> ## 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.
# 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.
The table below is a snapshot of the [DeepSWE](https://deepswe.datacurve.ai/) leaderboard (v1.1, highest published thinking level 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: 2026-08-16.**
## Benchmark levels are measurement settings
The thinking level in brackets in the chart is the **measurement configuration used for that benchmark result**, not a universal workflow default. A score measured at `max` does not mean every stage using that model should use `max`; benchmark model identity and production thinking effort are separate choices. When authoring a workflow, choose effort from the stage role and cost of being wrong, then verify that the configured model catalog supports the level.
## Pin model identity
When a workflow needs an exact model, call `workflow({ action: "models" })` and pin a returned `fullId`. Do not pin a
bare model ID: the same exact model ID can belong to more than one provider. For a bare exact `--model` ID, Atomic
uses the sole matching provider with configured authentication; if none or more than one match is authenticated, it
reports the ambiguity. Use `--provider --model ` or `--model /` to choose explicitly.
## Recommendation chart
The current highest-effort-config Pareto frontier is **claude-opus-5** (accuracy ceiling), **gpt-5.6-sol**, **gpt-5.6-terra**, **gpt-5.6-luna**, **deepseek-v4-pro**, and **deepseek-v4-flash**. Everything else is dominated on DeepSWE cost and accuracy and earns a place only through role fit or provider diversity. For the frontier reasoning, see [Pareto Efficiency](/models/pareto-efficiency).
| Model \[benchmark measurement level] | pass\@1 | \$/task | Verdict | Use it for |
| ------------------------------------ | ---------- | ---------- | ------------------------------ | ---------------------------------------------------------------------------------- |
| claude-opus-5 \[max] | 74% | \$11.84 | Accuracy ceiling / frontier | Final approval and the hardest debugging when one more point can justify the cost |
| gpt-5.6-sol \[max] | 73% | \$8.39 | Frontier | High-cost judgment gates; nearly the top score at lower cost than Opus 5 |
| gpt-5.6-terra \[max] | 70% | \$3.96 | Frontier — best top-tier value | High-accuracy reviewers and planners |
| claude-fable-5 \[max] | 70% | \$21.63 | Drop | Sol and Terra match or beat its score for much less |
| kimi-k3 \[max] | 69% | \$4.65 | Diversity fallback | Open-weights diversity, though Terra is cheaper and more accurate on DeepSWE |
| gpt-5.6-luna \[max] | 67% | \$0.61 | Frontier — best general value | Research, orchestration, workers, and code simplification |
| gpt-5.5 \[xhigh] | 67% | \$7.23 | Superseded | Luna matches its score for less than one tenth of the task cost |
| grok-4.6 \[xhigh] | 67% | \$5.50 | Provider fallback | xAI diversity; Luna has the same rounded score at lower DeepSWE task cost |
| gemini-3.7-flash \[high] | 65% | \$2.18 | Provider fallback | Strong Google-family result, but Luna is cheaper and more accurate |
| deepseek-v4-pro \[max] | 63% | \$0.24 | Frontier (budget) | Low-cost work where its 155-step average remains acceptable |
| claude-opus-4.8 \[max] | 59% | \$13.22 | Fallback only | Anthropic diversity and long-context behavior, not cost efficiency |
| qwen3.8-max \[xhigh] | 57% | \$3.73 | Provider fallback | Qwen diversity only; DeepSeek Pro and Luna dominate it |
| muse-spark-1.2 \[xhigh] | 55% | \$3.70 | Drop | DeepSeek Pro is cheaper and more accurate |
| claude-sonnet-5 \[max] | 54% | \$26.40 | Drop everywhere | Highest task cost and 268 average steps for a mid-table score |
| grok-4.5 \[high] | 54% | \$2.42 | Superseded | Grok 4.6 adds 13 points; cheaper frontier models still dominate both generations |
| deepseek-v4-flash \[max] | 53% | \$0.10 | Frontier (cheapest) | Very cheap mechanical loops that can tolerate lower accuracy and 153 average steps |
| muse-spark-1.1 \[xhigh] | 53% | \$2.36 | Superseded | Replaced by Muse Spark 1.2 and dominated by DeepSeek Pro |
| gpt-5.4 \[xhigh] | 52% | \$5.65 | Superseded | Luna is cheaper and 15 points more accurate |
| gemini-3.6-flash \[high] | 47% | \$2.21 | Drop from reasoning | Superseded by Gemini 3.7 Flash |
| glm-5.2 \[max] | 44% | \$3.92 | Superseded | Measured predecessor only; do not relabel this as GLM-5.3 |
| gemini-3.5-flash \[high] | 36% | \$3.45 | Drop from reasoning | Retain only where a low-effort retrieval role has separate evidence |
| kimi-k2.7-code \[low] | 31% | \$2.19 | Superseded | Kimi K3 is the current family fallback |
| claude-sonnet-4.6 \[high] | 30% | \$5.52 | Drop everywhere | Removed from all chains |
| gemini-3.1-pro \[high] | 12% | \$2.14 | Drop everywhere | Removed from all chains |
| glm-5.3 \[high] | unmeasured | unmeasured | Diversity fallback | Current direct Z.AI fallback; GLM-5.2 measurements are not relabeled |
DeepSWE values above use the v1.1 results and reporting corrections published through August 14, 2026; `pass@1` is rounded as on the live leaderboard and confidence intervals are omitted here. The highest published thinking level is a measurement choice, not a production default. See the live page for intervals, output tokens, steps, lower-effort configurations, and later corrections.
## Role-based thinking effort
Use this table when the user has not requested a thinking level. It is a production default by stage role, not a claim about the level used by any benchmark row:
| Stage role | Default thinking level | Why |
| ---------------------------------------------------------------------------------------- | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
| Security, identity, adversarial challenge, final approval | `max` | A wrong judgment can create a high-risk false approval or waste a full downstream loop. |
| Codebase mapping, lifecycle analysis, compatibility, planning, synthesis, triage, repair | `high` | These stages must resolve demanding uncertainty and preserve evidence across handoffs; routine synthesis may use `medium` when evidence quality holds. |
| User-impact review and final reporting | `medium` | Clear evidence-backed summaries usually do not need the deepest reasoning. |
| Deterministic checks | No model call | Run typechecks, tests, schema checks, runtime probes, and artifact checks as durable tool nodes. |
Reserve `max` for a high-cost-of-error role or an explicit user request. An explicit request wins over this role default, but the requested level still must appear in the configured catalog; do not invent an unsupported suffix. For each primary and fallback, choose a level for the same stage role independently. A fallback is not a reason to inherit `max` mechanically: use the role default at a supported level, choose another catalog model when needed, or leave the stage unpinned rather than guessing.
## 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** — use `max` when the reviewer makes a security, identity, adversarial, or final-approval decision whose wrong verdict discards an entire loop. `claude-opus-5` is the DeepSWE accuracy ceiling; `gpt-5.6-sol` is the lower-cost near-peer. Use another family when decorrelated errors matter.
* **Codebase mapping / planner** — start at `high` for repository mapping, lifecycle analysis, compatibility, and plans. `gpt-5.6-terra` is the strongest top-tier value at its measured `max` configuration; raise production effort to `max` only when the plan gates a high-cost loop or the user asks for it.
* **Debugger / triage / repair** — start at `high`; deep reasoning pays off when root-causing or repairing is costly. Weight DeepSWE and Terminal-Bench together rather than treating either as a complete measure.
* **Research / synthesis** — use `high` for demanding research and evidence reconciliation; use `medium` for routine synthesis when the evidence is already strong. `gpt-5.6-luna` remains the workhorse. Benchmark to weight: AA-LCR and AA-Omniscience.
* **Orchestrator / worker / cheap loops** — Luna offers the best broad cost/accuracy balance. DeepSeek V4 Pro and Flash occupy the budget frontier but take 155 and 153 steps on average; use them only when that longer path fits. Use another family when provider diversity matters.
* **User-impact review / final reporting** — use `medium` for impact summaries and reports that preserve the evidence needed by the user. Do not spend `max` here unless the user explicitly requests it or the role has become a high-cost-of-error approval.
* **Design** — a quality-first, unbenchmarked domain; keep a top-tier model (`gpt-5.6-sol` or `claude-fable-5`) when the design decision has high failure cost, and choose effort by the review or approval role rather than by the benchmark row.
* **Interactive coding sessions** — use `high` for complex, multi-step coding and `medium` for routine edits; reserve `max` for a high-cost-of-error judgment or an explicit user request.
* **Deterministic checks** — make typechecks, tests, schema validation, runtime probes, and artifact inspection tool nodes with no model call. Model self-report is not verification evidence.
## 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.