> ## Documentation Index
> Fetch the complete documentation index at: https://docs.bastani.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Model Selection

> Practical guidance for choosing models by workflow role, grounded in live coding-agent benchmarks (DeepSWE) and intelligence benchmarks (Artificial Analysis).

# 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.

<Note>
  The table below is a snapshot of the [DeepSWE](https://deepswe.datacurve.ai/) 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](/models/artificial-analysis-index). **Last compiled: 2026-07-17.**
</Note>

## 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](/models/pareto-efficiency).

| Model \[level]             | pass\@1 | \$/task | Verdict                            | Use it for                                                                                              |
| -------------------------- | ------- | ------- | ---------------------------------- | ------------------------------------------------------------------------------------------------------- |
| gpt-5.6-sol \[max]         | 73%     | \$8.39  | Accuracy ceiling / frontier        | Judgment gates where a wrong verdict wastes a whole loop, and the hardest debugging; current top scorer |
| gpt-5.6-terra \[max]       | 70%     | \$4.95  | Frontier — best top-tier value     | High-accuracy default for reviewers and planners; matches fable-5's accuracy at \~4× lower cost         |
| kimi-k3 \[max]             | 69%     | \$4.65  | Frontier                           | Near-top accuracy at the lowest top-tier cost; open weights, so also a provider-diversity pick          |
| gpt-5.6-luna \[max]        | 67%     | \$3.03  | Frontier — best value on the board | The workhorse: research, orchestrator, worker + code-simplifier subagents                               |
| gpt-5.5 \[xhigh]           | 67%     | \$7.23  | Superseded                         | luna matches 67% for \$3.03 — less than half the cost                                                   |
| claude-fable-5 \[max]      | 70%     | \$21.63 | Drop                               | terra matches 70% for \$4.95; kept only where Anthropic-family behavior is specifically wanted          |
| claude-opus-4.8 \[max]     | 59%     | \$13.22 | Fallback only                      | Dominated on cost/accuracy, but retained for Anthropic provider diversity and its long-context niche    |
| grok-4.5 \[high]           | 54%     | \$2.42  | Frontier (budget)                  | Cheap, capable worker; adds xAI provider diversity                                                      |
| claude-sonnet-5 \[max]     | 54%     | \$26.40 | Drop everywhere                    | Worst value on the chart; 268 steps of meandering                                                       |
| muse-spark-1.1 \[xhigh]    | 53%     | \$2.36  | Frontier (cheapest defensible)     | Cheapest model still on the frontier; open weights diversity                                            |
| gpt-5.4 \[xhigh]           | 52%     | \$5.65  | Superseded                         | grok-4.5 and luna dominate it on cost and accuracy                                                      |
| glm-5.2 \[max]             | 44%     | \$3.92  | Diversity only                     | Reviewer-C primary (a third model family decorrelates review errors); budget fallback elsewhere         |
| gemini-3.5-flash \[medium] | 37%     | \$7.34  | Drop from reasoning                | Token hose (276k output tokens); kept only at :low in retrieval chains where token price rules          |
| kimi-k2.7-code             | 31%     | \$2.82  | Drop                               | Dominated by muse-spark-1.1 (both cheaper reach); superseded by kimi-k3                                 |
| claude-sonnet-4.6 \[high]  | 30%     | \$5.52  | Drop everywhere                    | Removed from all chains                                                                                 |
| gemini-3.1-pro \[high]     | 12%     | \$9.48  | Drop everywhere                    | Value destruction; removed from all chains                                                              |

<Note>
  Scores above are DeepSWE `pass@1` with ±CI omitted for readability; see the [live leaderboard](https://deepswe.datacurve.ai/) 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.
</Note>

## 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](/models/artificial-analysis-index)).

* **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.

## Related

* [Pareto Efficiency](/models/pareto-efficiency) — cost-vs-accuracy frontier, dominated models, and provider-diversity exceptions.
* [Benchmark sources & when to reference each](/models/artificial-analysis-index) — what Artificial Analysis and DeepSWE measure, per benchmark, and how to keep these docs fresh from the live source.
* [Custom models](/models) — how to add model entries for supported provider APIs.