Research note: Model specifications and benchmark results are time-bound. Check the dated primary sources below before using them for a technical or purchasing decision.
Choosing the right foundation model for production software requires cutting through vendor marketing hype and evaluating concrete empirical metrics: SWE-bench issue resolution, schema compliance, latency, and input/output token pricing.
1. A Decision Matrix That Ages Gracefully
| Decision signal | What to measure | Preferred evidence |
|---|---|---|
| Task quality | Success on your own representative tasks | Blind, versioned evaluation set |
| Reliability | Schema compliance, retries, and failure modes | Application logs and adversarial cases |
| Latency | Time to first token and end-to-end completion | Same-region production-like benchmark |
| Cost | Total task cost, including retries and tools | Current provider pricing plus measured usage |
| Operational fit | Limits, privacy, availability, and support | Current provider documentation and contract |
Sources & Benchmarks
- • SWE-bench Official Leaderboard: swebench.com — Real-world GitHub software engineering benchmark.
- • Artificial Analysis Pricing Matrix: artificialanalysis.ai — Independent LLM speed, quality, and price benchmarks.