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 leaderboard (v1.1, highest published thinking level per model), a long-horizon coding-agent benchmark reporting pass@1 and average dollars per task. The source reports 113 tasks and was updated September 3, 2026. DeepSWE’s own default table view is Best — the best-scoring configuration per model — so four models show a different row there than the highest-effort row used here; the snapshot note below the table names them. 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. Last compiled: 2026-09-03.

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 <provider> --model <id> or --model <provider>/<id> to choose explicitly.

Recommendation chart

The current highest-effort-config Pareto frontier is gemini-3.8-flash (accuracy ceiling), gpt-5.6-luna, and glm-5.3-flash (cheapest point). It collapsed from five members to three when Gemini 3.8 Flash landed on September 1, 2026: at 73.83% unrounded it edges out claude-opus-5’s 73.65% — both display 74% — for

2.36 against

11.84, which pushes claude-opus-5, gpt-5.6-sol, and glm-5.3 off the frontier. Everything else displayed on the live DeepSWE leaderboard is dominated on cost and accuracy and earns a place only through role fit or provider diversity. For the frontier reasoning, see Pareto Efficiency.

Model [benchmark measurement level]	pass@1	$/task	Verdict	Use it for
gemini-3.8-flash [high]	74%	$2.36	Frontier — accuracy ceiling and best value	Judgment gates, hard debugging, and any role that wants top accuracy without top-tier cost; budget for 166 average steps and 143k output tokens per task
claude-opus-5 [max]	74%	$11.84	Dominated — top-tier role fit	Final approval and the hardest debugging when Anthropic-family behavior is specifically wanted; it matches Gemini 3.8 Flash’s rounded 74% but costs $9.48 more per task
gpt-6-astra [max]	73%	$12.37	Dominated — new arrival	Added September 3, 2026 at DeepSWE’s expected launch pricing rather than billed rates; its 28 average steps are the fewest of any row in this table, but Opus 5 scores higher for less
gpt-5.6-sol [max]	73%	$6.46	Near-peer, no longer on the frontier	High-cost judgment gates where OpenAI-family behavior is wanted; still about half Opus 5’s task cost, but Gemini 3.8 Flash is more accurate at just over a third of Sol’s task cost
gpt-5.6-terra [max]	70%	$3.96	Historical — outside the default selection	Last published measurement, re-verified unchanged in the September 3, 2026 artifact; DeepSWE deselects it by default, so re-enable it on the live page before relying on it
claude-fable-5 [max]	70%	$21.63	Drop	Sol matches or beats its score for much less, and Gemini 3.8 Flash is four rounded points better for about a ninth of the task cost
glm-5.3 [max]	69%	$3.99	Best open-weights value, off the frontier	Best open-weights mid-tier cost/accuracy point; matches Kimi K3’s rounded score for less, though Gemini 3.8 Flash is five rounded points better for $1.63 less
kimi-k3 [max]	69%	$4.65	Dominated	GLM-5.3 matches its rounded score for $0.66 less; Moonshot-family diversity only
gpt-5.6-luna [max]	67%	$0.61	Frontier — cheapest broadly-capable point	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, and Gemini 3.8 Flash supersedes it inside the Google family at 74% for $2.36
glm-5.3-flash [max]	63%	$0.24	Frontier — cheapest	Budget worker loops that can accept lower accuracy and 123 average steps
deepseek-v4-pro [max]	63%	$1.67	Dominated / provider fallback	DeepSeek diversity only; GLM-5.3 Flash has a higher unrounded score, fewer steps, and about one seventh of the cost
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; GLM-5.3 Flash and Luna dominate it
muse-spark-1.2 [xhigh]	55%	$3.70	Drop	GLM-5.3 Flash 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	Historical — outside the default selection	Last published measurement, re-verified unchanged; superseded by Grok 4.6 and dominated by current frontier models
deepseek-v4-flash [max]	53%	$0.46	Dominated / provider fallback	DeepSeek diversity only; GLM-5.3 Flash is ten points more accurate for about half the cost
muse-spark-1.1 [xhigh]	53%	$2.36	Historical — outside the default selection	Last published measurement, re-verified unchanged; replaced by Muse Spark 1.2 and dominated by current frontier models
gpt-5.4 [xhigh]	52%	$5.65	Historical — outside the default selection	Last published measurement, re-verified unchanged; 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	31%	$2.82	Historical — outside the default selection	Last published measurement had no effort level; Kimi K3 is the current family fallback
claude-sonnet-4.6 [high]	30%	$5.52	Historical — outside the default selection	Last published measurement, re-verified unchanged; removed from all chains
gemini-3.1-pro-preview [high]	12%	$2.14	Historical — outside the default selection	Last published measurement, re-verified unchanged; the live page labels it `gemini-3.1-pro`; removed from all chains

DeepSWE values above use the v1.1 results published in the September 3, 2026 snapshot, including the August 21 pricing corrections for GPT-5.6 Sol and DeepSeek V4. Sol’s cost reflects OpenAI’s promotional input and output price cut through at least November 21, 2026. DeepSWE uses DeepSeek’s peak rates; its off-peak rates are half as much. GPT-6 Astra’s costs are DeepSWE’s expected-launch-pricing estimate rather than billed rates, and every Astra dollar figure on these pages carries that caveat. pass@1 is rounded as on the live leaderboard and confidence intervals are omitted here — but note that the top of the board is a cluster: the top three rows span less than a point unrounded, well inside DeepSWE’s published run-to-run intervals, so read a one-row lead as a tie. The highest published thinking level is a measurement choice, not a production default, and DeepSWE’s own data shows effort saturation: for GPT-6 Astra, Claude Fable 5, Grok 4.6, and Gemini 3.7 Flash the best-scoring configuration is not the highest one. That is why DeepSWE’s default “Best” table view displays four rows this table does not: gpt-6-astra [xhigh] at 74% for

6.52 with 29 average steps, `claude-fable-5 [xhigh]` at 70% for

13.41, grok-4.6 [medium] at 67% for

3.45, and `gemini-3.7-flash [medium]` at 65% for

2.03. Seven measured configurations are retained here with their last published values because DeepSWE excludes them from its default model selection, not because they were withdrawn; each was re-verified unchanged against the September 3, 2026 artifact and can be re-enabled in the site’s model picker: GPT-5.6 Terra, Grok 4.5, Muse Spark 1.1, GPT-5.4, Kimi K2.7 Code, Claude Sonnet 4.6, and Gemini 3.1 Pro Preview. A DeepSWE row is not a claim that Atomic can route the model: run workflow({ action: "models" }) or --list-models to confirm that Gemini 3.8 Flash or GPT-6 Astra is in your configured catalog before pinning either. See the live page for intervals, output tokens, steps, lower-effort configurations, and later corrections.

Claude Fable 5.1 is in Atomic’s catalog and is not in the table above. It was released September 1, 2026 and is still absent from the September 3, 2026 DeepSWE snapshot this page is compiled from — DeepSWE has not measured it — so it has no measured pass@1 or $/task here. Do not read the claude-fable-5 row as a Fable 5.1 result: the two models differ in price and behavior, and an unmeasured model must not inherit its predecessor’s score. Benchmark it on your own workflow evals before promoting it into a stage.What is source-backed for claude-fable-5-1 today, from Anthropic’s model overview: a 1M-token context window and 128K maximum output; adaptive thinking that is always on, with effort low, medium, high, xhigh, and max and an Anthropic default of high; a June 2026 knowledge cutoff; and

10 input,

50 output,

12.50 five-minute cache write,

20 one-hour cache write, and

0.25 cache read per million tokens. The cache read is a quarter of Fable 5's

1.00, which is the main pricing reason to prefer it for long agentic sessions that re-read a cached prefix. Non-default temperature, top_p, and top_k return a 400 on every request, so Atomic omits temperature for this model.Atomic generates Fable 5.1 for the providers it has a matching runtime integration for. At the time of writing that is Anthropic, three Amazon Bedrock inference profiles (anthropic., global., and us.), OpenRouter, and the Vercel AI Gateway; a provider “latest” alias such as OpenRouter’s ~anthropic/claude-fable-latest may also route to it without naming it. That set genuinely moves — opencode zen published the model and then withdrew it while this page was being written — so run workflow({ action: "models" }) or --list-models for the current list rather than trusting this one. Published catalogs also list the model on Google Vertex, Google Vertex (Anthropic), Azure, and Azure Cognitive Services; Atomic has no Claude runtime integration for those providers and generates no entries for them, which is a current limitation rather than a roadmap commitment. What does not vary is the invariant that matters: Atomic’s preserved-thinking handling is scoped to provider: "anthropic" on the anthropic-messages API and applies to none of the other mirrors — including the Vercel AI Gateway, which rides anthropic-messages but is deliberately excluded. See Preserved thinking and model switches.

Gemini 3.8 Flash is in Atomic’s catalog and is not in the table above. It became generally available September 2, 2026, after the August 26, 2026 DeepSWE snapshot this page is compiled from, so it has no measured pass@1 or $/task here. Do not read the gemini-3.7-flash [high] | 65% | $2.18 row as a 3.8 Flash result; an unmeasured model must not inherit its predecessor’s score. Benchmark it on your own workflow evals before promoting it into a stage.What is source-backed for gemini-3.8-flash today, from Google’s model page, the developer’s guide, and the model card: a 1,048,576-token context window and 65,536 maximum output tokens; text, image, video, audio, and PDF input with text output; thinking with effort low, medium, and high; and

0.75 input,

3.75 output, and $0.075 cache read per million tokens. Google publishes no knowledge cutoff for it. Atomic advertises text and image input for this model because those are the inputs it can serialize on the Gemini path. Thinking cannot be turned off, and Google’s guide states that MINIMAL is unsupported, so the Google, Google Vertex, and opencode zen entries offer low, medium, and high. Google stopped publishing MINIMAL from Gemini 3.7 Flash onward; Atomic’s 3.7 Flash entries still offer it, which is a separate pre-existing gap.Atomic generates entries only from the upstream provider catalogs it supports, so the exact provider set and metadata can change. Run workflow({ action: "models" }) or --list-models for the current result. The current models.dev GitHub Copilot row advertises a 1,000,000-token context window, 64,000 maximum output tokens, and low, medium, and high reasoning efforts. Atomic consumes that row without supplementing or overriding it and routes it through Copilot’s OpenAI-compatible endpoint. Copilot availability still depends on GitHub’s rollout and administrator policy. The Vercel AI Gateway currently advertises a 1,000,000-token context window and no per-model thinking levels, so its entry offers off and minimal alongside the three Google levels.

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).

Reviewer / judgment gates — use max when the reviewer makes a security, identity, adversarial, or final-approval decision whose wrong verdict discards an entire loop. gemini-3.8-flash is the DeepSWE accuracy ceiling and reaches it at a fraction of top-tier cost; claude-opus-5 and gpt-5.6-sol are the near-peers when a different family is wanted. Use another family when decorrelated errors matter.
Codebase mapping / planner — start at high for repository mapping, lifecycle analysis, compatibility, and plans. gemini-3.8-flash is the strongest top-tier value at its measured high configuration, gpt-5.6-sol is the OpenAI-family near-peer, and glm-5.3 holds the open-weights mid tier; 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. GLM-5.3 Flash is the cheapest live frontier point at 63% for $0.24 with 123 average steps. Gemini 3.8 Flash is the most accurate frontier point but averages 166 steps and 143k output tokens per task, which makes it a judgment-gate choice rather than an automatic worker default. DeepSeek V4 Pro and Flash are provider-diversity options, not budget-frontier choices.
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. claude-fable-5-1 is the newer Anthropic model in this family and is also unmeasured here; treat it as a candidate to evaluate rather than a drop-in replacement, and do not carry Fable 5’s row over to it.
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.

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

Start here

Customization

Reference

Programmatic Usage

Platform Setup

Development

Changelog

Model Selection

Model Selection

Benchmark levels are measurement settings

Pin model identity

Recommendation chart

Role-based thinking effort

Scenario-based guidance

​Model Selection

​Benchmark levels are measurement settings

​Pin model identity

​Recommendation chart

​Role-based thinking effort

​Scenario-based guidance

​Related

Model Selection

Benchmark levels are measurement settings

Pin model identity

Recommendation chart

Role-based thinking effort

Scenario-based guidance

Related