fix(platform-cost): correct token avg denominator; add CostBucket type; update generated TS

- avg_input/output_tokens_per_request now divide by token_bearing_requests
  (rows where trackingType='tokens') instead of cost_bearing_requests
  (rows where trackingType='cost_usd'). These are different DB rows: LLM
  calls log tokens under 'tokens' type, not 'cost_usd', so the old
  denominator was wrong and produced inflated averages.
- Add CostBucket TypedDict to platform_cost.py; replace list[dict] with
  list[CostBucket] for type safety on cost_buckets field.
- Update openapi.json cost_buckets item schema with explicit bucket/count
  properties so orval generates a typed interface instead of object.
- Import CostBucket in PlatformCostContent.tsx and use it instead of
  inline anonymous type on the .map() callback.
- Add test assertions for avg_input/output_tokens_per_request and
  avg_cost_microdollars_per_request in test_returns_dashboard_with_data
  to lock in the correct denominator behaviour.

autogpt_platform/backend/backend/data/platform_cost.py

@@ -1,7 +1,7 @@
 import asyncio
 import logging
 from datetime import datetime, timedelta, timezone
-from typing import Any
+from typing import Any, TypedDict
 
 from prisma.models import PlatformCostLog as PrismaLog
 from prisma.models import User as PrismaUser
@@ -162,6 +162,11 @@ class CostLogRow(BaseModel):
     cache_creation_tokens: int | None = None
 
 
+class CostBucket(TypedDict):
+    bucket: str
+    count: int
+
+
 class PlatformCostDashboard(BaseModel):
     by_provider: list[ProviderCostSummary]
     by_user: list[UserCostSummary]
@@ -177,7 +182,7 @@ class PlatformCostDashboard(BaseModel):
     cost_p75_microdollars: float = 0.0
     cost_p95_microdollars: float = 0.0
     cost_p99_microdollars: float = 0.0
-    cost_buckets: list[dict] = []
+    cost_buckets: list[CostBucket] = []
 
 
 def _si(row: dict, field: str) -> int:
@@ -435,6 +440,11 @@ async def get_platform_cost_dashboard(
     cost_bearing_requests = sum(
         _ca(r) for r in total_agg_groups if r.get("trackingType") == "cost_usd"
     )
+    # Token-bearing request count: only rows where trackingType == "tokens".
+    # Token averages must use this denominator; cost_usd rows do not carry tokens.
+    token_bearing_requests = sum(
+        _ca(r) for r in total_agg_groups if r.get("trackingType") == "tokens"
+    )
 
     return PlatformCostDashboard(
         by_provider=[
@@ -470,13 +480,13 @@ async def get_platform_cost_dashboard(
         total_input_tokens=total_input_tokens,
         total_output_tokens=total_output_tokens,
         avg_input_tokens_per_request=(
-            total_input_tokens / cost_bearing_requests
-            if cost_bearing_requests > 0
+            total_input_tokens / token_bearing_requests
+            if token_bearing_requests > 0
             else 0.0
         ),
         avg_output_tokens_per_request=(
-            total_output_tokens / cost_bearing_requests
-            if cost_bearing_requests > 0
+            total_output_tokens / token_bearing_requests
+            if token_bearing_requests > 0
             else 0.0
         ),
         avg_cost_microdollars_per_request=(

autogpt_platform/backend/backend/data/platform_cost_test.py

@@ -324,6 +324,11 @@ class TestGetPlatformCostDashboard:
         assert dashboard.cost_p50_microdollars == 1000
         assert dashboard.cost_p95_microdollars == 4000
         assert len(dashboard.cost_buckets) == 1
+        # Token averages must use token_bearing_requests (3) not cost_bearing (0)
+        assert dashboard.avg_input_tokens_per_request == pytest.approx(1000 / 3)
+        assert dashboard.avg_output_tokens_per_request == pytest.approx(500 / 3)
+        # No cost_usd rows in total_agg → avg_cost should be 0
+        assert dashboard.avg_cost_microdollars_per_request == 0.0
 
     @pytest.mark.asyncio
     async def test_cache_tokens_aggregated_not_hardcoded(self):

autogpt_platform/frontend/src/app/(platform)/admin/platform-costs/components/PlatformCostContent.tsx

@@ -8,6 +8,7 @@ import { ProviderTable } from "./ProviderTable";
 import { UserTable } from "./UserTable";
 import { LogsTable } from "./LogsTable";
 import { usePlatformCostContent } from "./usePlatformCostContent";
+import type { CostBucket } from "@/app/api/__generated__/models/platformCostDashboard";
 
 interface Props {
   searchParams: {
@@ -298,7 +299,7 @@ export function PlatformCostContent({ searchParams }: Props) {
                   </h3>
                   <div className="grid grid-cols-2 gap-2 sm:grid-cols-3 md:grid-cols-6">
                     {dashboard.cost_buckets.map(
-                      (b: { bucket: string; count: number }) => (
+                      (b: CostBucket) => (
                         <div
                           key={b.bucket}
                           className="flex flex-col items-center rounded border p-2 text-center"

autogpt_platform/frontend/src/app/api/openapi.json

@@ -12151,7 +12151,7 @@
           "cost_p75_microdollars": { "type": "number", "title": "Cost P75 Microdollars", "default": 0.0 },
           "cost_p95_microdollars": { "type": "number", "title": "Cost P95 Microdollars", "default": 0.0 },
           "cost_p99_microdollars": { "type": "number", "title": "Cost P99 Microdollars", "default": 0.0 },
-          "cost_buckets": { "type": "array", "items": { "type": "object" }, "title": "Cost Buckets", "default": [] }
+          "cost_buckets": { "type": "array", "items": { "type": "object", "properties": { "bucket": { "type": "string" }, "count": { "type": "integer" } }, "required": ["bucket", "count"] }, "title": "Cost Buckets", "default": [] }
         },
         "type": "object",
         "required": [