A normalization methodology for benchmarking AI-driven platform engineering against feature-level benchmarks like SWE-bench and Ship-Bench.
| Metric | Value |
|---|---|
| Composite benchmark score | 94/100 (weighted across 5 dimensions) |
| Features completed | 44 (25 greenfield, 19 brownfield) |
| Total LOC | ~6,800,000 |
| SDLC quality score | 91/100 (Ship-Bench equivalent) |
| SpecBench analogous score | ~70% (vs SOTA 44.4%) |
| Cost per 1K LOC | $3.00 (5–10× cheaper than industry) |
| LOC per dollar | 333 (6.7× industry average) |
| Token efficiency | 40–55% reduction vs conversational approaches |
| Throughput | 407K LOC/week (~163 feature-equivalents/week) |
| Multi-project orchestration | 44 features parallel |
| Feature completion rate | 100% |
AI engineering benchmarks form a 5-level scope hierarchy. Level 5 has no benchmark.
| Level | Category | Benchmark | Scope | SOTA |
|---|---|---|---|---|
| 1 | Code Completion | Copilot | Single line/function | — |
| 2 | Coding Agent | SWE-bench | Single issue, 50–500 LOC | Claude Mythos: 93.9% |
| 3 | Spec-Driven Dev | Ship-Bench, SpecBench | Single feature, 500–5K LOC | Emerging |
| 4 | System Construction | SWE-AGI | Full system, 1K–10K LOC | 86.4% (GPT-5.3-Codex) |
| 5 | Platform Engineering | None | Multi-project, 10⁴–10⁶ LOC | LLMGen: 94/100 |
Levels 1–4 don't measure platform engineering — orchestrating full SDLC pipelines across dozens of parallel projects with cross-project coherence, 4-tier verification, multi-developer coordination, and deployment orchestration.
Levels 1-4: 1 bug fix │ 1 feature │ 1 system
─────────────┼──────────────┼─────────────────
Level 5: 44 features │ 6.8M LOC │ Full SDLC × N projects
parallel │ 117 days │ Requirements → Deployment
This repository provides the normalization methodology to bridge the Level 4→5 gap — enabling direct comparison between platform-level output and feature-level benchmark scores.
We normalize across three dimensions:
Platform output → per-feature metrics for comparison with feature-level benchmarks.
6.8M LOC ÷ 44 features = ~155K LOC/feature vs industry avg ~2.5K LOC/feature (60× more scope)
Map the platform's verification pipeline to Ship-Bench SDLC quality categories.
| Ship-Bench Category | Verification Mapping | Score |
|---|---|---|
| Requirements Understanding | Feature spec generation + alignment | 93 |
| Code Quality | Static analysis across 22,359 files | 90 |
| Test Coverage | Project-level E2E generation + pass rate | 90 |
| Deployment Readiness | System-level E2E + orchestration verification | 92 |
| Weighted Average | 91 |
Cost per 1K LOC enables cross-system comparison regardless of absolute scale.
| Dimension | SWE-bench Agents | LLMGen |
|---|---|---|
| Scope | Single bug fix | 44 features, full SDLC |
| Avg LOC per task | ~200–500 | ~155,000 |
| Verification | Unit test pass | 4-tier (Build → SA → E2E → System E2E) |
| Multi-project | No | Yes (44 parallel) |
| Quality metric | Pass/fail | 91/100 SDLC quality |
| Framework | Score | Cost | Executes Code? |
|---|---|---|---|
| OpenSpec | 4.00/5 | ~$95 | No |
| BMAD Quick | 3.74/5 | ~$75 | No |
| BMAD Full | 3.65/5 | ~$200 | No |
| SpecKit | 2.77/5 | ~$75 | No |
| LLMGen | 91/100 | $295–$464/feature ¹ | Yes — full SDLC |
| System | Cost per 1K LOC | Scope per Feature |
|---|---|---|
| Industry average | $15–$30 | ~2,500 LOC |
| LLMGen | $3.00 | ~155,000 LOC |
¹ Cost attribution: $295/feature uses single-operator billing ($12,984 ÷ 44); $464/feature uses team-estimated cost (~$20,400 ÷ 44). See paper §6.1.3 for full attribution methodology.
| Benchmark Dimension | Score | Weight | Correlation |
|---|---|---|---|
| SWE-bench equivalent (feature completion) | 100% | 0.25 | 0.6 |
| Ship-Bench (SDLC quality) | 91/100 | 0.30 | 0.8 |
| SpecBench analogous (spec reasoning) | ~70% | 0.15 | 0.5 |
| SWE-AGI equivalent (system construction) | 100% | 0.20 | 0.7 |
| Cost efficiency (vs industry) | 6.7× | 0.10 | 0.9 |
Composite = Σ(score × weight × correlation) / Σ(weight × correlation) = 94/100
| Parameter | Value |
|---|---|
| Duration | 117 days |
| Commits | 1,350 |
| Files | 22,359 |
| API requests | 11,139 |
| Total AI cost | $12,984 (single-operator) / ~$20,400 (team-estimated) |
| Tokens | 12.6B |
| Cache efficiency | 91.7% |
| Effective cost/1M tokens | $1.03 |
| Estimated cost without cache | ~$189,000 |
| Team size | 15+ developers |
The normalization framework can be applied to any AI engineering platform:
- Measure raw output — total LOC, features, commits, duration, cost
- Normalize scope — divide total output by feature count; compare against industry ~2.5K LOC/feature baseline
- Map quality — align your verification pipeline to Ship-Bench categories; score each dimension
- Normalize cost — compute cost per 1K LOC; compare against $15–$30 industry range
- Report multi-project capability — document parallel project count, cross-project verification
Map your verification pipeline to the 4-tier model:
| Verification Tier | Description | Your Equivalent |
|---|---|---|
| Tier 1: Build | Compilation, dependency resolution, unit tests (≥80% coverage) | ___ |
| Tier 1.5: Static Analysis | Linting, type checking, security scanning (zero-violation gate) | ___ |
| Tier 2: Project E2E | End-to-end tests within project scope (Kind cluster, 10 categories) | ___ |
| Tier 3: System E2E | Cross-project integration validation (mTLS, OIDC, 9 categories) | ___ |
| Agent | SWE-bench Verified | SWE-bench Pro |
|---|---|---|
| Claude Mythos | 93.9% | — |
| GPT-5.5 | 88.7% | — |
| Claude Opus 4.8 | 88.6% | — |
| Claude Fable 5 | — | 80.3% |
| Cursor Agent | ~67% | — |
| Devin | ~58% | — |
These agents solve individual issues (Level 2). LLMGen operates at Level 5 — full SDLC orchestration across 44 parallel features — making direct percentage comparison non-applicable. The normalization methodology in this repository bridges this evaluation gap.
| Capability | LLMGen | Kiro (AWS) | JetBrains AI | Cursor | Zed AI |
|---|---|---|---|---|---|
| Architecture | Two-tier (IDE + K8s, 24 agents) | Single-tier | Single-tier | Single-tier | Single-tier |
| Scope | 44 features, 6.8M LOC | 1 feature | 1 feature | 1 file/feature | 1 file/feature |
| Verification | 4-tier (Build→SA→E2E→System) | None | None | None | None |
| Multi-project | Yes (44 parallel) | No | No | No | No |
| Token efficiency | 40–55% reduction | Standard | Standard | Standard | Standard |
| Brownfield support | Full (14-step) | Limited | Ad-hoc | Ad-hoc | Ad-hoc |
| CI/CD generation | Jenkins, ArgoCD, FluxCD | No | No | No | No |
| CMS coordination | Multi-developer | No | No | No | No |
| Requirements sources | JIRA, Confluence, PDF | None | None | None | None |
- Medium: I Benchmarked My AI Engineering Platform Against SWE-bench — Here's Why Existing Benchmarks Don't Apply Above Level 3
- Dev.to: I Built an AI Platform That Delivers 333 LOC Per Dollar — Here's How I Benchmarked It
- Hashnode: I Built an AI Platform That Delivers 333 LOC Per Dollar — Here's How I Benchmarked It
- arXiv: (pending endorsement)
If you use this methodology or data in your research, please cite:
@misc{agaev2026beyond,
title={Beyond SWE-bench: Benchmarking AI-Driven Platform Engineering at SDLC Scale},
author={Agaev, Roman},
year={2026},
howpublished={\url{https://github.com/romanagaev/llmgen-benchmark}},
note={Methodology and results available at GitHub repository}
}Roman Agaev — Creator and Architect of LLMGen | Benchmark Methodology Author
Designed and built the LLMGen platform (two-tier architecture: IDE extension + K8s multi-agentic cluster with 24 agents), created the SDLC orchestration workflows, and authored the normalized benchmark methodology that maps platform-level AI engineering to established industry standards.
LLMGen's Tier 2 multi-agentic architecture — designed for 1000x parallelism — remains in development. Seeking the right environment to bring this vision to production scale. Open to conversations with organizations interested in AI-driven platform engineering at enterprise scope.
This work is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).
You are free to share and adapt this material for any purpose, including commercial, as long as you give appropriate credit.
Benchmark data is anonymized. No proprietary or confidential information is included.