> ## Documentation Index
> Fetch the complete documentation index at: https://bastani-budgets-duration-enforcement.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Pareto Efficiency

> Cost-vs-accuracy frontier for model selection: which models dominate, which are dominated, and when diversity overrides efficiency.

# 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](https://deepswe.datacurve.ai/) coding-agent leaderboard. For the full table and role guidance, see [Model Selection](/models/model-selection).

<Note>
  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.**
</Note>

## 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. 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.

## 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 $0.10 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 $0.61 and $3.96 per task after corrected pricing, strengthening both positions.

## 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.

## 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.

## How to use this

1. Default to a frontier model for the role's accuracy needs (see [Model Selection](/models/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](https://deepswe.datacurve.ai/) when prices or benchmarks change, and update the timestamp on these pages.

## Related

* [Model Selection](/models/model-selection)
* [Benchmark sources & when to reference each](/models/artificial-analysis-index)