{"slug": "your-kimi-k3-1-pro-responses-truncate-silently", "title": "Your Kimi K3.1 Pro Responses Truncate Silently", "summary": "A developer documented a silent-truncation failure mode when pointing OpenAI-compatible clients at Kimi K3.1 Pro: streaming loops that only read delta.content miss the finish_reason field on the final chunk, so generations cut off at max_tokens are stored as complete answers. The writeup provides a Python loop that captures finish_reason and raises on \"length\", a budgeted_max_tokens helper that sizes requests against the documented context limit, and a parity check for comparing the Pro and Highspeed variants, which use separate model strings and are not guaranteed to produce identical output.", "body_md": "You point an existing OpenAI-compatible client at Kimi K3.1 Pro and it works on the first try. Then a long generation comes back cut off mid-sentence, and nothing in your code complained. The request succeeded, the stream closed cleanly, and your user got half an answer.\n\nBy the end you can detect a truncated stream, size requests against the documented context limit, and choose between the Pro and Highspeed variants with evidence instead of vibes.\n\nEvery chat completion comes back with a `finish_reason`. It is `stop` when the model finished naturally and `length` when it hit your `max_tokens` cap. In a non-streaming call you would see it on the response object. In a streaming call it arrives on the final chunk, and most hand-written stream loops only read `delta.content`.\n\nThat is the failure. Your loop consumes text, the connection closes, and `finish_reason == \"length\"` lands in a chunk you never inspected. The model had more to say and your application stored a truncated answer as if it were complete.\n\nThe `max_tokens` parameter makes this worse. Leave it unset and some providers apply a conservative default. Set it too high and a long prompt pushes you over the context limit, which fails the whole request instead of truncating it.\n\nThis loop reads the text and records the termination reason from the last chunk.\n\n``` python\nfrom openai import OpenAI\n\nclient = OpenAI(\n    base_url=\"https://api.example.com/v1\",\n    api_key=\"sk-...\",\n)\n\nstream = client.chat.completions.create(\n    model=\"kimi-k3.1-pro\",  # use the exact ID your provider lists\n    messages=[{\"role\": \"user\", \"content\": user_prompt}],\n    max_tokens=2048,\n    stream=True,\n)\n\nfinish_reason = None\nfor chunk in stream:\n    choice = chunk.choices[0]\n    if choice.delta.content:\n        yield choice.delta.content\n    if choice.finish_reason:\n        finish_reason = choice.finish_reason\n\nif finish_reason == \"length\":\n    raise RuntimeError(\"Generation hit the token cap mid-output\")\n```\n\nThe check runs after the loop, not inside it, because the final chunk often carries `finish_reason` with empty content. Raising here turns a silent truncation into a visible failure your retry path can handle.\n\nModel IDs differ between providers, so confirm the string against your provider's model list before shipping. A typo returns a 404 that looks like an auth problem.\n\nKimi ships K3.1 Pro in two variants, and they are separate model strings rather than a speed setting on one endpoint. Swapping the string changes latency and output, so treat the choice as a real decision.\n\n| Variant | Use it when | Tradeoff | \n|---|---|---|\n| K3.1 Pro | Correctness is the priority | Slower per request | \n| K3.1 Pro Highspeed | Latency and throughput dominate | Not guaranteed output-identical to Pro | \n\nDo not assume Highspeed returns the same text faster. Run your own parity check on a sample of real prompts before you migrate anything that feeds a downstream parser.\n\n``` python\ndef parity_report(prompt, model_a, model_b):\n    a = client.chat.completions.create(\n        model=model_a, messages=prompt, temperature=0\n    ).choices[0].message.content\n    b = client.chat.completions.create(\n        model=model_b, messages=prompt, temperature=0\n    ).choices[0].message.content\n    return {\"identical\": a == b, \"a\": a, \"b\": b}\n```\n\nPin the model string in one constant. If a variant change silently alters output, you want the diff to be a deliberate commit, not a config drift nobody noticed.\n\nTruncation and request failure have opposite causes. You fail when the prompt plus `max_tokens` exceeds the context limit, and you truncate when `max_tokens` is smaller than the answer needs. Budget explicitly against the limit your provider documents.\n\n``` python\ndef budgeted_max_tokens(prompt_tokens, context_limit, cap=4096):\n    remaining = context_limit - prompt_tokens\n    if remaining <= 0:\n        raise ValueError(f\"Prompt exceeds the context limit by {-remaining} tokens\")\n    return min(cap, remaining)\n```\n\nCount the prompt with the same tokenizer the provider uses, not a character heuristic. A rough estimate is fine for dashboards, but the request that crosses the line is the one you measured badly.\n\nA retry loop that re-sends a request containing tool calls can fire a side effect twice. Rate-limit errors are safe to retry; a half-executed tool call is not.\n\n``` python\nimport time\n\ndef call_with_retry(fn, attempts=4, base=1.0):\n    for attempt in range(attempts):\n        try:\n            return fn()\n        except Exception as exc:\n            if attempt == attempts - 1 or \"429\" not in str(exc):\n                raise\n        time.sleep(base * 2 ** attempt)\n```\n\nThis retries only on the rate-limit error and re-raises everything else. Give tool calls their own idempotency key so a retry is a duplicate detection problem rather than a double-charge problem.\n\n`finish_reason` on the final chunk, so check it after your loop ends.`finish_reason == \"length\"` means the model hit your cap, not that it finished; treat it as an error.`max_tokens` from the documented context limit instead of copying a constant.\n[Kimi K3.1 Pro and K3.1 Pro Highspeed](https://www.kimi.ai/blog/kimi-k3-1-pro) announces the two variants. I added the stream-termination checks, token budgeting, and retry handling that the announcement leaves out.\n\nThese write-ups are researched and published with no paywall, sponsor, or tracking. If one saved you an afternoon, a small tip keeps them coming.\n\nUSDT, USDC or USDD · TRC-20 (Tron)\n\n```\nTFTNsfyomKrnUutRjBTGVULp19ByW29KbY\n```\n\n", "url": "https://wpnews.pro/news/your-kimi-k3-1-pro-responses-truncate-silently", "canonical_source": "https://dev.to/robust_true_try/your-kimi-k31-pro-responses-truncate-silently-2odg", "published_at": "2026-10-24 02:25:24+00:00", "updated_at": "2026-10-24 02:41:21.653507+00:00", "lang": "en", "topics": ["large-language-models", "ai-tools", "developer-tools", "ai-products"], "entities": ["Kimi", "K3.1 Pro", "K3.1 Pro Highspeed", "OpenAI"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/your-kimi-k3-1-pro-responses-truncate-silently", "markdown": "https://wpnews.pro/news/your-kimi-k3-1-pro-responses-truncate-silently.md", "text": "https://wpnews.pro/news/your-kimi-k3-1-pro-responses-truncate-silently.txt", "jsonld": "https://wpnews.pro/news/your-kimi-k3-1-pro-responses-truncate-silently.jsonld"}}