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Pareto Efficiency

A model is Pareto-efficient (on the frontier) if no other model is both cheaper and more accurate. Everything not on the frontier is dominated — some other option matches or beats it on accuracy for less money — and should be avoided unless it earns a slot through a specific role fit or provider diversity. The axes here are pass@1 (accuracy) and average dollars per task (cost), taken from the DeepSWE coding-agent leaderboard. For the full table and role guidance, see Model Selection.
Figures are a snapshot of DeepSWE v1.1 using the highest published thinking level per model and reporting corrections through August 14, 2026. The frontier moves whenever a model, run, or price changes — DeepSWE publishes a live cost-vs-score scatter, so read the frontier off the live chart rather than trusting a static list. Last compiled: 2026-08-16.

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The frontier

Six highest-effort model configurations currently sit on the frontier, from the cheapest measured task cost to the accuracy ceiling:
  • deepseek-v4-flash [max] — 53% for $0.10. The cheapest point, with lower accuracy and 153 average steps.
  • deepseek-v4-pro [max] — 63% for 0.24.Alargeaccuracygainforanother0.24. A large accuracy gain for another 0.14 per task, with 155 average steps.
  • gpt-5.6-luna [max] — 67% for $0.61. The best broad value on the board.
  • gpt-5.6-terra [max] — 70% for $3.96. The best top-tier value after the July price correction.
  • gpt-5.6-sol [max] — 73% for $8.39. The lower-cost near-peer to the accuracy leader.
  • claude-opus-5 [max] — 74% for $11.84. The current accuracy ceiling.

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What changed — the frontier moved

The August results and reporting fixes changed both ends of the frontier:
  • Claude Opus 5 [max] set a new 74% ceiling, one point above Sol, at $11.84 per task.
  • DeepSeek V4 Flash and Pro [max] added 0.10and0.10 and 0.24 budget-frontier points. Their 153–155 average steps remain a separate latency and loop-length cost.
  • GPT-5.6 Luna and Terra [max] now cost 0.61and0.61 and 3.96 per task after corrected pricing, strengthening both positions.

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Dominated models — and why

  • claude-fable-5 [max] — Sol is more accurate and much cheaper; Terra matches its rounded score for less than one fifth of the task cost.
  • kimi-k3 [max] — Terra is one point more accurate and $0.69 cheaper; Kimi remains useful for open-weights diversity.
  • gpt-5.5 [xhigh] and grok-4.6 [xhigh] — Luna matches their rounded 67% for $0.61.
  • gemini-3.7-flash [high] — Luna is two points more accurate and less than one third of its task cost.
  • grok-4.5 [high], muse-spark-1.1 [xhigh], muse-spark-1.2 [xhigh], and gpt-5.4 [xhigh] — the new DeepSeek and GPT-5.6 points dominate these former budget choices.
  • claude-opus-4.8 [max] and claude-sonnet-5 [max] — dominated on both cost and accuracy.
  • qwen3.8-max [xhigh], gemini-3.6-flash [high], gemini-3.5-flash [high], glm-5.2 [max], kimi-k2.7-code [low], claude-sonnet-4.6 [high], and gemini-3.1-pro [high] — each has a cheaper, more accurate measured alternative.

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Diversity and role-fit exceptions

Efficiency is not the only axis. A dominated model can still earn a slot when it decorrelates errors or fills a niche:
  • grok-4.6 — retained as the operational xAI and OpenRouter provider-diversity fallback.
  • glm-5.3 [high] — unmeasured in the current DeepSWE snapshot; retained as a direct Z.AI provider-diversity fallback. GLM-5.2 results are not relabeled as GLM-5.3.
  • kimi-k3 — retained as a strong open-weights provider-diversity option despite Terra’s strict DeepSWE dominance.
  • claude-opus-4.8 [max] — retained where Anthropic diversity or its long-context behavior has separate value.
  • claude-fable-5 — kept where Anthropic-family behavior is specifically wanted, such as the quality-first, unbenchmarked design chain.
  • Unmeasured models — a family without current DeepSWE or Artificial Analysis coverage may remain an operational default, but should not inherit a predecessor’s score.

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How to use this

  1. Default to a frontier model for the role’s accuracy needs (see Model Selection).
  2. Only reach for a dominated model when you have an explicit reason — provider diversity, a long-context or token-price niche, or an unbenchmarked domain like design.
  3. Re-read the frontier off the DeepSWE live chart when prices or benchmarks change, and update the timestamp on these pages.