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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.

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The frontier

Six models currently sit on the frontier, spanning “cheapest defensible worker” to “accuracy ceiling”:

• gpt-5.6-sol [max] — the accuracy axis (73% pass@1, $8.39). Nothing scores higher.
• gpt-5.6-terra [max] — 70% for $4.95. Matches claude-fable-5’s accuracy at roughly a quarter of the cost.
• kimi-k3 [max] — 69% for $4.65. Near-top accuracy at the lowest top-tier price; open weights.
• gpt-5.6-luna [max] — 67% for $3.03. The best value on the board for high-accuracy work.
• grok-4.5 [high] — 54% for $2.42. A cheap, capable mid-tier worker.
• muse-spark-1.1 [xhigh] — 53% for $2.36. The cheapest model still on the frontier.

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What changed — the frontier moved

The previous generation’s frontier (gpt-5.5 and claude-fable-5) is now dominated by the gpt-5.6 family:

• claude-fable-5 [max] (70%, $21.63) — matched on accuracy by **gpt-5.6-terra** (70$4.95) at ~4× lower cost.
• gpt-5.5 [xhigh] (67%, $7.23) — matched on accuracy by **gpt-5.6-luna** (67$3.03) at less than half the cost.

This is the reason to key these docs to the live benchmarks: a single release cycle reshaped the entire recommendation.

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Dominated models — and why

• claude-fable-5 [max] — superseded by gpt-5.6-terra on cost for equal accuracy.
• gpt-5.5 [xhigh] and gpt-5.4 [xhigh] — superseded by gpt-5.6-luna / grok-4.5.
• claude-opus-4.8 [max], claude-sonnet-5 [max] — dominated on cost/accuracy; sonnet-5 is the worst value on the chart.
• kimi-k2.7-code — superseded by kimi-k3 and undercut by muse-spark-1.1.
• claude-sonnet-4.6 [high], gemini-3.1-pro [high] — dominated; not in any chain.
• gemini-3.5-flash [medium] — dropped from reasoning roles (token hose at 276k output tokens); retained only at [low] in retrieval chains where token price dominates.

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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:

• glm-5.2 — kept as reviewer-C because a third model family decorrelates review errors, even though it is dominated on raw efficiency.
• kimi-k3 — frontier on efficiency and an open-weights provider-diversity option; a strong reviewer-C or fallback.
• claude-opus-4.8 [max] — retained for Anthropic provider diversity and its long-context niche.
• claude-fable-5 — kept where Anthropic-family behavior is specifically wanted, e.g. the quality-first, unbenchmarked design chain.
• Unmeasured models — any family in use on main without DeepSWE or Artificial Analysis coverage should be marked unmeasured. Being unmeasured is not the same as being dominated; such models may remain operational defaults until measured.

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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.

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Related

• Model Selection
• Benchmark sources & when to reference each