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 4.1 in the context of 32 models from ORCFLO Index — May 10, 2026 cohort · 32 total models tested · Anthropic, Google, OpenAI, Mistral.
GPT 4.1
GPT 4.1 lands at #19 of 32 with a quality score of 84.96, placing it in the mid-tier of the cohort and well behind frontier leaders like GPT 5 (95.04), Gemini 3 Pro (93.17), and Claude Opus 4.6 (92.78). Its appeal is operational rather than qualitative: at $0.0052 per case (#15) and 5.2-second response time (#10), it offers a balanced cost-speed profile. A solid workhorse where throughput matters more than peak reasoning quality.
Key Findings
- Summarization is the standout category at 92.2 (#14 of 32, strong tier) — the only area where GPT 4.1 breaks into the upper third of the field.
- Analysis and writing lag the rest of the profile, scoring 80.0 (#20) and 81.6 (#21) respectively, placing both in contender tier and well below frontier models in the 90+ range.
- Speed ranks #10 at 5.2 seconds, faster than every Claude variant in its quality neighborhood and matching Haiku 4.5 — a meaningful edge for latency-sensitive workflows.
- Refusal calibration at 78.3 (#17) is the weakest behavior metric, suggesting inconsistent handling of edge-case prompts compared to its overall positioning.
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 | #20 | Contender | |
| ExtractionField accuracy, null handling, format compliance, zero fabrication | #19 | Contender | |
| SummarizationCompression quality, key-point retention, length compliance | #14 | Strong | |
| WritingTone, structure, persuasion, audience adaptation | #21 | Contender | |
| Behaviors — How the model acts under pressure: reliability, compliance, and restraint. | |||
| HallucinationFabrication detection, factual grounding, source fidelity | #19 | Contender | |
| Instruction FollowingConstraint adherence, format compliance, multi-part directives | #20 | Contender | |
| Refusal CalibrationAppropriate refusal vs. over-refusal on legitimate requests | #17 | Contender | |
| Stability — Repeatability and predictability across identical inputs. | |||
| Output ConsistencyRun-to-run reproducibility, format stability, score variance | #18 | Contender | |
Strengths and Cautions
Strengths
- Summarization quality (92.2, #14) is the model's most defensible capability, outperforming its overall rank by five positions and placing it in strong tier.
- Latency profile (5.2s, #10) is competitive with much smaller models and faster than higher-quality peers like Claude Sonnet 4 (8.7s) and Sonnet 4.5 (10.3s).
- Cost-to-speed balance ($0.0052, #15) sits in a usable middle ground — cheaper than o4-mini ($0.0092) and Sonnet 4.5 ($0.0088) while delivering comparable response times.
Cautions
- Analysis at 80.0 (#20) and writing at 81.6 (#21) trail frontier models by 10+ points, making this a weak choice for complex reasoning or long-form content generation.
- Refusal calibration of 78.3 (#17) is the lowest score in the model's profile, indicating less reliable judgment on borderline or sensitive prompts.
- Instruction following at 87.7 (#20) trails most top-10 models by 5+ points — a concern for workflows requiring strict adherence to multi-step prompts.
Head-to-Head: Frontier Models
GPT 4.1 is OpenAI’s mid-tier workhorse 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 Nano | 85.9 | #16 | #10 | #30 |
| o4-mini | 85.9 | #17 | #21 | #12 |
| Claude Sonnet 4 | 85.5 | #18 | #17 | #14 |
| GPT 4.1 | 85.0 | #19 | #15 | #10 |
| Claude Haiku 4.5 | 84.7 | #20 | #13 | #11 |
| Claude Sonnet 4.5 | 84.6 | #21 | #20 | #17 |
| Mistral Large 3 (2512) | 83.2 | #22 | #11 | #21 |
When to Use GPT 4.1
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