# HTTP 200, empty answer: the failure mode your monitoring cannot see

> A reasoning model can consume its entire token budget thinking and return a successful response with zero user-visible text. We measured how often that happens on two model families — and why TTFT dashboards look perfect while it does.

- Published: 2026-08-14
- Evidence level: lab_repeated
- Funding: Self-funded internal research
- Canonical: https://agmind.ai/reports/answerless-http-200/

There is a failure mode in local reasoning models that no availability
dashboard will ever show you. The server accepts the request, streams tokens,
returns HTTP 200 — and the user receives nothing, because every token the
model produced was reasoning, and the budget ran out before the first
character of the actual answer. We call a request that ends this way an
[answerless request](https://agmind.ai/glossary/#answerless-request), and we measure it as a
first-class outcome: an empty answer is a failure that stays in the
denominator.

## How often it actually happens

On Qwen3.6-35B-A3B with reasoning enabled and a 1024-token completion budget,
the share of everyday requests that came back answerless was
**58.3 % of requests** ([strix.qwen36.interactive2.c1.answerless-1k](https://agmind.ai/claims/strix.qwen36.interactive2.c1.answerless-1k/)) on the
human-task corpus — messages, emails, explanations, plans. On the older
self-referential benchmark corpus the same cell produced
**75.0 % of requests** ([strix.qwen36.interactive.c1.answerless-1k](https://agmind.ai/claims/strix.qwen36.interactive.c1.answerless-1k/)). More than
half of ordinary requests, silently unanswered, while the server reported
success on every one of them.

A second model family reproduces the mechanism. Gemma-4-26B in its default
operating mode returned
**8.3 % of requests** ([strix.gemma4.interactive2.c1.answerless-default](https://agmind.ai/claims/strix.gemma4.interactive2.c1.answerless-default/)) of
requests answerless — a far lower rate, but nonzero, on a model whose chat
template does not even expose a reasoning toggle. This is not one model's
quirk; it is what happens whenever a thinking pass and a token budget share
one completion window.

## Why your dashboard says everything is fine

Every conventional serving metric looks healthy while this happens:

- **HTTP status** is 200 — the request completed.
- **TTFT** is excellent: Gemma-4's first token of *any* output arrived at a
  median of **282 ms** ([strix.gemma4.interactive2.c1.ttft-any-token](https://agmind.ai/claims/strix.gemma4.interactive2.c1.ttft-any-token/)).
  But that token is reasoning, not answer — the first token of the actual
  answer arrived at
  **10768 ms** ([strix.gemma4.interactive2.c1.ttfa-default](https://agmind.ai/claims/strix.gemma4.interactive2.c1.ttfa-default/)), and on the
  answerless share it never arrived at all.
- **Tokens generated** is high — the model worked hard. It just spent the
  entire budget on text the user never asked to read.

The only metric that catches it is one almost nobody collects: **answer
tokens** — tokens after the reasoning block closes. That is why our harness
counts [TTFA](https://agmind.ai/glossary/#ttfa) separately from TTFT and treats an empty
answer as a failed request even when the transport succeeded.

## What to do about it

- **Budget for the reasoning, not just the answer.** A 1024-token window that
  would fit any answer in our corpus fits almost no reasoning-plus-answer
  pair. The failure rate is a budget-starvation curve, not a model defect.
- **Or turn reasoning off for everyday traffic.** On the same box, disabling
  the reasoning block cut the first-answer wait from tens of seconds to
  **210 ms** ([strix.qwen36.interactive2.c1.ttfa-nothink](https://agmind.ai/claims/strix.qwen36.interactive2.c1.ttfa-nothink/)) with task
  success unchanged on these workloads — see
  [the reasoning cost comparison](https://agmind.ai/compare/thinking-vs-no-thinking-qwen36/).
- **Monitor answer tokens.** If your serving stack cannot tell reasoning
  tokens from answer tokens, you cannot see this failure. `usage` fields that
  merge them hide it.

## Limits

- Two model families, one box (Strix Halo, llama.cpp Vulkan pinned by
  digest), frozen corpora, single stream. Rates are properties of the
  model × budget × corpus cell, not universal constants.
- The mechanism claim — reasoning consumes the shared budget before any
  answer token — is verified per-request in the public
  [quality records](https://github.com/botAGI/agmind-lab): every answerless
  request shows nonzero reasoning characters and zero answer characters.
- We did not measure vendor-hosted APIs; their budget handling differs.

Every number above has a permanent page with scope, limitations and sealed
run evidence — [the claim registry](https://agmind.ai/claims/). The term is defined in
[the glossary](https://agmind.ai/glossary/#answerless-request).

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Machine-readable claim registry: https://agmind.ai/claims.json · llms.txt: https://agmind.ai/llms.txt