Add SubQ Snap benchmark results

.gitattributes

@@ -62,3 +62,4 @@ predictions/current/*/gliner25-decide.jsonl filter=lfs diff=lfs merge=lfs -text
 predictions/current/*/openai-decisions.jsonl filter=lfs diff=lfs merge=lfs -text
 predictions/current/*/mercury-decide.jsonl filter=lfs diff=lfs merge=lfs -text
 predictions/current/*/microsoft-decision-1.jsonl filter=lfs diff=lfs merge=lfs -text
+predictions/current/security/subq-snap.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -27,6 +27,7 @@ Pairs come from open-source projects in Rust, JavaScript, TypeScript, Python, Go
 | [GLiNER-2.5-Decide](https://huggingface.co/fastino/GLiNER2.5-Decide) | [Fastino classification API](https://api.fastino.ai/v1/chat/completions) (`fastino/GLiNER-2.5-Decide`) |
 | [Mercury Decide](https://openrouter.ai/inception/mercury-decide) | [OpenRouter SystemOne API](https://openrouter.ai/api/v1/systemone) (`inception/mercury-decide`) |
 | [Microsoft-Decision-1](https://openrouter.ai/microsoft/microsoft-decision-1) | [OpenRouter SystemOne API](https://openrouter.ai/api/v1/systemone) (`microsoft/microsoft-decision-1`, Azure) |
+| [SubQ Snap Preview](https://docs.subq.ai/using-the-api/models/) | [SubQ Decisions API](https://api.subq.ai/v1/systemone) (`subq-snap-preview`) |
 
 <!-- BEGIN TRADEOFF_RESULTS -->
 
@@ -50,6 +51,7 @@ Failed requests count as incorrect decisions: a failed issue is a miss; a failed
 | --- | ---: | ---: | ---: | ---: |
 | [GPT-6 Luna Decisions](https://developers.openai.com/api/docs/guides/decisions) | 48.7% | 38.7% | **10.0%** | $1.102 |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 33.4% | 27.1% | 6.3% | $0.239 |
+| [SubQ Snap Preview](https://docs.subq.ai/using-the-api/models/) | 15.3% | 9.9% | 5.3% | $0.568 |
 | [Microsoft-Decision-1](https://openrouter.ai/microsoft/microsoft-decision-1) | 24.0% | 18.8% | 5.1% | $0.481 |
 | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 17.7% | 12.7% | 5.0% | $0.551 |
 | [SemiF](https://github.com/TheoLeeCJ/SemIf) | 43.0% | 39.7% | 3.3% | $0.239 |
@@ -69,6 +71,7 @@ Failed requests count as incorrect decisions: a failed issue is a miss; a failed
 | --- | ---: | ---: | ---: | ---: |
 | [GPT-6 Luna Decisions](https://developers.openai.com/api/docs/guides/decisions) | 52.4% | 41.3% | **11.1%** | $0.247 |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 35.8% | 27.3% | 8.6% | $0.054 |
+| [SubQ Snap Preview](https://docs.subq.ai/using-the-api/models/) | 19.3% | 11.2% | 8.2% | $0.133 |
 | [TypeLLM](https://typellm.ai/docs) | 11.3% | 5.8% | 5.6% | $0.129 |
 | [Microsoft-Decision-1](https://openrouter.ai/microsoft/microsoft-decision-1) | 25.5% | 20.6% | 4.8% | $0.108 |
 | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 20.0% | 15.6% | 4.5% | $0.123 |
@@ -94,6 +97,7 @@ Failed requests count as incorrect decisions: a failed issue is a miss; a failed
 | [TypeLLM](https://typellm.ai/docs) | 27.8% | 17.4% | 10.5% | $0.566 |
 | [GPT-6 Luna Decisions](https://developers.openai.com/api/docs/guides/decisions) | 54.1% | 44.5% | 9.7% | $1.469 |
 | [Mercury Decide](https://openrouter.ai/inception/mercury-decide) | 27.1% | 17.8% | 9.3% | **$0.131** |
+| [SubQ Snap Preview](https://docs.subq.ai/using-the-api/models/) | 24.7% | 15.9% | 8.8% | $0.817 |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 33.1% | 25.8% | 7.3% | $0.208 |
 | [Microsoft-Decision-1](https://openrouter.ai/microsoft/microsoft-decision-1) | 43.9% | 38.2% | 5.6% | $0.455 |
 | [Span-01](https://www.respan.ai/docs/documentation/span-01/concept) | 21.0% | 17.2% | 3.9% | $0.251 |
@@ -114,6 +118,7 @@ Failed requests count as incorrect decisions: a failed issue is a miss; a failed
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 21.9% | 18.5% | 3.3% | $0.074 |
 | [TypeLLM](https://typellm.ai/docs) | 8.8% | 6.2% | 2.5% | $0.203 |
 | [Mercury Decide](https://openrouter.ai/inception/mercury-decide) | 11.1% | 9.0% | 2.1% | **$0.047** |
+| [SubQ Snap Preview](https://docs.subq.ai/using-the-api/models/) | 10.3% | 8.3% | 2.0% | $0.278 |
 | [Microsoft-Decision-1](https://openrouter.ai/microsoft/microsoft-decision-1) | 18.1% | 16.7% | 1.4% | $0.163 |
 | [GLiNER-2.5-Decide](https://huggingface.co/fastino/GLiNER2.5-Decide) | 2.0% | **1.5%** | 0.6% | $0.142 |
 | [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | **100.0%** | 100.0% | 0.0% | ~$0.528 |
@@ -132,6 +137,7 @@ Failed requests count as incorrect decisions: a failed issue is a miss; a failed
 | --- | ---: | ---: | ---: | ---: |
 | [Mercury Decide](https://openrouter.ai/inception/mercury-decide) | **81.8%** | 6.5% | **75.2%** | $0.020 |
 | [GPT-6 Luna Decisions](https://developers.openai.com/api/docs/guides/decisions) | 76.6% | 4.7% | 72.0% | $0.093 |
+| [SubQ Snap Preview](https://docs.subq.ai/using-the-api/models/) | 71.5% | 2.8% | 68.7% | $0.045 |
 | [TypeLLM](https://typellm.ai/docs) | 61.7% | 2.8% | 58.9% | $0.050 |
 | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 50.0% | 0.5% | 49.5% | $0.048 |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 52.8% | 5.1% | 47.7% | $0.018 |
@@ -148,6 +154,7 @@ Failed requests count as incorrect decisions: a failed issue is a miss; a failed
 | --- | ---: | ---: | ---: | ---: |
 | [Mercury Decide](https://openrouter.ai/inception/mercury-decide) | **87.3%** | 8.5% | **78.9%** | $0.006 |
 | [GPT-6 Luna Decisions](https://developers.openai.com/api/docs/guides/decisions) | 84.5% | 7.0% | 77.5% | $0.030 |
+| [SubQ Snap Preview](https://docs.subq.ai/using-the-api/models/) | 71.8% | 4.2% | 67.6% | $0.016 |
 | [TypeLLM](https://typellm.ai/docs) | 59.2% | 5.6% | 53.5% | $0.016 |
 | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 53.5% | 1.4% | 52.1% | $0.016 |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 53.5% | 8.5% | 45.1% | $0.006 |
@@ -166,6 +173,8 @@ Span-01 uses its [behavior-scoring API](https://www.respan.ai/docs/documentation
 
 Liquid d1 received the same Perch method state and all applicable questions in one API call per method. Semantic lint used the same compiled `ensure` rule as Jev.
 
+SubQ Snap Preview uses its native noul probabilities with an 8,192-token limit reported by the server. Perch sizes the method context and batches its questions; large methods use overlapping source readings that cover every target-source byte. Each question’s highest issue score supplies the method decision. Input-token usage returned by SubQ is priced at its [published $0.05 per million rate](https://docs.subq.ai/using-the-api/models/).
+
 GLiNER-2.5-Decide scores the answer criteria as native classification labels. Perch sizes each method's context and batches its applicable questions to fit the endpoint. Large methods are read in overlapping source chunks; each question's highest issue score supplies the method decision. Returned label confidences supply the scores. Timeouts and requests that still cannot fit are recorded as failures. Costs use API-reported prompt tokens at the live catalog rate of $0.03 per million input tokens.
 
 Inference costs cover scoring the method sides in each table. Jev costs are estimated from API-reported input tokens at [TypeSafe's published rate](https://typesafe.ai/blog/introducing-system-one-models-and-jev); for semantic lint, input-token usage was measured by replaying the same 570 requests with `jev-1.13.0`. Span-01 costs use API-reported input tokens at [Respan's published rate](https://www.respan.ai/docs/documentation/span-01/concept). Beam costs are API-reported. CLM cost is an estimated share of a 38.4-minute H100 run costing $2.23 total, allocated by embedding count. Costs are not ranked. TypeLLM cost uses API-reported input tokens at its [published rate](https://typellm.ai/pricing); no thinking tokens were used.
@@ -190,6 +199,7 @@ Liquid d1, Mercury Decide and Microsoft-Decision-1 receive each method's saved c
 - [`perch-sizing.mjs`](https://huggingface.co/datasets/perchscan/benchmark-results/blob/main/perch-sizing.mjs): uses Perch's source chunking, context sizing, and question batching. Each reading records its source-byte coverage.
 - [`openai_decisions.py`](openai_decisions.py) and [`openai-decisions-manifest.json`](openai-decisions-manifest.json): resumable Decisions API scorer and public benchmark file checksums.
 - [`openrouter_decisions.py`](openrouter_decisions.py) and [`openrouter-decisions-manifest.json`](openrouter-decisions-manifest.json): resumable native SystemOne scorer and public benchmark file checksums.
+- [`subq.py`](subq.py): SubQ Decisions scorer using the shared native runner and Perch’s context sizing.
 - [`perch-lint-bench-public`](https://huggingface.co/datasets/perchscan/perch-lint-bench-public): paired rules and its runnable scorer.
 
 To run Jev with the current pack, set `TYPESAFE_API_KEY` or `PERCH_API_KEY` in your environment:
@@ -241,6 +251,14 @@ python3 openrouter_decisions.py --model microsoft/microsoft-decision-1 --manifes
 
 The paid endpoints use the full saved context. Mercury’s free endpoint is available with `--model inception/mercury-decide:free`; its daily account quota can pause a run. Rerun the same command to resume saved outcomes.
 
+To run SubQ Snap Preview, use the public files and manifest above, install `httpx`, and set `SUBQ_API_KEY` and `PERCH_SOURCE` to a [Perch checkout](https://github.com/lakeday-org/perch) with its npm dependencies installed. Node must be on your PATH:
+
+```sh
+python3 subq.py --manifest openrouter-decisions-manifest.json --out subq-results
+```
+
+Rerun the same command to resume saved outcomes.
+
 ## License
 
 Everything here except the scripts is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/): the results, predictions, question pack and this card. The scripts (`*.py`, `*.mjs` and `adapters/`) are licensed under MIT. The pair data is in the [bug](https://huggingface.co/datasets/perchscan/bug-pairs-public), [security](https://huggingface.co/datasets/perchscan/security-pairs-public) and [lint](https://huggingface.co/datasets/perchscan/perch-lint-bench-public) datasets, each with its own license. See their cards for the data sources.

openrouter_decisions.py

@@ -22,6 +22,10 @@ import openai_decisions as shared
 
 ROOT = Path(__file__).resolve().parent
 API = 'https://openrouter.ai/api/v1/systemone'
+API_LABEL = 'OpenRouter'
+KEY_ENV = 'OPENROUTER_API_KEY'
+COST_BASIS = 'OpenRouter API-reported usage.cost'
+HOST_PREFIX = 'OpenRouter / '
 CATALOG = {
     'inception/mercury-decide:free': ('mercury-decide', 'inception/mercury-decide-20260930',
         'mercury-decide-perch-systemone-v1', 'mercury-decide-20261009', 'Inception', 65536, 0),
@@ -87,7 +91,7 @@ def payload_for(pair, role, domain, pack, model):
 
 def prediction(pair, role, domain, pack, response, elapsed, model):
     if response.get('model') != REVISION:
-        raise AccountError('OpenRouter returned a different checkpoint: ' + str(response.get('model')))
+        raise AccountError(API_LABEL + ' returned a different checkpoint: ' + str(response.get('model')))
     checks = questions_for(pair, domain, pack)
     answers = response.get('answers', {})
     if not isinstance(answers, dict) or set(answers) != {c['name'] for c in checks}:
@@ -133,13 +137,14 @@ def predict(key, pair, role, domain, pack, timeout, limiter, model):
         response = _local.client.post(API, json=payload, timeout=timeout)
         if response.status_code >= 400:
             try:
-                detail = response.json().get('error', {})
+                error_body = response.json()
+                detail = error_body.get('error') or error_body.get('detail') or {}
             except ValueError:
                 detail = {}
             metadata = detail.get('metadata') or {}
             code = metadata.get('error_type') or detail.get('code') or response.status_code
             if response.status_code in (401, 402, 403, 404):
-                raise AccountError(f'OpenRouter HTTP {response.status_code}: {code}')
+                raise AccountError(f'{API_LABEL} HTTP {response.status_code}: {code}')
             if response.status_code == 429:
                 # A daily quota cannot be fixed by retrying this method.
                 remaining = response.headers.get('x-ratelimit-remaining')
@@ -157,7 +162,7 @@ def predict(key, pair, role, domain, pack, timeout, limiter, model):
                         delay = 2 ** (attempt + 1)
                     time.sleep(min(30, max(1, delay)))
                     continue
-            raise RuntimeError(f'OpenRouter HTTP {response.status_code}: {code}')
+            raise RuntimeError(f'{API_LABEL} HTTP {response.status_code}: {code}')
         return prediction(pair, role, domain, pack, response.json(),
                           time.monotonic() - started, model)
     raise AssertionError('unreachable')
@@ -173,8 +178,8 @@ def report(group, pack, model):
     result.update(model_id=MODEL_ID, model_revision=REVISION, requested_model=model,
                   protocol_id=PROTOCOL, run_id=RUN_ID,
                   inference_cost_usd=sum(r.get('usage', {}).get('cost', 0) for r in rows),
-                  inference_cost_basis='OpenRouter API-reported usage.cost',
-                  inference_endpoint=API, inference_host='OpenRouter / ' + HOST,
+                  inference_cost_basis=COST_BASIS,
+                  inference_endpoint=API, inference_host=HOST_PREFIX + HOST,
                   context_limit_tokens=CONTEXT_LIMIT)
     return result
 
@@ -252,9 +257,9 @@ def main():
     spent = save_reports()
     if args.score_saved or not pending:
         return
-    key = os.environ.get('OPENROUTER_API_KEY')
+    key = os.environ.get(KEY_ENV)
     if not key:
-        raise AccountError('Set OPENROUTER_API_KEY')
+        raise AccountError('Set ' + KEY_ENV)
     if args.model.endswith(':free'):
         limits = httpx.get('https://openrouter.ai/api/v1/key',
             headers={'Authorization': 'Bearer ' + key}, timeout=args.timeout)
@@ -268,6 +273,8 @@ def main():
             raise AccountError('OpenRouter daily free request allowance exhausted')
         pending = pending[:remaining]
         print(f'Free allowance: scheduling at most {len(pending)} requests today.', flush=True)
+    if args.budget is not None and CONTEXT_LIMIT is None:
+        parser.error('A spending cap requires a published context limit; account credit still limits spending')
     limiter = RequestLimiter(args.requests_per_second)
     iterator, attempted = iter(pending), 0
     reserved = 0.
@@ -283,7 +290,7 @@ def main():
             job = next(iterator, None)
             if job:
                 group, pair, role = job
-                bound = maximum_charge(args.model, len(questions_for(pair, group['domain'], pack)))
+                bound = maximum_charge(args.model, len(questions_for(pair, group['domain'], pack))) if args.budget is not None else 0.
                 if args.budget is not None and spent + reserved + bound > args.budget:
                     budget_paused = True
                     return

perch-sizing.mjs

@@ -45,7 +45,17 @@ function methodInput(side) {
   const callees = (state.calls ?? []).map(neighbor), callers = (state.called_by ?? []).map(neighbor);
   const edges = (state.call_graph ?? []).map(edge => Array.isArray(edge) ? edge : String(edge).split(' -> '))
     .filter(edge => edge.length === 2).map(edge => edge.map(name => names.get(name) ?? name));
-  return { node, lines, callees, callers, edges };
+  const imports = (state.imports ?? []).map(item => {
+    if (typeof item !== 'string') return item;
+    const from = item.lastIndexOf(' from ');
+    if (from < 0) throw new Error('Saved import has no module: ' + item);
+    const target = item.slice(0, from), module = item.slice(from + 6);
+    const as = target.lastIndexOf(' as ');
+    const name = as < 0 ? target : target.slice(0, as);
+    const alias = as < 0 ? name : target.slice(as + 4);
+    return { name, alias, module };
+  });
+  return { node, lines, imports, moduleScopeText: state.module_scope ?? null, callees, callers, edges };
 }
 
 function plan({ side, checks, stateBudget, requestBudget, maxQuestions }) {

predictions/current/security/subq-snap.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a61de1126c4ec1bf4a53c1549643bde0253144bc436dab29b4c8bfb618d47994
+size 17403214

results-current.jsonl

@@ -64,3 +64,9 @@
 {"alert_rule": "any issue score > max(0.5, question min)", "answered_methods": 996, "average_precision": 0.4758656734035225, "average_precision_answered_methods": 0.4758656734035225, "benchmark_methods": 996, "benchmark_pairs": 498, "benchmark_sha256": "3df4ade65fedd1128d30bc17c9c9869c8ab9bd526e666156d0e16685e78f8a73", "complete": true, "confusion": {"fn": 396, "fp": 24, "tn": 548, "tp": 28}, "context_limit_tokens": 65536, "domain": "bug", "f1": 0.11764705882352941, "failed_methods": 0, "failure_counts": {}, "failure_counts_per_question": {}, "failure_policy": "failed positive counts as FN; failed negative counts as FP; failed pair counts as incorrect ordering", "false_positive_rate": 0.04195804195804196, "inference_cost_basis": "OpenRouter API-reported usage.cost", "inference_cost_usd": 0.19667368, "inference_endpoint": "https://openrouter.ai/api/v1/systemone", "inference_host": "OpenRouter / Liquid", "input_tokens": 4916842, "latency_basis": "Per-method elapsed time, including client rate-limit waits and 429 backoff", "latency_median_seconds": 1.7929241659585387, "latency_p95_seconds": 2.340836458024569, "model_id": "liquid-d1", "model_revision": "liquid/d1-20260930", "pair_order": 0.5062344139650873, "pair_order_correct": 203, "pair_order_eligible": 401, "precision": 0.5384615384615384, "protocol_id": "liquid-d1-openrouter-perch-systemone-v1", "question_pack_sha256": "afe116cc8461552aaca996f8f0b639cdf5e42e9d8291c59995482309b5c330dc", "recall": 0.0660377358490566, "refused_methods": 0, "requested_model": "liquid/d1", "run_id": "liquid-d1-openrouter-20261012", "threshold": 0.5, "track": "private", "vulnerable_or_buggy_before_above_fixed_after": {"correct": 203, "eligible": 401, "ties": 5}}
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 {"alert_rule": "any issue score > max(0.5, question min)", "answered_methods": 878, "average_precision": 0.4793341864639895, "average_precision_answered_methods": 0.4793341864639895, "benchmark_methods": 878, "benchmark_pairs": 439, "benchmark_sha256": "a4b81728e9e520cf1c6f342974bec369d72f5b7d8aabc1c9cf2aceba966f8051", "complete": true, "confusion": {"fn": 260, "fp": 141, "tn": 339, "tp": 138}, "context_limit_tokens": 65536, "domain": "security", "f1": 0.4076809453471196, "failed_methods": 0, "failure_counts": {}, "failure_counts_per_question": {}, "failure_policy": "failed positive counts as FN; failed negative counts as FP; failed pair counts as incorrect ordering", "false_positive_rate": 0.29375, "inference_cost_basis": "OpenRouter API-reported usage.cost", "inference_cost_usd": 2.36226772, "inference_endpoint": "https://openrouter.ai/api/v1/systemone", "inference_host": "OpenRouter / Liquid", "input_tokens": 59056693, "latency_basis": "Per-method elapsed time, including client rate-limit waits and 429 backoff", "latency_median_seconds": 2.1423684580950066, "latency_p95_seconds": 2.9545628749765456, "model_id": "liquid-d1", "model_revision": "liquid/d1-20260930", "pair_order": 0.4547738693467337, "pair_order_correct": 181, "pair_order_eligible": 398, "precision": 0.4946236559139785, "protocol_id": "liquid-d1-openrouter-perch-systemone-v1", "question_pack_sha256": "afe116cc8461552aaca996f8f0b639cdf5e42e9d8291c59995482309b5c330dc", "recall": 0.34673366834170855, "refused_methods": 0, "requested_model": "liquid/d1", "run_id": "liquid-d1-openrouter-20261012", "threshold": 0.5, "track": "private", "vulnerable_or_buggy_before_above_fixed_after": {"correct": 181, "eligible": 398, "ties": 4}}
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results.jsonl

@@ -116,3 +116,9 @@
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+{"alert_rule": "rule violation probability > 0.7", "answered_methods": 428, "average_precision": 0.954599215445173, "average_precision_answered_methods": 0.954599215445173, "benchmark_methods": 428, "benchmark_pairs": 214, "benchmark_sha256": "534f6c2abddc507c40f4597607e97e07168c544011d617b83aa39e08abb35d75", "complete": true, "confusion": {"fn": 61, "fp": 6, "tn": 208, "tp": 153}, "context_limit_basis": "SubQ server context-length validation: maximum model length 8192", "context_limit_tokens": 8192, "domain": "lint", "f1": 0.8203753351206434, "failed_methods": 0, "failure_counts": {}, "failure_counts_per_question": {}, "failure_policy": "failed positive counts as FN; failed negative counts as FP; failed pair counts as incorrect ordering", "false_positive_rate": 0.028037383177570093, "inference_cost_basis": "API-reported input tokens at SubQ published $0.05 per million input tokens", "inference_cost_usd": 0.045379050000000004, "inference_endpoint": "https://api.subq.ai/v1/systemone", "inference_host": "SubQ", "input_tokens": 907581, "latency_basis": "Per-method elapsed time, including client rate-limit waits and 429 backoff", "latency_median_seconds": 1.8166153749916703, "latency_p95_seconds": 2.551551292068325, "model_id": "subq-snap", "model_revision": "subq-snap-preview", "pair_order": 0.9672897196261683, "pair_order_correct": 207, "pair_order_eligible": 214, "perch_revision": "4af67af46d76ac99398d61c7ad0a45441760d4dc", "perch_sizing_bundle_sha256": "17592731f69d88e2d1dda3f93b52eab2e3d2fc440d6b3bf0af6583e718cbd51a", "precision": 0.9622641509433962, "protocol_id": "subq-snap-perch-sized-systemone-v1", "question_pack_sha256": null, "recall": 0.7149532710280374, "refused_methods": 0, "requested_model": "subq-snap-preview", "run_id": "subq-snap-20261012", "sizing": {"adapter_sha256": "cef0475d20258b14a8820ce734cfcd422f397131b18ecf3fba5b40c5ec9d7b87", "request_budget": 6500, "state_budget": 4500}, "threshold": 0.7, "track": "public", "vulnerable_or_buggy_before_above_fixed_after": {"correct": 207, "eligible": 214, "ties": 0}}
+{"alert_rule": "rule violation probability > 0.7", "answered_methods": 142, "average_precision": 0.9686294456382798, "average_precision_answered_methods": 0.9686294456382798, "benchmark_methods": 142, "benchmark_pairs": 71, "benchmark_sha256": "a6e9f2e4ee8ae77bdaf23af46920d97d1ba47375dc9ce03b950cdbe95c2777f9", "complete": true, "confusion": {"fn": 20, "fp": 3, "tn": 68, "tp": 51}, "context_limit_basis": "SubQ server context-length validation: maximum model length 8192", "context_limit_tokens": 8192, "domain": "lint", "f1": 0.816, "failed_methods": 0, "failure_counts": {}, "failure_counts_per_question": {}, "failure_policy": "failed positive counts as FN; failed negative counts as FP; failed pair counts as incorrect ordering", "false_positive_rate": 0.04225352112676056, "inference_cost_basis": "API-reported input tokens at SubQ published $0.05 per million input tokens", "inference_cost_usd": 0.015941200000000003, "inference_endpoint": "https://api.subq.ai/v1/systemone", "inference_host": "SubQ", "input_tokens": 318824, "latency_basis": "Per-method elapsed time, including client rate-limit waits and 429 backoff", "latency_median_seconds": 2.152747832937166, "latency_p95_seconds": 2.732683040900156, "model_id": "subq-snap", "model_revision": "subq-snap-preview", "pair_order": 0.9577464788732394, "pair_order_correct": 68, "pair_order_eligible": 71, "perch_revision": "4af67af46d76ac99398d61c7ad0a45441760d4dc", "perch_sizing_bundle_sha256": "17592731f69d88e2d1dda3f93b52eab2e3d2fc440d6b3bf0af6583e718cbd51a", "precision": 0.9444444444444444, "protocol_id": "subq-snap-perch-sized-systemone-v1", "question_pack_sha256": null, "recall": 0.7183098591549296, "refused_methods": 0, "requested_model": "subq-snap-preview", "run_id": "subq-snap-20261012", "sizing": {"adapter_sha256": "cef0475d20258b14a8820ce734cfcd422f397131b18ecf3fba5b40c5ec9d7b87", "request_budget": 6500, "state_budget": 4500}, "threshold": 0.7, "track": "private", "vulnerable_or_buggy_before_above_fixed_after": {"correct": 68, "eligible": 71, "ties": 0}}
+{"alert_rule": "any issue score > max(0.5, question min)", "answered_methods": 4426, "average_precision": 0.48487100893482865, "average_precision_answered_methods": 0.48487100893482865, "benchmark_methods": 4440, "benchmark_pairs": 2220, "benchmark_sha256": "32bc68bc85d5535ad28e103adbfa0c09c73e09d0f53a53f7ca35ec219a96e6e2", "complete": true, "confusion": {"fn": 1577, "fp": 256, "tn": 2323, "tp": 284}, "context_limit_basis": "SubQ server context-length validation: maximum model length 8192", "context_limit_tokens": 8192, "domain": "bug", "f1": 0.23656809662640566, "failed_methods": 14, "failure_counts": {"Context sizing failed": 14}, "failure_counts_per_question": {}, "failure_policy": "failed positive counts as FN; failed negative counts as FP; failed pair counts as incorrect ordering", "false_positive_rate": 0.09926328034121752, "inference_cost_basis": "API-reported input tokens at SubQ published $0.05 per million input tokens", "inference_cost_usd": 0.56760645, "inference_endpoint": "https://api.subq.ai/v1/systemone", "inference_host": "SubQ", "input_tokens": 11352129, "latency_basis": "Per-method elapsed time, including client rate-limit waits and 429 backoff", "latency_median_seconds": 1.812008082983084, "latency_p95_seconds": 2.3090446670539677, "model_id": "subq-snap", "model_revision": "subq-snap-preview", "pair_order": 0.5095398428731762, "pair_order_correct": 908, "pair_order_eligible": 1782, "perch_revision": "4af67af46d76ac99398d61c7ad0a45441760d4dc", "perch_sizing_bundle_sha256": "17592731f69d88e2d1dda3f93b52eab2e3d2fc440d6b3bf0af6583e718cbd51a", "precision": 0.5259259259259259, "protocol_id": "subq-snap-perch-sized-systemone-v1", "question_pack_sha256": "afe116cc8461552aaca996f8f0b639cdf5e42e9d8291c59995482309b5c330dc", "recall": 0.15260612573885007, "refused_methods": 0, "requested_model": "subq-snap-preview", "run_id": "subq-snap-20261012", "sizing": {"adapter_sha256": "cef0475d20258b14a8820ce734cfcd422f397131b18ecf3fba5b40c5ec9d7b87", "request_budget": 6500, "state_budget": 4500}, "threshold": 0.5, "track": "public", "vulnerable_or_buggy_before_above_fixed_after": {"correct": 908, "eligible": 1782, "ties": 2}}
+{"alert_rule": "any issue score > max(0.5, question min)", "answered_methods": 992, "average_precision": 0.5253123825884426, "average_precision_answered_methods": 0.5253123825884426, "benchmark_methods": 996, "benchmark_pairs": 498, "benchmark_sha256": "3df4ade65fedd1128d30bc17c9c9869c8ab9bd526e666156d0e16685e78f8a73", "complete": true, "confusion": {"fn": 342, "fp": 64, "tn": 508, "tp": 82}, "context_limit_basis": "SubQ server context-length validation: maximum model length 8192", "context_limit_tokens": 8192, "domain": "bug", "f1": 0.28771929824561404, "failed_methods": 4, "failure_counts": {"Context sizing failed": 4}, "failure_counts_per_question": {}, "failure_policy": "failed positive counts as FN; failed negative counts as FP; failed pair counts as incorrect ordering", "false_positive_rate": 0.11188811188811189, "inference_cost_basis": "API-reported input tokens at SubQ published $0.05 per million input tokens", "inference_cost_usd": 0.13267365, "inference_endpoint": "https://api.subq.ai/v1/systemone", "inference_host": "SubQ", "input_tokens": 2653473, "latency_basis": "Per-method elapsed time, including client rate-limit waits and 429 backoff", "latency_median_seconds": 1.776716374908574, "latency_p95_seconds": 2.5099464589729905, "model_id": "subq-snap", "model_revision": "subq-snap-preview", "pair_order": 0.49875311720698257, "pair_order_correct": 200, "pair_order_eligible": 401, "perch_revision": "4af67af46d76ac99398d61c7ad0a45441760d4dc", "perch_sizing_bundle_sha256": "17592731f69d88e2d1dda3f93b52eab2e3d2fc440d6b3bf0af6583e718cbd51a", "precision": 0.5616438356164384, "protocol_id": "subq-snap-perch-sized-systemone-v1", "question_pack_sha256": "afe116cc8461552aaca996f8f0b639cdf5e42e9d8291c59995482309b5c330dc", "recall": 0.19339622641509435, "refused_methods": 0, "requested_model": "subq-snap-preview", "run_id": "subq-snap-20261012", "sizing": {"adapter_sha256": "cef0475d20258b14a8820ce734cfcd422f397131b18ecf3fba5b40c5ec9d7b87", "request_budget": 6500, "state_budget": 4500}, "threshold": 0.5, "track": "private", "vulnerable_or_buggy_before_above_fixed_after": {"correct": 200, "eligible": 401, "ties": 1}}
+{"alert_rule": "any issue score > max(0.5, question min)", "answered_methods": 2621, "average_precision": 0.5145437196389815, "average_precision_answered_methods": 0.5145437196389815, "benchmark_methods": 2622, "benchmark_pairs": 1311, "benchmark_sha256": "2407059caa3486371749884138b997d89043eec646e548ee8f44ba3ab08514b8", "complete": true, "confusion": {"fn": 860, "fp": 235, "tn": 1245, "tp": 282}, "context_limit_basis": "SubQ server context-length validation: maximum model length 8192", "context_limit_tokens": 8192, "domain": "security", "f1": 0.3399638336347197, "failed_methods": 1, "failure_counts": {"Context sizing failed": 1}, "failure_counts_per_question": {}, "failure_policy": "failed positive counts as FN; failed negative counts as FP; failed pair counts as incorrect ordering", "false_positive_rate": 0.15878378378378377, "inference_cost_basis": "API-reported input tokens at SubQ published $0.05 per million input tokens", "inference_cost_usd": 0.81717625, "inference_endpoint": "https://api.subq.ai/v1/systemone", "inference_host": "SubQ", "input_tokens": 16343525, "latency_basis": "Per-method elapsed time, including client rate-limit waits and 429 backoff", "latency_median_seconds": 2.3714272920042276, "latency_p95_seconds": 4.737156999995932, "model_id": "subq-snap", "model_revision": "subq-snap-preview", "pair_order": 0.5355871886120996, "pair_order_correct": 602, "pair_order_eligible": 1124, "perch_revision": "4af67af46d76ac99398d61c7ad0a45441760d4dc", "perch_sizing_bundle_sha256": "17592731f69d88e2d1dda3f93b52eab2e3d2fc440d6b3bf0af6583e718cbd51a", "precision": 0.5454545454545454, "protocol_id": "subq-snap-perch-sized-systemone-v1", "question_pack_sha256": "afe116cc8461552aaca996f8f0b639cdf5e42e9d8291c59995482309b5c330dc", "recall": 0.2469352014010508, "refused_methods": 0, "requested_model": "subq-snap-preview", "run_id": "subq-snap-20261012", "sizing": {"adapter_sha256": "cef0475d20258b14a8820ce734cfcd422f397131b18ecf3fba5b40c5ec9d7b87", "request_budget": 6500, "state_budget": 4500}, "threshold": 0.5, "track": "public", "vulnerable_or_buggy_before_above_fixed_after": {"correct": 602, "eligible": 1124, "ties": 1}}
+{"alert_rule": "any issue score > max(0.5, question min)", "answered_methods": 878, "average_precision": 0.5004935106105339, "average_precision_answered_methods": 0.5004935106105339, "benchmark_methods": 878, "benchmark_pairs": 439, "benchmark_sha256": "a4b81728e9e520cf1c6f342974bec369d72f5b7d8aabc1c9cf2aceba966f8051", "complete": true, "confusion": {"fn": 357, "fp": 40, "tn": 440, "tp": 41}, "context_limit_basis": "SubQ server context-length validation: maximum model length 8192", "context_limit_tokens": 8192, "domain": "security", "f1": 0.17118997912317327, "failed_methods": 0, "failure_counts": {}, "failure_counts_per_question": {}, "failure_policy": "failed positive counts as FN; failed negative counts as FP; failed pair counts as incorrect ordering", "false_positive_rate": 0.08333333333333333, "inference_cost_basis": "API-reported input tokens at SubQ published $0.05 per million input tokens", "inference_cost_usd": 0.2778463, "inference_endpoint": "https://api.subq.ai/v1/systemone", "inference_host": "SubQ", "input_tokens": 5556926, "latency_basis": "Per-method elapsed time, including client rate-limit waits and 429 backoff", "latency_median_seconds": 2.590956083033234, "latency_p95_seconds": 4.45469124999363, "model_id": "subq-snap", "model_revision": "subq-snap-preview", "pair_order": 0.4648241206030151, "pair_order_correct": 185, "pair_order_eligible": 398, "perch_revision": "4af67af46d76ac99398d61c7ad0a45441760d4dc", "perch_sizing_bundle_sha256": "17592731f69d88e2d1dda3f93b52eab2e3d2fc440d6b3bf0af6583e718cbd51a", "precision": 0.5061728395061729, "protocol_id": "subq-snap-perch-sized-systemone-v1", "question_pack_sha256": "afe116cc8461552aaca996f8f0b639cdf5e42e9d8291c59995482309b5c330dc", "recall": 0.10301507537688442, "refused_methods": 0, "requested_model": "subq-snap-preview", "run_id": "subq-snap-20261012", "sizing": {"adapter_sha256": "cef0475d20258b14a8820ce734cfcd422f397131b18ecf3fba5b40c5ec9d7b87", "request_budget": 6500, "state_budget": 4500}, "threshold": 0.5, "track": "private", "vulnerable_or_buggy_before_above_fixed_after": {"correct": 185, "eligible": 398, "ties": 0}}

subq.py

@@ -0,0 +1,203 @@
+#!/usr/bin/env python3
+"""Benchmark SubQ Snap with native decisions and Perch context sizing.
+
+SubQ's server reports an 8,192-token maximum. Perch batches questions and reads
+large methods in overlapping chunks, preserving every target-source byte.
+Cost uses API-reported input tokens at the published $0.05 per million rate.
+"""
+from collections import Counter
+import hashlib
+import json
+import os
+from pathlib import Path
+import subprocess
+import sys
+import threading
+import time
+
+import httpx
+import openrouter_decisions as runner
+from gliner_decide import PerchSizer, SizeError
+
+MODEL = 'subq-snap-preview'
+STATE_BUDGET = 4500
+REQUEST_BUDGET = 6500
+_native_prediction = runner.prediction
+_native_report = runner.report
+_sizer = None
+_sizer_lock = threading.Lock()
+_source_revision = None
+
+
+def prediction(pair, role, domain, pack, response, elapsed, model):
+    usage = response.get('usage') or {}
+    tokens = usage.get('input_tokens')
+    if isinstance(tokens, bool) or not isinstance(tokens, int) or tokens < 0:
+        raise runner.AnswerError('Missing input-token usage', response, elapsed)
+    response = dict(response, usage=dict(usage, cost=tokens * .05 / 1_000_000))
+    return _native_prediction(pair, role, domain, pack, response, elapsed, model)
+
+
+def sizer():
+    global _sizer, _source_revision
+    with _sizer_lock:
+        if _sizer is None:
+            source = os.environ.get('PERCH_SOURCE')
+            if not source:
+                raise runner.AccountError('Set PERCH_SOURCE to a Perch checkout with npm dependencies installed')
+            source = Path(source)
+            _source_revision = subprocess.check_output(['git', '-C', str(source), 'rev-parse', 'HEAD'], text=True).strip()
+            _sizer = PerchSizer(source, Path(os.environ.get('SUBQ_SIZING_CACHE', '.subq-sizing')))
+        return _sizer
+
+
+def request(key, state, checks, timeout, limiter):
+    if not hasattr(runner._local, 'client'):
+        runner._local.client = httpx.Client(headers={'Authorization': 'Bearer ' + key})
+    payload = {'model': MODEL, 'state': state,
+               'questions': {c['name']: c['question'] for c in checks}}
+    for attempt in range(3):
+        limiter.wait()
+        response = runner._local.client.post(runner.API, json=payload, timeout=timeout)
+        if response.status_code >= 400:
+            try:
+                detail = response.json().get('detail') or response.json().get('error') or {}
+                message = detail.get('message', '') if isinstance(detail, dict) else str(detail)
+                code = detail.get('error_type', response.status_code) if isinstance(detail, dict) else response.status_code
+            except ValueError:
+                code, message = response.status_code, ''
+            error = f'SubQ HTTP {response.status_code}: {code}: {message}'
+            if response.status_code in (401, 402, 403, 404):
+                raise runner.AccountError(error)
+            if response.status_code in (400, 413, 422) and any(text in message.lower() for text in ('maximum model length', 'context length', 'context window', 'too many tokens')):
+                raise SizeError(error)
+            if response.status_code == 429 and attempt < 2:
+                try:
+                    delay = float(response.headers.get('retry-after', 2 ** (attempt + 1)))
+                except ValueError:
+                    delay = 2 ** (attempt + 1)
+                time.sleep(min(30, max(1, delay)))
+                continue
+            raise RuntimeError(error)
+        body = response.json()
+        if body.get('model') != MODEL:
+            raise runner.AccountError('SubQ returned a different model: ' + str(body.get('model')))
+        answers = body.get('answers')
+        if not isinstance(answers, dict) or set(answers) != {c['name'] for c in checks}:
+            raise runner.AnswerError('Missing or unexpected answers', body, 0)
+        for name, answer in answers.items():
+            if not isinstance(answer, dict) or answer.get('type') != 'noul':
+                raise runner.AnswerError('Unexpected answer type for ' + name, body, 0)
+            runner.core.probability(answer.get('noul'))
+        tokens = body.get('usage', {}).get('input_tokens')
+        if isinstance(tokens, bool) or not isinstance(tokens, int) or tokens < 0:
+            raise runner.AnswerError('Missing input-token usage', body, 0)
+        return body
+    raise AssertionError('unreachable')
+
+
+def predict(key, pair, role, domain, pack, timeout, limiter, model):
+    checks = runner.questions_for(pair, domain, pack)
+    by_name = {c['name']: c for c in checks}
+    tokens, requests, discarded = 0, 0, 0
+    started, budget = time.monotonic(), STATE_BUDGET
+    try:
+        for attempt in range(9):
+            probabilities, scores, readings = {}, {}, []
+            try:
+                plans = sizer().plan(pair[role], checks, budget, REQUEST_BUDGET, len(checks))
+                def ask(plan, names):
+                    nonlocal requests, tokens
+                    requests += 1
+                    try:
+                        body = request(key, plan['state'], [by_name[n] for n in names], timeout, limiter)
+                    except runner.AnswerError as exc:
+                        charged = exc.usage.get('input_tokens', 0)
+                        if isinstance(charged, int) and not isinstance(charged, bool) and charged >= 0:
+                            tokens += charged
+                        raise
+                    except SizeError:
+                        if len(names) == 1:
+                            raise
+                        middle = (len(names) + 1) // 2
+                        ask(plan, names[:middle]); ask(plan, names[middle:])
+                        return
+                    tokens += body['usage']['input_tokens']
+                    for name, answer in body['answers'].items():
+                        probability = runner.core.probability(answer['noul'])
+                        score = runner.core.raised_score(by_name[name], probability)
+                        if name not in scores or score > scores[name]:
+                            scores[name], probabilities[name] = score, probability
+                    reading = {k: v for k, v in plan.items() if k not in ('state', 'question_names')}
+                    reading.update(question_names=names, answers=body['answers'], usage=body['usage'],
+                                   state_sha256=hashlib.sha256(json.dumps(plan['state'], sort_keys=True).encode()).hexdigest())
+                    readings.append(reading)
+                for plan in plans:
+                    ask(plan, plan['question_names'])
+                break
+            except SizeError:
+                discarded += len(readings)
+                budget //= 2
+                if attempt == 8 or budget < 128:
+                    raise
+        if set(probabilities) != set(by_name):
+            raise RuntimeError('Not every question received a score')
+        body = {'model': MODEL, 'answers': {n: {'type': 'noul', 'noul': p} for n, p in probabilities.items()},
+                'usage': {'input_tokens': tokens, 'output_tokens': 0}}
+        row = prediction(pair, role, domain, pack, body, time.monotonic()-started, model)
+        row.update(readings=readings, api_requests=requests, discarded_readings=discarded,
+                   final_state_budget=budget, perch_revision=_source_revision,
+                   sizing={'state_budget': STATE_BUDGET, 'request_budget': REQUEST_BUDGET,
+                           'adapter_sha256': hashlib.sha256((runner.ROOT/'perch-sizing.mjs').read_bytes()).hexdigest()})
+        return row
+    except Exception as exc:
+        exc.usage = {'input_tokens': tokens, 'output_tokens': 0, 'cost': tokens * .05 / 1_000_000}
+        raise
+
+
+def report(group, pack, model):
+    result = _native_report(group, pack, model)
+    # Aggregate failures without exposing private method names from sizing errors.
+    failures = Counter()
+    for row in group['failures'].values():
+        message = row.get('error', '')
+        if 'Check incomplete:' in message:
+            category = 'Context sizing failed'
+        elif message.startswith('SubQ HTTP '):
+            category = ':'.join(message.split(':')[:2]).strip()
+        elif 'timeout' in message.lower() or 'timed out' in message.lower():
+            category = 'Request timed out'
+        else:
+            category = 'Invalid or incomplete decision'
+        failures[category] += 1
+    result['failure_counts'] = dict(failures)
+    result.update(context_limit_basis='SubQ server context-length validation: maximum model length 8192',
+                  sizing={'state_budget': STATE_BUDGET, 'request_budget': REQUEST_BUDGET},
+                  perch_revision=next((row.get('perch_revision') for row in group['predictions'].values()
+                                       if row.get('perch_revision')), _source_revision))
+    return result
+
+
+def configure():
+    runner.API = 'https://api.subq.ai/v1/systemone'
+    runner.API_LABEL = 'SubQ'
+    runner.KEY_ENV = 'SUBQ_API_KEY'
+    runner.COST_BASIS = 'API-reported input tokens at SubQ published $0.05 per million input tokens'
+    runner.HOST_PREFIX = ''
+    runner.CATALOG = {MODEL: ('subq-snap', MODEL, 'subq-snap-perch-sized-systemone-v1',
+                             'subq-snap-20261012', 'SubQ', 8192, .05)}
+    runner.MODELS = (MODEL,)
+    runner.prediction = prediction
+    runner.predict = predict
+    runner.report = report
+
+
+if __name__ == '__main__':
+    configure()
+    if '--score-saved' not in sys.argv:
+        sizer()
+    try:
+        runner.main()
+    finally:
+        if _sizer is not None:
+            _sizer.close()

test_subq.py

@@ -0,0 +1,57 @@
+import unittest
+from unittest.mock import Mock, patch
+import httpx
+import subq
+
+
+class SubQTests(unittest.TestCase):
+    def setUp(self):
+        names = ('API API_LABEL KEY_ENV COST_BASIS HOST_PREFIX CATALOG MODELS MODEL_ID REVISION PROTOCOL RUN_ID HOST CONTEXT_LIMIT PRICE prediction predict report').split()
+        self.restore = patch.multiple(subq.runner, **{n: getattr(subq.runner, n) for n in names})
+        self.restore.start(); self.addCleanup(self.restore.stop)
+        subq.configure(); subq.runner.select_model(subq.MODEL)
+        self.pack = subq.runner.core.load_pack(subq.runner.ROOT/'question-pack.json')
+        self.pair = {'pair_id': 'example', 'language': 'python',
+                     'rule': {'ensure': 'Reject zero before division.'},
+                     'before': {'state': {'method': {'source': 'return a / b'}}}}
+
+    def plan(self, names):
+        return {'state': self.pair['before']['state'], 'question_names': names,
+                'covers': {'start_byte': 0, 'end_byte': 12}}
+
+    def answer(self, probability, tokens=100):
+        return {'model': subq.MODEL, 'answers': {'semantic': {'type': 'noul', 'noul': probability}},
+                'usage': {'input_tokens': tokens, 'output_tokens': 0}}
+
+    def test_chunk_scores_preserve_polarity_and_sum_billed_tokens(self):
+        sizer = Mock(); sizer.plan.return_value = [self.plan(['semantic']), self.plan(['semantic'])]
+        with patch.object(subq, 'sizer', return_value=sizer), patch.object(subq, 'request', side_effect=[self.answer(.9), self.answer(.2, 200)]) as request:
+            row = subq.predict('key', self.pair, 'before', 'lint', self.pack, 30, Mock(), subq.MODEL)
+        self.assertEqual(request.call_count, 2)
+        self.assertAlmostEqual(row['score'], .8)
+        self.assertTrue(row['alert'])
+        self.assertEqual(row['usage']['input_tokens'], 300)
+        self.assertAlmostEqual(row['usage']['cost'], .000015)
+        self.assertEqual(len(row['readings']), 2)
+
+    def test_timeout_stays_failed_and_retains_prior_reading_cost(self):
+        sizer = Mock(); sizer.plan.return_value = [self.plan(['semantic']), self.plan(['semantic'])]
+        with patch.object(subq, 'sizer', return_value=sizer), patch.object(subq, 'request', side_effect=[self.answer(.9, 300), httpx.ReadTimeout('timeout')]) as request:
+            with self.assertRaises(httpx.ReadTimeout) as raised:
+                subq.predict('key', self.pair, 'before', 'lint', self.pack, 30, Mock(), subq.MODEL)
+        self.assertEqual(request.call_count, 2)
+        self.assertEqual(raised.exception.usage['input_tokens'], 300)
+        self.assertAlmostEqual(raised.exception.usage['cost'], .000015)
+
+    def test_size_rejection_reduces_state_budget(self):
+        sizer = Mock(); sizer.plan.return_value = [self.plan(['semantic'])]
+        with patch.object(subq, 'sizer', return_value=sizer), patch.object(subq, 'request', side_effect=[subq.SizeError('maximum model length'), self.answer(.1)]) as request:
+            row = subq.predict('key', self.pair, 'before', 'lint', self.pack, 30, Mock(), subq.MODEL)
+        self.assertEqual(request.call_count, 2)
+        self.assertEqual(row['final_state_budget'], subq.STATE_BUDGET//2)
+        self.assertEqual(sizer.plan.call_args.args[2], subq.STATE_BUDGET//2)
+        self.assertTrue(row['alert'])
+
+
+if __name__ == '__main__':
+    unittest.main()

tradeoff.py

@@ -35,6 +35,7 @@ NAMES = {
     "microsoft-decision-1": ("Microsoft-Decision-1", "https://openrouter.ai/microsoft/microsoft-decision-1"),
     "mercury-decide": ("Mercury Decide", "https://openrouter.ai/inception/mercury-decide"),
     "openai-decisions": ("GPT-6 Luna Decisions", "https://developers.openai.com/api/docs/guides/decisions"),
+    "subq-snap": ("SubQ Snap Preview", "https://docs.subq.ai/using-the-api/models/"),
 }
 # Shapes distinguish models independently of color, including in grayscale.
 COLORS = {
@@ -42,14 +43,14 @@ COLORS = {
     "diffusiongemma": "#ff9234", "span01": "#00c49a", "clm": "#a0a8b5",
     "semif": "#ef5b5b", "gliner25-decide": "#ffe05b",
     "microsoft-decision-1": "#f5f5f5", "mercury-decide": "#6e8fff",
-    "openai-decisions": "#b8e186",
+    "openai-decisions": "#b8e186", "subq-snap": "#ffd5bf",
 }
 MARKERS = {
     "jev": "circle", "typellm": "diamond", "liquid-d1": "triangle",
     "diffusiongemma": "square", "span01": "triangle-down", "clm": "hexagon",
     "semif": "cross", "gliner25-decide": "pentagon",
     "microsoft-decision-1": "plus", "mercury-decide": "star",
-    "openai-decisions": "ring",
+    "openai-decisions": "ring", "subq-snap": "hourglass",
 }
 ORDER = tuple(NAMES)
 OLD_PACK_SHA = "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d"
@@ -271,6 +272,9 @@ def marker(model: str, x: float, y: float, size: float = 7,
         element = f'<circle r="{size-1}" fill="#101318" stroke="{color}" stroke-width="3"/>'
     elif shape == "square":
         element = f'<rect x="{-size}" y="{-size}" width="{size*2}" height="{size*2}" {style}/>'
+    elif shape == "hourglass":
+        points = f'{-size},{-size} {size},{-size} 0,0 {size},{size} {-size},{size} 0,0'
+        element = f'<polygon points="{points}" {style}/>'
     elif shape in ("plus", "cross"):
         thickness = size * .35
         path = f'M {-thickness} {-size} H {thickness} V {-thickness} H {size} V {thickness} H {thickness} V {size} H {-thickness} V {thickness} H {-size} V {-thickness} H {-thickness} Z'