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 Nano in the context of 32 models from ORCFLO Index — May 10, 2026 cohort · 32 total models tested · Anthropic, Google, OpenAI, Mistral.
GPT 4.1 Nano
GPT 4.1 Nano lands at #31 of 32 in overall quality (71.26), placing it near the bottom of the cohort behind frontier leaders like GPT 5 (95.04), Gemini 3 Pro (93.17), and Claude Opus 4.6 (92.78). Its appeal is economic, not qualitative: at $0.0002 per case (#2) and 4.0s response time (#3), it is among the cheapest and fastest options available. Use it where throughput and unit cost matter more than answer fidelity.
Key Findings
- Quality ranks #31 of 32 overall, with last-place finishes in Extraction (73.9), Instruction Following (77.7), Refusal Calibration (51.0), and Output Consistency (73.7).
- Cost and speed are the sole differentiators: $0.0002/case (#2) and 4.0s avg response (#3) make it viable for high-volume, low-stakes pipelines.
- Summarization is the one bright spot at 86.7 (#22), reaching contender tier — the only category where it escapes the trailing group.
- Refusal Calibration scores 51.0, dead last, signaling poor judgment on when to decline or qualify responses.
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 | #29 | Trailing | |
| ExtractionField accuracy, null handling, format compliance, zero fabrication | #32 | Trailing | |
| SummarizationCompression quality, key-point retention, length compliance | #22 | Contender | |
| WritingTone, structure, persuasion, audience adaptation | #31 | Trailing | |
| Behaviors — How the model acts under pressure: reliability, compliance, and restraint. | |||
| HallucinationFabrication detection, factual grounding, source fidelity | #27 | Trailing | |
| Instruction FollowingConstraint adherence, format compliance, multi-part directives | #32 | Trailing | |
| Refusal CalibrationAppropriate refusal vs. over-refusal on legitimate requests | #32 | Trailing | |
| Stability — Repeatability and predictability across identical inputs. | |||
| Output ConsistencyRun-to-run reproducibility, format stability, score variance | #32 | Trailing | |
Strengths and Cautions
Strengths
- Lowest-tier pricing at $0.0002 per case (#2 of 32), matched only by Gemini 2.0 Flash-Lite and undercut only by Mistral Small 3.
- Fast response times averaging 4.0 seconds (#3 of 32), suitable for latency-sensitive or interactive workloads.
- Acceptable summarization quality at 86.7 (#22), the model's only category outside the trailing tier.
Cautions
- Last place in four categories — Extraction, Instruction Following, Refusal Calibration, and Output Consistency — making it unsuitable for structured-data or compliance-sensitive tasks.
- Analysis scores 58.2 (#29) and Writing 69.3 (#31), ruling out reasoning-heavy or customer-facing content work.
- Refusal Calibration at 51.0 (#32) raises governance risk in regulated environments where over- or under-refusal carries cost.
Head-to-Head: Frontier Models
GPT 4.1 Nano is OpenAI’s budget throughput 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 |
|---|---|---|---|---|
| Mistral Small 3 (24B) | 79.9 | #26 | #1 | #7 |
| Gemini 2.0 Flash-Lite | 78.0 | #27 | #3 | #2 |
| o3-mini | 77.4 | #28 | #27 | #20 |
| GPT 4o | 75.4 | #29 | #16 | #6 |
| GPT 4o Mini | 72.8 | #30 | #6 | #8 |
| GPT 4.1 Nano | 71.3 | #31 | #2 | #3 |
| Codestral (2508) | 71.1 | #32 | #8 | #1 |
When to Use GPT 4.1 Nano
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