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Model Selection

This page gives workflow authors and runtime policy code a practical way to answer:
  • Which model should a workflow use by default?
  • Which model should it use for judgment gates, debugging, planning, research, cheap worker loops, and fallback diversity?
  • Which models are dominated on cost/accuracy and should be avoided unless they have a specific role fit?
It is a static reference. It does not change runtime model routing — routing is configured elsewhere. Treat these recommendations as a starting point and validate against your own workflow evals.
The table below is a snapshot of the DeepSWE leaderboard (v1.1, best effort per model), a long-horizon coding-agent benchmark reporting pass@1 and average dollars per task. Benchmarks and pricing drift and new models ship constantly, so treat the live leaderboards as authoritative and refresh this page from them rather than hand-maintaining scores. See Benchmark sources & when to reference each. Last compiled: 2026-07-17.

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Recommendation chart

The Pareto frontier — models where nothing else is both cheaper and more accurate — is currently gpt-5.6-sol (accuracy ceiling), gpt-5.6-terra, kimi-k3, gpt-5.6-luna, grok-4.5, and muse-spark-1.1. Everything else is dominated and earns a place only through role fit or provider diversity. For the frontier reasoning, see Pareto Efficiency.
Scores above are DeepSWE pass@1 with ±CI omitted for readability; see the live leaderboard for confidence intervals, output-token, and step counts. A model absent from both DeepSWE and Artificial Analysis should be marked unmeasured rather than assigned a guessed score — unmeasured models may still remain operational defaults.

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Scenario-based guidance

Pick by the cost of being wrong in each role, not by raw accuracy. Match the role to the benchmark that best measures it (see Benchmark sources).
  • Reviewer / judgment gates — a wrong verdict discards an entire loop, so pay for accuracy: gpt-5.6-sol [max] primary, gpt-5.6-terra as reviewer-B, and a different family (kimi-k3 or glm-5.2) as reviewer-C for decorrelated errors. Benchmark to weight: DeepSWE pass@1 and the AA Agentic Index.
  • Planner — gpt-5.6-terra [max] for high-accuracy planning at reasonable cost; gpt-5.6-sol when the plan gates an expensive loop.
  • Debugger — gpt-5.6-sol [max] primary; deep reasoning pays off where root-causing is expensive. Benchmark to weight: DeepSWE pass@1, Terminal-Bench.
  • Research — gpt-5.6-luna [max] as the workhorse, with kimi-k3 in the fallback chain for provider diversity. Benchmark to weight: AA-LCR (long context), AA-Omniscience (factual reliability).
  • Orchestrator / worker / cheap loops — gpt-5.6-luna [max] for the main worker and code-simplifier subagents; grok-4.5 [high] or muse-spark-1.1 [xhigh] for trivial mechanical one-shots.
  • Design — a quality-first, unbenchmarked domain; keep a top-tier model (gpt-5.6-sol or claude-fable-5) here and rely on human judgment rather than a score.
  • Interactive coding sessions — gpt-5.6-terra [max] as a balanced default.