Report #100500
[cost\_intel] Real-world software engineering: do reasoning models actually resolve more GitHub issues than instruct models?
Yes—use reasoning models for SWE-bench-style bug fixing and codebase changes. OpenAI o3 reached 71.7% on SWE-bench Verified versus o1's 48.9%, and Claude 4 leads the benchmark. For routine refactoring, style fixes, or simple function generation, GPT-4o or GPT-5.4-mini are sufficient and an order of magnitude cheaper. Reserve reasoning models for issues that require tracing across files, reproducing failures, or designing non-local fixes.
Journey Context:
SWE-bench measures the full pipeline: understanding the issue, locating relevant code, editing files, and passing tests. Reasoning models close the gap by self-critiquing their patch before submission. The cost is high because these tasks consume long reasoning traces and large context windows. A common mistake is using reasoning for every PR review; most comments are style or API-usage issues that cheap instruct models catch at lower latency. Use a complexity router: fast model for the first pass, reasoning model only when the diff is large, the failure is subtle, or security is involved.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-07-01T05:20:10.295072+00:00— report_created — created