fix: send supported reasoning parameters to gpt-5.1 and gpt-6 models

This commit is contained in:
Manuel Escalante
2026-10-26 18:35:47 +01:00
parent 55e830904d
commit 542ff12890
2 changed files with 39 additions and 15 deletions
@@ -5,6 +5,7 @@ Models are loaded dynamically from the API.
"""
from typing import Optional, List
import json
import re
import urllib.request
import urllib.error
from .base import AIProvider, AIProviderError
@@ -36,11 +37,11 @@ class OpenAIProvider(AIProvider):
]
# Recommended models for chat (in priority order)
RECOMMENDED_PREFIXES = ['gpt-4o-mini', 'gpt-4o', 'gpt-4-turbo', 'gpt-4', 'gpt-3.5-turbo']
RECOMMENDED_PREFIXES = ['gpt-6-luna', 'gpt-5.6-luna', 'gpt-5.4-mini', 'gpt-6-sol']
@staticmethod
def _is_reasoning_model(model: str) -> bool:
"""True for OpenAI reasoning models (o-series + non-chat gpt-5+).
"""True for OpenAI reasoning models (o-series + non-chat gpt-5 and later).
These use a stricter API contract than chat models:
- Must use ``max_completion_tokens`` instead of ``max_tokens``
@@ -54,11 +55,24 @@ class OpenAIProvider(AIProvider):
# o1, o3, o4, o5 ... (o<digit>...)
if len(m) >= 2 and m[0] == 'o' and m[1].isdigit():
return True
# gpt-5, gpt-5-mini, gpt-5.1, gpt-5.2-pro ... EXCEPT *-chat-latest
if m.startswith('gpt-5') and '-chat' not in m:
return True
return False
# gpt-5, gpt-5-mini, gpt-5.1, gpt-6-luna, gpt-6.1-sol ... EXCEPT *-chat-latest
major = re.match(r'gpt-(\d+)', m)
return bool(major) and int(major.group(1)) >= 5 and '-chat' not in m
@staticmethod
def _reasoning_effort(model: str) -> str:
"""Lowest reasoning_effort the model accepts.
gpt-5 / gpt-5-mini / gpt-5-nano (and the o-series) accept 'minimal'.
gpt-5.1 and later reject 'minimal' with HTTP 400, and gpt-6-astra /
gpt-6.1-sol also reject 'none', so 'low' is the value all of them
accept. In tests it spent 0-5 reasoning tokens on a notification.
"""
m = model.lower()
if re.match(r'gpt-5(?!\.)', m) or m.startswith('o'):
return 'minimal'
return 'low'
def list_models(self) -> List[str]:
"""List available models for chat completions.
@@ -189,22 +203,19 @@ class OpenAIProvider(AIProvider):
# apply model-specific defaults.
if self.base_url:
payload['max_tokens'] = max_tokens
# Reasoning models (o1/o3/o4/gpt-5*, excluding *-chat-latest) use a
# Reasoning models (o1/o3/o4/gpt-5+, excluding *-chat-latest) use a
# different parameter contract: max_completion_tokens instead of
# max_tokens, and no temperature field. Sending the classic chat
# parameters to them produces HTTP 400 Bad Request.
#
# They also spend output budget on internal reasoning by default,
# which empties the user-visible reply when max_tokens is small
# (like the ~200 we use for notifications). reasoning_effort
# 'minimal' keeps that internal reasoning to a minimum so the
# entire budget is available for the translation, which is
# exactly what this pipeline wants. OpenAI documents 'minimal',
# 'low', 'medium', 'high' — 'minimal' is the right setting for a
# straightforward translate+explain task.
# (like the ~200 we use for notifications). The lowest
# reasoning_effort the model accepts keeps that internal reasoning
# to a minimum so the budget goes to the translation.
elif self._is_reasoning_model(self.model):
payload['max_completion_tokens'] = max_tokens
payload['reasoning_effort'] = 'minimal'
payload['reasoning_effort'] = self._reasoning_effort(self.model)
else:
payload['max_tokens'] = max_tokens
payload['temperature'] = 0.3
@@ -98,6 +98,19 @@ class OpenAICompatibleModelTests(unittest.TestCase):
self.assertNotIn("temperature", provider.captured_payload)
self.assertNotIn("max_tokens", provider.captured_payload)
def test_newer_reasoning_models_use_low_effort(self):
# gpt-5.1+ reject 'minimal'; gpt-6* were sent chat parameters before.
for model in ("gpt-5.6-luna", "gpt-6-luna", "gpt-6.1-sol"):
with self.subTest(model=model):
provider = CapturingOpenAIProvider(api_key="token", model=model)
self.assertEqual(provider.generate("system", "user", max_tokens=123), "ok")
self.assertEqual(provider.captured_payload["max_completion_tokens"], 123)
self.assertEqual(provider.captured_payload["reasoning_effort"], "low")
self.assertNotIn("temperature", provider.captured_payload)
self.assertNotIn("max_tokens", provider.captured_payload)
def test_runtime_ai_config_prefers_provider_specific_model(self):
manager = self._manager({
"ai_enabled": "true",