FollowingConstraint adherence, format compliance, multi-part directives | #7 | Leader | |
| Refusal CalibrationAppropriate refusal vs. over-refusal on legitimate requests | #9 | Strong | |
| Stability — Repeatability and predictability across identical inputs. | |||
| Output ConsistencyRun-to-run reproducibility, format stability, score variance | #19 | Contender | |
Strengths and Cautions
Strengths
- Analysis and Extraction at #5 of 32: Scores of 92.7 and 91.7 place it in the leader tier for structured reasoning and data retrieval workloads.
- Hallucination control (95.5, rank #4): Outperforms all GPT 5 variants below it on factual grounding, making it strong for regulated content and reference-quality output.
- Instruction Following at 93.4 (#7): Reliable adherence to complex prompts, paired with the lowest cost per case among the top six models overall.
Cautions
- Speed rank #27 of 32: The 19.7-second average makes it unsuitable for interactive chat, real-time assistants, or any latency-sensitive workflow.
- Output Consistency drops to #19 of 32 (88.0): Only contender-tier on stability, meaning repeated identical prompts may produce more variation than peers like GPT 5.1 or Claude Opus 4.6.
- Summarization rank #12: A noticeable step down from its analysis and extraction strengths, and behind several lower-quality peers in the cohort.
Head-to-Head: Frontier Models
Gemini 2.5 Pro is Google’s cost-efficient leader-tier 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 5.5 | 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 |
| GPT 5.4 | 90.8 | #8 | #25 | #23 |
| Claude Opus 4.5 | 90.7 | #9 | #28 | #15 |
When to Use Gemini 2.5 Pro
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