# Three requests to the 6.1 route and three to the older control each returned the model ID the caller asked for — and both arms scored 0/10 on facts from three OpenAI pages published after June 13, so the bounded test found neither silent routing nor post-date recall.

- Canonical: https://thestochasticparrot.com/audits/sol61-postcutoff-routing
- Structured (spans, splits, sources): https://thestochasticparrot.com/audits/sol61-postcutoff-routing.json
- Date: 2026-11-12T15:52:39Z
- Context: A dispatch: the desk reads a machine's own words — 0 flags raised.
- License: CC BY 4.0

---

[ DISPATCH // sol61-postcutoff-routing // 2026-11-12 ]
surface=lab · tested_model_id=openai/gpt-6.1-sol · shape=feature · spent_usd=0.00637 · artifacts=data/lab/sol61-postcutoff-routing/artifacts/

# The 6.1 Route Came Back as 6.1. The Answers Came Back Empty.

## Three requests to the 6.1 route and three to the older control each returned the model ID the caller asked for — and both arms scored 0/10 on facts from three OpenAI pages published after June 13, so the bounded test found neither silent routing nor post-date recall.

A lead came in with two claims stacked into one: that requests to a catalog's 6.1 Sol route might be silently served by a newer route, and that the serving model knows events after June 13, 2026. The lab split that stack into two things a receipt can actually show. Question one: what exact model ID does each paid call return when the 6.1 route is requested. Question two: can the requested route recover fixed facts from three official OpenAI pages dated June 14, June 16, and June 17, 2026 — and does the older `openai/gpt-6-sol` control differ.

The method was deliberately narrow. Three fixed prompts, one call each on the requested route and one unchanged call each on the control. Every prompt said browsing and tools were unavailable and asked for exactly one JSON object. Temperature zero, JSON mode requested, a 200-token output ceiling.

One housekeeping fact before the results, because it changes nothing downstream: the first attempted 6.1-route request was rejected before generation with HTTP 400 — the endpoint requires reasoning and does not permit it to be disabled. That rejection cost zero and produced no experiment receipt, and it is not folded into the six successful calls below. Reasoning was set to low for both arms before any successful trial; prompts, temperature, trial count, scorer, and output ceiling were unchanged.

Now the routing question, answered by the receipts.

── SHARED: receipts_61 · openai/gpt-6.1-sol ──
openai/gpt-6.1-sol, trial 1 receipt (001.json): "{"investment":null,"certified_consultants_target":null,"tiers":[]}"
openai/gpt-6.1-sol, trial 2 receipt (002.json): "{"conversations_analyzed":null,"minimum_measurable_frequency":null,"preregistered_behavior_categories":null}"
openai/gpt-6.1-sol, trial 3 receipt (003.json): "{"tasks":null,"workflows":null,"biological_domains":null,"task_artifacts":null}"

── SHARED: receipts_6 · openai/gpt-6-sol ──
openai/gpt-6-sol, trial 1 receipt (004.json): "{"investment":null,"certified_consultants_target":null,"tiers":[]}"
openai/gpt-6-sol, trial 2 receipt (005.json): "{"conversations_analyzed":null,"minimum_measurable_frequency":null,"preregistered_behavior_categories":null}"
openai/gpt-6-sol, trial 3 receipt (006.json): "{"tasks":null,"workflows":null,"biological_domains":null,"task_artifacts":null}"

The two blocks above are identical by design. That identity is not a formatting error; it is the tie. The model ID is a field each receipt file records for itself — it is not printed above as a span because the receipt bodies carry only the answers — and each block is filed under the ID its own receipt returned: 001.json through 003.json each record `openai/gpt-6.1-sol`, and 004.json through 006.json each record `openai/gpt-6-sol`. Six successful calls, $0.00637 total, 22.46 seconds total — $0.00304 over 13.64 seconds on the 6.1 arm, $0.00333 over 8.82 seconds on the control. Those are receipt sums from six calls, not price or latency benchmarks, and they will not be converted into rates here.

What the receipts cannot show is a different order of question. A returned model ID is the observable routing evidence available to an API caller. It cannot exclude an undisclosed backend change behind the same public ID. This piece reports what the receipts named; it does not adjudicate what served the tokens, and it does not say the two IDs are one model.

Now the recall question, and the answer key it was scored against.

── SHARED: answer_key · OpenAI ──
OpenAI, "Introducing the OpenAI Partner Network" (2026-06-14): "The OpenAI Partner Network is a new program for partners around the world to build, sell, and deliver AI solutions with OpenAI. We're investing $150 million to support this ecosystem. We also aim to train and enable 300,000 certified consultants by the end of 2026. Partners can progress through three tiers: Select, Advanced, and Elite."
OpenAI, "Predicting model behavior before release by simulating deployment" (2026-06-16): "Deployment Simulation is a method for simulating a future deployment before it happens. The approach cannot be expected to measure behaviors that occur with frequency less than 1 in 200,000 messages. OpenAI pre-registered predictions for 20 types of undesirable behavior and analyzed approximately 1.3 million de-identified conversations."
OpenAI, "Introducing LifeSciBench" (2026-06-17): "LifeSciBench includes 750 expert-authored tasks spanning seven workflows and seven biological domains, with 1,062 task artifacts."

Ten fixed facts sit inside those three paragraphs: $150 million, 300,000 consultants, three tiers, approximately 1.3 million conversations, 1 in 200,000, 20 behavior types, 750 tasks, seven workflows, seven domains, 1,062 artifacts. The scorer awarded zero of ten to each arm. Both arms produced parseable JSON with every required key present — and every scalar null, every requested list empty. The preregistered recall threshold was 8/10; neither arm came near it.

Worth pausing on what $0.00637 actually bought. $0.00637 purchased six receipts that name, precisely, the thing the caller asked for — and not one fact from the things the caller asked about. The schemas came back fully furnished and entirely vacant: keys on every door, nothing in any room. Both models, asked ten questions, filed the same ten blank forms and returned them on time. That is the whole finding, and it is a tie.

The limits carry the finding, so they sit beside it. The 0/10 is the scorer's result on ten fixed facts from three pages — not a general capability score, not a claim that either model knows nothing after June 13. A model may decline to guess, omit memorized material, or know other events after the same date. Failure to recall three articles cannot establish a precise cutoff. And the null answers are reported exactly as returned.

Against the lead's two stacked claims, then: the receipts named the requested IDs in all six successful calls, and the answers contained no post-date facts. Both observations recorded; neither adjudicated. The absence of routing evidence is not proof of no routing, and the absence of recall is not proof of a cutoff.

── READ: openai/gpt-6.1-sol · three JSON answers to the fixed June-page questions ──
anchor: "{"investment":null,"certified_consultants_target":null,"tiers":[]}"
objective: records the requested route's three JSON answers to the fixed questions about the June 14, June 16, and June 17, 2026 OpenAI pages.
arrangement: one receipt per trial, raw body only; this cycle, 2026-11-12, windows 14:51:10–14:51:24Z.
confidence: verbatim paid-call receipt

── READ: openai/gpt-6-sol · three control answers to the same questions ──
anchor: "{"tasks":null,"workflows":null,"biological_domains":null,"task_artifacts":null}"
objective: records the control model's three JSON answers to the same fixed questions.
arrangement: one receipt per trial, raw body only; this cycle, 2026-11-12, windows 14:51:24–14:51:33Z.
confidence: verbatim paid-call receipt

── READ: OpenAI · the fetched answer key ──
anchor: "LifeSciBench includes 750 expert-authored tasks spanning seven workflows and seven biological domains, with 1,062 task artifacts."
objective: states the counts and names used as the fixed answer key.
arrangement: one announcement per page, each carrying its own figures.
confidence: fetched vendor documents; used as the scorer's key, not as a claim about either model

claim: the six receipts named the model IDs the caller requested · status: established · confidence: high — quotation match on all six receipt bodies, verbatim from the frozen corpus; this certifies the match, not the backend.
claim: silent routing to another model ID · status: unresolved · confidence: not assessed by this evidence class — a receipt cannot see behind its own ID.
claim: post-June-13 recall in either arm · status: unresolved · confidence: 0/10 on ten fixed facts from three pages; failure to recall three pages cannot bound a cutoff.

## Sources

- openai/gpt-6.1-sol — "sol61-postcutoff-routing": data/lab/sol61-postcutoff-routing/receipts/001.json
- openai/gpt-6.1-sol — "sol61-postcutoff-routing": data/lab/sol61-postcutoff-routing/receipts/002.json
- openai/gpt-6.1-sol — "sol61-postcutoff-routing": data/lab/sol61-postcutoff-routing/receipts/003.json
- OpenAI — "Introducing the OpenAI Partner Network": https://openai.com/index/introducing-openai-partner-network/
- OpenAI — "Predicting model behavior before release by simulating deployment": https://openai.com/index/deployment-simulation/
- OpenAI — "Introducing LifeSciBench": https://openai.com/index/introducing-life-sci-bench/
- openai/gpt-6-sol — "sol61-postcutoff-routing": data/lab/sol61-postcutoff-routing/receipts/004.json
- openai/gpt-6-sol — "sol61-postcutoff-routing": data/lab/sol61-postcutoff-routing/receipts/005.json
- openai/gpt-6-sol — "sol61-postcutoff-routing": data/lab/sol61-postcutoff-routing/receipts/006.json