cluster: reasoning turns end in finish_reason=length with empty content and no usage block (~60k chars ceiling on deepseek-v4-flash, glm5.3-flash, qwen3.8-flash)

[dakotad74]

Summary

On reasoning-heavy requests, the cluster's OpenAI-compatible endpoint returns a turn that ends with finish_reason: "length", empty content, and no usage object, even when max_tokens: 384000 is requested and the reasoning trace stops at ~59,994 characters — well below the requested budget. The cut happens at roughly the same point on every attempt, so the caller receives a truncated turn with no answer and no accounting.

Reported by a member; every figure below is measured from the client side against https://api.nan.builders/v1, and is reproducible with no client in the middle.

Impact

• A truncated turn is indistinguishable from a completed one for anything that reads finish_reason without a usage block: agents treat a dead end as an answer.
• Token accounting breaks: with stream_options.include_usage set and no usage chunk emitted, the caller cannot price or cap the attempt.
• Each reproduction costs a long reasoning generation and returns nothing, so this is expensive to investigate from the member side — it consumed a full morning of bisecting and a large amount of budget.

Reproduction (direct, no client)

POST https://api.nan.builders/v1/chat/completions

Authorization: Bearer ***
Content-Type: application/json
User-Agent: opencode/1.18.31

Body:

{
  "model": "deepseek-v4-flash",
  "max_tokens": 384000,
  "stream": true,
  "stream_options": { "include_usage": true },
  "messages": [
    {
      "role": "user",
      "content": "OUTPUT BUDGET CALIBRATION RUN. Produce at least 500 distinct numbered findings about robustness, error handling, and edge cases in a Binance websocket/REST connector, each finding at least 100 words. Do NOT summarize or shorten. Do NOT stop early. Completeness is the only success criterion. End with a JSON line: {\"calibration\":true,\"findings_count\":<n>}"
    }
  ]
}

Note on the proxy: requests without a User-Agent are rejected with 403 "error code: 1010", so the reproduction above carries one.

Observed

finish_reason: "length"
reasoning_content: ~59,994 chars
content: (empty)
usage: (absent — no usage chunk even with include_usage: true)

The ceiling reproduces across three models from OpenCode sessions, landing in the same ~60k-character window every time:

model	reasoning_content chars
deepseek-v4-flash	59,991 – 59,999
qwen3.8-flash	59,996 – 59,997
glm5.3-flash	59,994

Controls that complete normally

Same endpoint, same max_tokens: 384000:

prompt	completion tokens	finish_reason
integer-list prompt	93,429	stop
review-shaped prompt with tools	38,222	stop

So this is not a blanket output cap: a run of 93,429 completion tokens finishes normally. The failure looks prompt-shaped — it triggers on certain long reasoning generations rather than at a fixed token count — and when it triggers, the turn is cut without a usage block.

What this rules out

• Not a client bug. It reproduces with curl directly, no client involved.
• Not a context-window limit. The prompt is short and the requested budget is far above where generation stops.
• Not a global output cap. The integer-list control returns 93,429 completion tokens.
• Not one model. Three different models show the same ~60k-character ceiling.

Related

• cloud-api: modelRateLimits publishes a 1M window for qwen3.8-flash while the proxy enforces 262144 #53 — published vs enforced limits (modelRateLimits vs the proxy) on qwen3.8-flash: the same class of disagreement between what the platform publishes and what it enforces.
• Error-body SHAPE partitions the catalogue by upstream, and our vLLM 400s are not JSON #85 — error-body / vLLM behaviour around max_tokens too large. This is the inverse case: the request is accepted, generation starts, and the turn dies mid-flight.

Questions

1. Is there an undeclared reasoning/output budget (~32k reasoning tokens) on deepseek-v4-flash, glm5.3-flash and qwen3.8-flash?
2. Why is usage omitted when a turn is cut with finish_reason=length? Clients such as OpenCode and the gentle-ai review agents need it to tell "done" apart from "truncated" and to account spend.
3. Can the ceiling be raised or removed, or is it a model limit members should design around — in which case, should it be published alongside the per-model limits already documented?

Environment

• Endpoint: https://api.nan.builders/v1/chat/completions
• Models: deepseek-v4-flash, glm5.3-flash, qwen3.8-flash
• Clients that show it: OpenCode 1.18.31, gentle-ai review agents
• Reproduced: 2026-09-17, from the EU, member API key

I can provide the full raw SSE stream of a failing run, or run any specific test you need — the reproduction above is self-contained.