feat/upgrade open ai model to 5.4mini

backend/nlp.py

@@ -15,9 +15,8 @@
 from posthog_client import posthog_client as ph_client
 
 MODEL_PARAMS = {
-    "model": "gpt-4o",
-    # "model": "gpt-5-mini",
-    # "reasoning_effort": "minimal",
+    "model": "gpt-5.4-mini",
+    "reasoning_effort": "low",
     # "text_verbosity": "medium"
 }
 DEBUG_PROMPTS = False
@@ -43,8 +42,7 @@ async def warmup_nlp():
     # make a dummy request to make sure everything is imported
     try:
         await dummy_client.chat.completions.create(
-            model="gpt-4o",
-            # model="gpt-5-mini",
+            model="gpt-5.4-mini",
             # reasoning_effort="minimal",
             messages=[{"role": "user", "content": "Hello"}],
         )

experiment/app/api/chat/route.ts

@@ -9,7 +9,7 @@ export async function POST(req: Request) {
   const scenario = getScenario(scenarioId);
 
   const result = streamText({
-    model: openai('gpt-5.2'),
+    model: openai('gpt-5.4-mini', { reasoningEffort: 'low' }),
     system: scenario.chat.systemPrompt,
     messages: convertToModelMessages(messages),
     maxOutputTokens: 300,

experiment/app/api/writing-support/route.ts

@@ -142,10 +142,11 @@ export async function POST(req: Request) {
     }
 
     const result = await generateObject({
-      model: openai('gpt-5.2'),
+      model: openai('gpt-5.4-mini', { reasoningEffort: 'low' }),
       schema: listResponseSchema,
       prompt: fullPrompt,
       system: 'You are a helpful and insightful writing assistant.',
+      maxOutputTokens: 500,
     });
 
     const suggestions = result.object.responses.length > 0

experiment/lib/eval/colleagueEval.ts

@@ -49,7 +49,7 @@ export async function evalColleagueResponse(
   testInput: string,
   colleagueResponse: string,
   criterion: EvalCriterion,
-  model: string = 'gpt-4o-mini'
+  model: string = 'gpt-5.4-mini'
 ): Promise<EvalResult> {
   const judgePrompt = `You are evaluating an AI colleague's response in a workplace chat scenario.
 
@@ -62,7 +62,7 @@ Think step by step, then answer with ONLY a JSON object (no markdown, no code bl
 {"answer": "yes" or "no", "reasoning": "brief 1-sentence explanation"}`;
 
   const result = await generateText({
-    model: openai(model),
+    model: openai(model, { reasoningEffort: 'low' }),
     prompt: judgePrompt,
     maxOutputTokens: 150,
   });

experiment/scripts/evalColleague.ts

@@ -41,7 +41,7 @@ async function callColleague(
   ];
 
   const result = await generateText({
-    model: openai('gpt-5.2'),
+    model: openai('gpt-5.4-mini', { reasoningEffort: 'low' }),
     system: systemPrompt,
     messages,
     maxOutputTokens: 300,

scripts/llm_analysis.py

@@ -46,7 +46,7 @@ def get_scenario_context(scenario_id: str) -> dict:
     return SCENARIOS.get(scenario_id, SCENARIOS['roomDoubleBooking'])
 
 
-def analyze_email_quality(email_text: str, scenario_id: str, model: str = 'gpt-4o') -> dict:
+def analyze_email_quality(email_text: str, scenario_id: str, model: str = 'gpt-5.4-mini') -> dict:
     """
     Analyze email quality on multiple dimensions.
 
@@ -109,12 +109,13 @@ def analyze_email_quality(email_text: str, scenario_id: str, model: str = 'gpt-4
         model=model,
         messages=[{'role': 'user', 'content': prompt}],
         response_format={'type': 'json_object'},
+        reasoning_effort='low',
     )
 
     return json.loads(response.choices[0].message.content)
 
 
-def analyze_recipient_feelings(email_text: str, scenario_id: str, model: str = 'gpt-4o') -> dict:
+def analyze_recipient_feelings(email_text: str, scenario_id: str, model: str = 'gpt-5.4-mini') -> dict:
     """
     Analyze how well the email addresses the recipient's likely emotions.
 
@@ -169,12 +170,13 @@ def analyze_recipient_feelings(email_text: str, scenario_id: str, model: str = '
         model=model,
         messages=[{'role': 'user', 'content': prompt}],
         response_format={'type': 'json_object'},
+        reasoning_effort='low',
     )
 
     return json.loads(response.choices[0].message.content)
 
 
-def extract_factual_questions(email_text: str, scenario_id: str, model: str = 'gpt-4o') -> list[str]:
+def extract_factual_questions(email_text: str, scenario_id: str, model: str = 'gpt-5.4-mini') -> list[str]:
     """
     Generate a list of factual questions a careful reader would want to verify.
 
@@ -215,6 +217,7 @@ def extract_factual_questions(email_text: str, scenario_id: str, model: str = 'g
         model=model,
         messages=[{'role': 'user', 'content': prompt}],
         response_format={'type': 'json_object'},
+        reasoning_effort='low',
     )
 
     result = json.loads(response.choices[0].message.content)
@@ -224,7 +227,7 @@ def extract_factual_questions(email_text: str, scenario_id: str, model: str = 'g
 def compare_questions_to_chat(
     questions: list[str],
     chat_messages: list[dict],
-    model: str = 'gpt-4o'
+    model: str = 'gpt-5.4-mini'
 ) -> dict:
     """
     Compare the factual questions that should be asked against
@@ -270,6 +273,7 @@ def compare_questions_to_chat(
         model=model,
         messages=[{'role': 'user', 'content': prompt}],
         response_format={'type': 'json_object'},
+        reasoning_effort='low',
     )
 
     return json.loads(response.choices[0].message.content)
@@ -278,7 +282,7 @@ def compare_questions_to_chat(
 def analyze_ai_influence(
     email_text: str,
     ai_suggestions: list[dict],
-    model: str = 'gpt-4o'
+    model: str = 'gpt-5.4-mini'
 ) -> dict:
     """
     Analyze how much of the AI suggestions made it into the final email.
@@ -345,6 +349,7 @@ def analyze_ai_influence(
         model=model,
         messages=[{'role': 'user', 'content': prompt}],
         response_format={'type': 'json_object'},
+        reasoning_effort='low',
     )
 
     result = json.loads(response.choices[0].message.content)
@@ -355,7 +360,7 @@ def analyze_ai_influence(
 
 def run_full_analysis(
     participant_data: dict,
-    model: str = 'gpt-4o',
+    model: str = 'gpt-5.4-mini',
     cache=None
 ) -> dict:
     """