diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 11ca43d8..ea2fb245 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -66,6 +66,13 @@ repos:
       entry: tools/addlicense.sh
       pass_filenames: true
       files: '\.py$'
+    - id: model-cost-in-sync
+      name: model_cost.json is generated from model_profiles.yaml
+      language: python
+      entry: python scripts/pricing/build_model_cost.py --check
+      additional_dependencies: [pyyaml]
+      pass_filenames: false
+      files: '^(model_cost/|scripts/pricing/)'
 # Add mypy hooks for both local runs and CI (manual stage)
 - repo: local
   hooks:
diff --git a/README.md b/README.md
index 8af2c2d9..f3e3d86a 100644
--- a/README.md
+++ b/README.md
@@ -140,17 +140,18 @@ Create a config file in `./router_inference/config/<router_name>.json`. An examp
 }
 ```
 
-For each model in your config, add an entry with the pricing per million tokens in this format at [`model_cost/model_cost.json`](./model_cost/model_cost.json):
-
-```json
-{
-  "gpt-4o-mini": {
-    "input_token_price_per_million": 0.15,
-    "output_token_price_per_million": 0.6
-  },
-}
+Every model in your config needs a price. Prices live in [`model_cost/model_profiles.yaml`](./model_cost/model_profiles.yaml): one entry per model, keyed by its OpenRouter id, with the other spellings of the same model as aliases. If your model is listed under another spelling, add yours to its `aliases`. For a new model, add an entry with its price per million tokens and a link to the published price:
+
+```yaml
+  "openai/gpt-4o-mini":
+    price: {input: 0.15, output: 0.6}
+    aliases: ["gpt-4o-mini"]
+    as_of: "2026-10-31"
+    source: "https://openrouter.ai/openai/gpt-4o-mini"
 ```
 
+Then regenerate [`model_cost/model_cost.json`](./model_cost/model_cost.json), which the evaluator reads, with `python scripts/pricing/build_model_cost.py`. A model name with no entry is charged the highest price in the table.
+
 > [!NOTE]
 > Ensure all models in your above config files are listed in [`./universal_model_names.py`](./universal_model_names.py). If you add a new model, you must also add the API inference endpoint in [`llm_inference/model_inference.py`](./llm_inference/model_inference.py).
 
@@ -221,7 +222,7 @@ To get your router on the leaderboard, you can open a Pull Request with your rou
    - When the PR is ready for evaluation, call `/evaluate` in the PR comment to trigger the evaluation workflow. See an example [here](https://github.com/RouteWorks/RouterArena/pull/71#issuecomment-3904936480).
    - The automated workflow will:
      - Validate your submission
-     - Run evaluation on the full dataset with the evaluation code on `main`. Only the three files above are taken from your branch, plus any new models you add to `model_cost/model_cost.json` and `universal_model_names.py` (models already listed keep their price on `main`)
+     - Run evaluation on the full dataset with the evaluation code on `main`. Only the three files above are taken from your branch, plus any new models you add to the price table (`model_cost/model_profiles.yaml` and the generated `model_cost/model_cost.json`) and `universal_model_names.py` (models already listed keep their price on `main`)
      - Post results as a comment on your PR
      - Update the leaderboard upon approval
 
diff --git a/llm_evaluation/evaluate_models.py b/llm_evaluation/evaluate_models.py
index 854dd1f8..36d4bd3e 100644
--- a/llm_evaluation/evaluate_models.py
+++ b/llm_evaluation/evaluate_models.py
@@ -16,6 +16,7 @@
 import json
 import argparse
 import glob
+import threading
 from typing import Dict, List, Any, Optional
 import sys
 
@@ -87,6 +88,10 @@ def __init__(
             str, Any
         ] = {}  # Store existing results for incremental evaluation
         self.cost_config: Dict[str, Any] = {}  # Store cost configuration
+        # Model names charged at the maximum price because no price profile
+        # matched them -> number of rows.
+        self.unpriced_models: Dict[str, int] = {}
+        self._unpriced_lock = threading.Lock()
         self.num_workers = num_workers
 
         # Load dataset configurations
@@ -207,21 +212,39 @@ def load_cost_config(self):
             self.cost_config = {}
 
     def _lookup_cost_info(self, model_name: str):
-        """Find the pricing entry for a model name, trying an exact match first
-        and then a substring fallback (historical behaviour). Returns the cost
-        dict, or None if no price is known."""
+        """Find the pricing entry for a model name by exact match on a model id
+        or alias (see model_cost/model_profiles.yaml). Returns the cost dict, or
+        None if no price is known.
+
+        There is deliberately no fuzzy/substring fallback: it used to let a name
+        pick up whichever price key happened to be a substring of it."""
         if not self.cost_config or not model_name:
             return None
         # Remove _batch suffix if present for cost lookup
         cost_lookup_name = (
             model_name[:-6] if model_name.endswith("_batch") else model_name
         )
-        if cost_lookup_name in self.cost_config:
-            return self.cost_config[cost_lookup_name]
-        for config_name in self.cost_config.keys():
-            if config_name in cost_lookup_name or cost_lookup_name in config_name:
-                return self.cost_config[config_name]
-        return None
+        return self.cost_config.get(cost_lookup_name)
+
+    def max_price_info(self) -> Dict[str, float]:
+        """The highest input and output prices in the cost table. Used to charge
+        rows whose model has no price profile, so an unregistered or misspelled
+        model is never cheaper than a registered one."""
+        if not self.cost_config:
+            return {
+                "input_token_price_per_million": 0.0,
+                "output_token_price_per_million": 0.0,
+            }
+        return {
+            "input_token_price_per_million": max(
+                float(v.get("input_token_price_per_million", 0.0))
+                for v in self.cost_config.values()
+            ),
+            "output_token_price_per_million": max(
+                float(v.get("output_token_price_per_million", 0.0))
+                for v in self.cost_config.values()
+            ),
+        }
 
     def has_price(self, model_name: str) -> bool:
         """Whether a price is known for this model name. Used to decide whether a
@@ -248,14 +271,22 @@ def calculate_inference_cost(
         cost_info = self._lookup_cost_info(model_name)
 
         if not cost_info:
-            print(
-                f"Warning: No cost configuration found for model {model_name} (lookup: {cost_lookup_name})"
-            )
-            if len(self.cost_config) > 0:
+            # Unmatched model: charge the highest price in the table rather than
+            # dropping the cost, and warn once per model name.
+            cost_info = self.max_price_info()
+            with self._unpriced_lock:
+                first_time = cost_lookup_name not in self.unpriced_models
+                self.unpriced_models[cost_lookup_name] = (
+                    self.unpriced_models.get(cost_lookup_name, 0) + 1
+                )
+            if first_time:
                 print(
-                    f"Available cost config keys (first 10): {list(self.cost_config.keys())[:10]}"
+                    f"Warning: No price profile for model {cost_lookup_name!r}; "
+                    "charging the maximum price "
+                    f"(${cost_info['input_token_price_per_million']}/"
+                    f"${cost_info['output_token_price_per_million']} per 1M tokens). "
+                    "Add it to model_cost/model_profiles.yaml."
                 )
-            return 0.0
 
         # Calculate cost
         input_tokens = token_usage.get("input_tokens", 0) or 0
diff --git a/llm_evaluation/run.py b/llm_evaluation/run.py
index fbbc3f1d..c72fca01 100644
--- a/llm_evaluation/run.py
+++ b/llm_evaluation/run.py
@@ -362,14 +362,18 @@ def evaluate_single_prediction(
         )
         return False
 
-    # Convert to universal model name
+    # Convert to universal model name. An unknown name is still graded (the score
+    # does not depend on it) and is charged the maximum price below, so a router
+    # cannot drop rows from its results by naming an unregistered model.
     try:
         universal_model_name = ModelNameManager.get_universal_name(model_name)
     except Exception as e:
-        logger.error(
-            f"Error converting model name '{model_name}' to universal name: {e}"
+        # Logged once per name by the evaluator and summarised after the run.
+        logger.debug(
+            f"Unknown model name '{model_name}' ({e}); evaluating it anyway "
+            "and charging the maximum price."
         )
-        return False
+        universal_model_name = model_name
 
     # Determine dataset name from global_index
     dataset_name = evaluator.determine_dataset_from_global_index(global_index)
@@ -586,6 +590,18 @@ def save_callback():
     logger.info(
         f"Predictions saved to: ./router_inference/predictions/{router_name}.json"
     )
+    if evaluator.unpriced_models:
+        max_price = evaluator.max_price_info()
+        logger.warning(
+            "Charged at the maximum price "
+            f"(${max_price['input_token_price_per_million']}/"
+            f"${max_price['output_token_price_per_million']} per 1M tokens) "
+            "because no price profile matched:"
+        )
+        for name, rows in sorted(
+            evaluator.unpriced_models.items(), key=lambda kv: -kv[1]
+        ):
+            logger.warning(f"  {name}: {rows} rows")
     logger.info("=" * 60)
 
     # Compute and display router-level metrics
diff --git a/model_cost/model_cost.json b/model_cost/model_cost.json
index f3b8c2f2..0ff5af5a 100644
--- a/model_cost/model_cost.json
+++ b/model_cost/model_cost.json
@@ -1,143 +1,271 @@
 {
-  "google/gemma-3n-e4b-it": {
-    "input_token_price_per_million": 0.02,
-    "output_token_price_per_million": 0.04
+  "agnes-2.0-flash": {
+    "input_token_price_per_million": 0.03,
+    "output_token_price_per_million": 0.15
   },
-  "gemini-3-pro-preview": {
-    "input_token_price_per_million": 2.0,
-    "output_token_price_per_million": 12.0
+  "anthropic/claude-3-haiku": {
+    "input_token_price_per_million": 0.25,
+    "output_token_price_per_million": 1.25
   },
-  "gemini-3-flash-preview": {
-    "input_token_price_per_million": 0.5,
-    "output_token_price_per_million": 3
+  "claude-3-haiku-20240307": {
+    "input_token_price_per_million": 0.25,
+    "output_token_price_per_million": 1.25
   },
-  "o4-mini": {
-    "input_token_price_per_million": 1.1,
-    "output_token_price_per_million": 4.4
+  "anthropic/claude-haiku-4.5": {
+    "input_token_price_per_million": 1,
+    "output_token_price_per_million": 5
   },
-  "gpt-5.1": {
-    "input_token_price_per_million": 1.25,
-    "output_token_price_per_million": 10.0
+  "anthropic/claude-haiku-4-5-20251001": {
+    "input_token_price_per_million": 1,
+    "output_token_price_per_million": 5
   },
-  "gpt-5.2": {
-    "input_token_price_per_million": 1.75,
-    "output_token_price_per_million": 14.0
+  "claude-haiku-4-5": {
+    "input_token_price_per_million": 1,
+    "output_token_price_per_million": 5
   },
-  "gpt-5.1-None": {
-    "input_token_price_per_million": 1.25,
-    "output_token_price_per_million": 10.0
+  "claude-haiku-4-5-20251001": {
+    "input_token_price_per_million": 1,
+    "output_token_price_per_million": 5
   },
-  "gpt-5-mini": {
-    "input_token_price_per_million": 0.25,
-    "output_token_price_per_million": 2.0
+  "claude-haiku-4.5": {
+    "input_token_price_per_million": 1,
+    "output_token_price_per_million": 5
   },
-  "gpt-5-nano": {
-    "input_token_price_per_million": 0.05,
-    "output_token_price_per_million": 0.4
+  "anthropic/claude-haiku-5.5": {
+    "input_token_price_per_million": 0.1,
+    "output_token_price_per_million": 0.5
+  },
+  "anthropic/claude-opus-4.1": {
+    "input_token_price_per_million": 15,
+    "output_token_price_per_million": 75
+  },
+  "claude-opus-4-1": {
+    "input_token_price_per_million": 15,
+    "output_token_price_per_million": 75
+  },
+  "anthropic/claude-opus-4.6": {
+    "input_token_price_per_million": 5,
+    "output_token_price_per_million": 25
+  },
+  "claude-opus-4-6": {
+    "input_token_price_per_million": 5,
+    "output_token_price_per_million": 25
+  },
+  "anthropic/claude-opus-4.7": {
+    "input_token_price_per_million": 15,
+    "output_token_price_per_million": 75
+  },
+  "claude-opus-4-7": {
+    "input_token_price_per_million": 15,
+    "output_token_price_per_million": 75
+  },
+  "anthropic/claude-opus-5.5": {
+    "input_token_price_per_million": 4,
+    "output_token_price_per_million": 20
+  },
+  "anthropic/claude-sonnet-4": {
+    "input_token_price_per_million": 3,
+    "output_token_price_per_million": 15
+  },
+  "claude-sonnet-4": {
+    "input_token_price_per_million": 3,
+    "output_token_price_per_million": 15
+  },
+  "anthropic/claude-sonnet-4.5": {
+    "input_token_price_per_million": 3,
+    "output_token_price_per_million": 15
+  },
+  "anthropic/claude-sonnet-4-5": {
+    "input_token_price_per_million": 3,
+    "output_token_price_per_million": 15
+  },
+  "claude-sonnet-4-5": {
+    "input_token_price_per_million": 3,
+    "output_token_price_per_million": 15
+  },
+  "anthropic/claude-sonnet-5.5": {
+    "input_token_price_per_million": 2,
+    "output_token_price_per_million": 10
+  },
+  "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B": {
+    "input_token_price_per_million": 0.18,
+    "output_token_price_per_million": 0.85
+  },
+  "deepseek-v3.1": {
+    "input_token_price_per_million": 1.23,
+    "output_token_price_per_million": 4.94
+  },
+  "deepseek/deepseek-chat": {
+    "input_token_price_per_million": 0.27,
+    "output_token_price_per_million": 1.1
+  },
+  "deepseek-chat": {
+    "input_token_price_per_million": 0.27,
+    "output_token_price_per_million": 1.1
+  },
+  "deepseek/deepseek-reasoner": {
+    "input_token_price_per_million": 0.28,
+    "output_token_price_per_million": 0.42
   },
   "deepseek-reasoner": {
     "input_token_price_per_million": 0.28,
     "output_token_price_per_million": 0.42
   },
-  "gemini-3.0-pro": {
-    "input_token_price_per_million": 2.0,
-    "output_token_price_per_million": 12.0
+  "deepseek/deepseek-v3.2": {
+    "input_token_price_per_million": 0.26,
+    "output_token_price_per_million": 0.38
   },
   "deepseek-v3.2": {
-    "input_token_price_per_million": 0.28,
-    "output_token_price_per_million": 0.42
+    "input_token_price_per_million": 0.26,
+    "output_token_price_per_million": 0.38
   },
-  "test": {
-    "input_token_price_per_million": 1.0,
-    "output_token_price_per_million": 2.0
+  "deepseek/deepseek-v4-flash": {
+    "input_token_price_per_million": 0.14,
+    "output_token_price_per_million": 0.28
   },
-  "claude-sonnet-4-5": {
-    "input_token_price_per_million": 3.0,
-    "output_token_price_per_million": 15.0
+  "deepseek/deepseek-v4-flash-0731": {
+    "input_token_price_per_million": 0.14,
+    "output_token_price_per_million": 0.28
   },
-  "claude-3-haiku-20240307": {
-    "input_token_price_per_million": 0.25,
-    "output_token_price_per_million": 1.25
+  "deepseek-ai/DeepSeek-V4-Flash-0731": {
+    "input_token_price_per_million": 0.14,
+    "output_token_price_per_million": 0.28
   },
-  "gpt-4o-mini": {
-    "input_token_price_per_million": 0.15,
-    "output_token_price_per_million": 0.6
+  "deepseek/deepseek-v4-pro": {
+    "input_token_price_per_million": 0.435,
+    "output_token_price_per_million": 0.87
   },
-  "glm-4.5-air": {
+  "deepseek/deepseek-v4.1-flash": {
+    "input_token_price_per_million": 0.09,
+    "output_token_price_per_million": 0.18
+  },
+  "deepseek/deepseek-v4.1-flash@high": {
+    "input_token_price_per_million": 0.09,
+    "output_token_price_per_million": 0.18
+  },
+  "deepseek/deepseek-v4.1-flash@low": {
+    "input_token_price_per_million": 0.09,
+    "output_token_price_per_million": 0.18
+  },
+  "gemini-2.0-flash-001": {
     "input_token_price_per_million": 0.1,
-    "output_token_price_per_million": 0.3
+    "output_token_price_per_million": 0.4
   },
-  "glm-4.6": {
-    "input_token_price_per_million": 0.28,
-    "output_token_price_per_million": 1.12
+  "gemini-3-pro-preview": {
+    "input_token_price_per_million": 2,
+    "output_token_price_per_million": 12
+  },
+  "gemini-3.0-pro": {
+    "input_token_price_per_million": 2,
+    "output_token_price_per_million": 12
+  },
+  "glm-4-air": {
+    "input_token_price_per_million": 0.07,
+    "output_token_price_per_million": 0.07
   },
   "glm-4-air-250414": {
     "input_token_price_per_million": 0.07,
     "output_token_price_per_million": 0.07
   },
-  "qwen_qwen3-vl-235b-a22b-thinking": {
+  "google/gemini-2.5-flash": {
     "input_token_price_per_million": 0.3,
-    "output_token_price_per_million": 1.2
-  },
-  "gemini-2.5-flash-lite": {
-    "input_token_price_per_million": 0.1,
-    "output_token_price_per_million": 0.4
+    "output_token_price_per_million": 2.5
   },
   "gemini-2.5-flash": {
     "input_token_price_per_million": 0.3,
     "output_token_price_per_million": 2.5
   },
-  "gemini-2.0-flash-001": {
+  "google/gemini-2.5-flash-lite": {
     "input_token_price_per_million": 0.1,
     "output_token_price_per_million": 0.4
   },
-  "qwen_qwen3-vl-32b-instruct": {
-    "input_token_price_per_million": 0.5,
-    "output_token_price_per_million": 1.5
+  "gemini-2.5-flash-lite": {
+    "input_token_price_per_million": 0.1,
+    "output_token_price_per_million": 0.4
   },
-  "z-ai_glm-4.7": {
-    "input_token_price_per_million": 0.4,
-    "output_token_price_per_million": 1.5
+  "google/gemini-2.5-pro": {
+    "input_token_price_per_million": 1.25,
+    "output_token_price_per_million": 10
   },
-  "qwen_qwen3-vl-235b-a22b-instruct": {
-    "input_token_price_per_million": 0.2,
-    "output_token_price_per_million": 1.2
+  "google/gemini-3-flash-preview": {
+    "input_token_price_per_million": 0.5,
+    "output_token_price_per_million": 3
   },
-  "qwen_qwen3-coder": {
-    "input_token_price_per_million": 0.22,
-    "output_token_price_per_million": 0.95
+  "gemini-3-flash-preview": {
+    "input_token_price_per_million": 0.5,
+    "output_token_price_per_million": 3
   },
-  "x-ai_grok-code-fast-1": {
-    "input_token_price_per_million": 0.2,
+  "google/gemini-3.1-flash-lite": {
+    "input_token_price_per_million": 0.25,
     "output_token_price_per_million": 1.5
   },
-  "xiaomi_mimo-v2-flash:free": {
+  "google/gemini-3.1-flash-lite-preview": {
     "input_token_price_per_million": 0.1,
-    "output_token_price_per_million": 0.3
+    "output_token_price_per_million": 0.4
   },
-  "openai_gpt-oss-120b": {
-    "input_token_price_per_million": 0.039,
-    "output_token_price_per_million": 0.19
+  "gemini-3.1-flash-lite-preview": {
+    "input_token_price_per_million": 0.1,
+    "output_token_price_per_million": 0.4
   },
-  "qwen_qwen3-235b-a22b-2507": {
-    "input_token_price_per_million": 0.071,
-    "output_token_price_per_million": 0.463
+  "google/gemini-3.1-pro-preview": {
+    "input_token_price_per_million": 2,
+    "output_token_price_per_million": 12
   },
-  "qwen/qwen3-next-80b-a3b-instruct": {
-    "input_token_price_per_million": 0.09,
-    "output_token_price_per_million": 1.1
+  "gemini-3.1-pro-preview": {
+    "input_token_price_per_million": 2,
+    "output_token_price_per_million": 12
   },
-  "claude-haiku-4.5": {
-    "input_token_price_per_million": 1.0,
-    "output_token_price_per_million": 5.0
+  "google/gemini-3.5-flash-lite": {
+    "input_token_price_per_million": 0.3,
+    "output_token_price_per_million": 2.5
   },
-  "x-ai_grok-4.1-fast": {
-    "input_token_price_per_million": 0.2,
-    "output_token_price_per_million": 0.5
+  "google/gemini-3.7-flash": {
+    "input_token_price_per_million": 0.75,
+    "output_token_price_per_million": 3.75
   },
-  "mistralai_devstral-2512:free": {
-    "input_token_price_per_million": 0.05,
-    "output_token_price_per_million": 0.22
+  "google/gemini-3.8-flash": {
+    "input_token_price_per_million": 0.75,
+    "output_token_price_per_million": 3.75
+  },
+  "gemini-3.8-flash": {
+    "input_token_price_per_million": 0.75,
+    "output_token_price_per_million": 3.75
+  },
+  "google/gemma-3n-e4b-it": {
+    "input_token_price_per_million": 0.02,
+    "output_token_price_per_million": 0.04
+  },
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@@ -159,301 +299,317 @@
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-  "openai/gpt-5.6-luna": {
+  "qwen/qwen3-vl-235b-a22b-instruct": {
     "input_token_price_per_million": 0.2,
     "output_token_price_per_million": 1.2
   },
-  "openai/gpt-5.6-terra": {
-    "input_token_price_per_million": 2.0,
-    "output_token_price_per_million": 12.0
-  },
-  "openai/gpt-5.6-sol": {
-    "input_token_price_per_million": 4.0,
-    "output_token_price_per_million": 20.0
-  },
-  "openai/gpt-6-luna": {
-    "input_token_price_per_million": 0.1,
-    "output_token_price_per_million": 0.5
+  "qwen_qwen3-vl-235b-a22b-instruct": {
+    "input_token_price_per_million": 0.2,
+    "output_token_price_per_million": 1.2
   },
-  "openai/gpt-6-sol": {
-    "input_token_price_per_million": 2.0,
-    "output_token_price_per_million": 10.0
+  "qwen/qwen3.5-9b": {
+    "input_token_price_per_million": 0.05,
+    "output_token_price_per_million": 0.15
   },
-  "openai/gpt-6.1-sol": {
-    "input_token_price_per_million": 2.0,
-    "output_token_price_per_million": 10.0
+  "qwen/qwen3.5-flash-02-23": {
+    "input_token_price_per_million": 0.065,
+    "output_token_price_per_million": 0.26
   },
-  "anthropic/claude-sonnet-5.5": {
-    "input_token_price_per_million": 2.0,
-    "output_token_price_per_million": 10.0
+  "qwen_qwen3-vl-235b-a22b-thinking": {
+    "input_token_price_per_million": 0.3,
+    "output_token_price_per_million": 1.2
   },
-  "anthropic/claude-opus-5.5": {
-    "input_token_price_per_million": 4.0,
-    "output_token_price_per_million": 20.0
+  "qwen_qwen3-vl-32b-instruct": {
+    "input_token_price_per_million": 0.5,
+    "output_token_price_per_million": 1.5
   },
-  "moonshotai/kimi-k3": {
-    "input_token_price_per_million": 3.0,
-    "output_token_price_per_million": 15.0
+  "test": {
+    "input_token_price_per_million": 1,
+    "output_token_price_per_million": 2
   },
-  "z-ai/glm-5.3-flash": {
-    "input_token_price_per_million": 0.15,
-    "output_token_price_per_million": 0.5
+  "THUDM/GLM-4-9B-0414": {
+    "input_token_price_per_million": 0.05,
+    "output_token_price_per_million": 0.61
   },
   "upstage/solar-mini": {
     "input_token_price_per_million": 0.15,
@@ -471,27 +627,67 @@
     "input_token_price_per_million": 0.3,
     "output_token_price_per_million": 1.2
   },
-  "deepseek-ai/DeepSeek-V4-Flash-0731": {
+  "x-ai/grok-4.1-fast": {
+    "input_token_price_per_million": 0.2,
+    "output_token_price_per_million": 0.5
+  },
+  "grok-4-1-fast-reasoning": {
+    "input_token_price_per_million": 0.2,
+    "output_token_price_per_million": 0.5
+  },
+  "x-ai_grok-4.1-fast": {
+    "input_token_price_per_million": 0.2,
+    "output_token_price_per_million": 0.5
+  },
+  "x-ai/grok-4.3": {
+    "input_token_price_per_million": 1.25,
+    "output_token_price_per_million": 2.5
+  },
+  "grok-4.3": {
+    "input_token_price_per_million": 1.25,
+    "output_token_price_per_million": 2.5
+  },
+  "x-ai_grok-code-fast-1": {
+    "input_token_price_per_million": 0.2,
+    "output_token_price_per_million": 1.5
+  },
+  "xiaomi/mimo-v2.5": {
     "input_token_price_per_million": 0.14,
     "output_token_price_per_million": 0.28
   },
-  "gpt-5.6-luna": {
-    "input_token_price_per_million": 0.2,
-    "output_token_price_per_million": 1.2
+  "xiaomi_mimo-v2-flash:free": {
+    "input_token_price_per_million": 0.1,
+    "output_token_price_per_million": 0.3
   },
-  "ministral-8b-latest": {
-    "input_token_price_per_million": 0.15,
-    "output_token_price_per_million": 0.15
+  "z-ai/glm-4.5-air": {
+    "input_token_price_per_million": 0.1,
+    "output_token_price_per_million": 0.3
   },
-  "moonshotai/Kimi-K2.6": {
-    "input_token_price_per_million": 0.95,
-    "output_token_price_per_million": 4.0
+  "glm-4.5-air": {
+    "input_token_price_per_million": 0.1,
+    "output_token_price_per_million": 0.3
   },
-  "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B": {
-    "input_token_price_per_million": 0.06,
-    "output_token_price_per_million": 0.24
+  "z-ai/glm-4.6": {
+    "input_token_price_per_million": 0.28,
+    "output_token_price_per_million": 1.12
   },
-  "z-ai/glm-5.3@low": {
+  "glm-4.6": {
+    "input_token_price_per_million": 0.28,
+    "output_token_price_per_million": 1.12
+  },
+  "z-ai/glm-4.7": {
+    "input_token_price_per_million": 0.4,
+    "output_token_price_per_million": 1.5
+  },
+  "z-ai_glm-4.7": {
+    "input_token_price_per_million": 0.4,
+    "output_token_price_per_million": 1.5
+  },
+  "z-ai/glm-5": {
+    "input_token_price_per_million": 1,
+    "output_token_price_per_million": 3.2
+  },
+  "z-ai/glm-5.3": {
     "input_token_price_per_million": 0.079,
     "output_token_price_per_million": 1.4
   },
@@ -499,16 +695,20 @@
     "input_token_price_per_million": 0.079,
     "output_token_price_per_million": 1.4
   },
-  "z-ai/glm-5.3-flash@default": {
+  "z-ai/glm-5.3@low": {
+    "input_token_price_per_million": 0.079,
+    "output_token_price_per_million": 1.4
+  },
+  "z-ai/glm-5.3-flash": {
     "input_token_price_per_million": 0.041,
     "output_token_price_per_million": 0.077
   },
-  "deepseek/deepseek-v4.1-flash@low": {
-    "input_token_price_per_million": 0.09,
-    "output_token_price_per_million": 0.18
+  "glm-5.3-flash": {
+    "input_token_price_per_million": 0.041,
+    "output_token_price_per_million": 0.077
   },
-  "deepseek/deepseek-v4.1-flash@high": {
-    "input_token_price_per_million": 0.09,
-    "output_token_price_per_million": 0.18
+  "z-ai/glm-5.3-flash@default": {
+    "input_token_price_per_million": 0.041,
+    "output_token_price_per_million": 0.077
   }
 }
diff --git a/model_cost/model_profiles.yaml b/model_cost/model_profiles.yaml
new file mode 100644
index 00000000..82e78902
--- /dev/null
+++ b/model_cost/model_profiles.yaml
@@ -0,0 +1,510 @@
+# SPDX-FileCopyrightText: Copyright contributors to the RouterArena project
+# SPDX-License-Identifier: Apache-2.0
+#
+# Model price profiles: the source of truth for model_cost/model_cost.json.
+# One entry per model, keyed by its OpenRouter id when OpenRouter lists the model.
+# Every other spelling a submission uses (provider prefix, date, effort suffix such as
+# @high) is an alias. After editing, regenerate the JSON with:
+#   python scripts/pricing/build_model_cost.py
+#
+# Fields
+#   price.input / price.output  USD per 1M tokens (required)
+#   aliases                     other spellings of the same model; they share its price
+#   as_of                       date the price was recorded
+#   source                      URL of the published price (required for new models)
+#   note                        free text, e.g. known conflicts to resolve
+#
+# Prices are fixed for now; a market sync will update them later. A model name that
+# matches no id or alias is charged the highest input and output price in this file.
+
+models:
+  "agnes-2.0-flash":
+    price: {input: 0.03, output: 0.15}
+    as_of: "2026-09-27"
+    source: null
+  "anthropic/claude-3-haiku":
+    price: {input: 0.25, output: 1.25}
+    aliases: ["claude-3-haiku-20240307"]
+    as_of: "2026-09-27"
+    source: null
+    note: "claude-3-haiku-20240307: retired from OpenRouter; kept at its last listed price."
+  "anthropic/claude-haiku-4.5":
+    price: {input: 1, output: 5}
+    aliases: ["anthropic/claude-haiku-4-5-20251001", "claude-haiku-4-5", "claude-haiku-4-5-20251001", "claude-haiku-4.5"]
+    as_of: "2026-09-27"
+    source: null
+    note: "Unified spellings; previous prices: claude-haiku-4-5 0.8/4."
+  "anthropic/claude-haiku-5.5":
+    price: {input: 0.1, output: 0.5}
+    as_of: null
+    source: null
+  "anthropic/claude-opus-4.1":
+    price: {input: 15, output: 75}
+    aliases: ["claude-opus-4-1"]
+    as_of: "2026-09-27"
+    source: null
+  "anthropic/claude-opus-4.6":
+    price: {input: 5, output: 25}
+    aliases: ["claude-opus-4-6"]
+    as_of: "2026-09-27"
+    source: null
+  "anthropic/claude-opus-4.7":
+    price: {input: 15, output: 75}
+    aliases: ["claude-opus-4-7"]
+    as_of: "2026-09-27"
+    source: null
+  "anthropic/claude-opus-5.5":
+    price: {input: 4, output: 20}
+    as_of: null
+    source: null
+  "anthropic/claude-sonnet-4":
+    price: {input: 3, output: 15}
+    aliases: ["claude-sonnet-4"]
+    as_of: "2026-09-27"
+    source: null
+    note: "anthropic/claude-sonnet-4: retired from OpenRouter; kept at its last listed price."
+  "anthropic/claude-sonnet-4.5":
+    price: {input: 3, output: 15}
+    aliases: ["anthropic/claude-sonnet-4-5", "claude-sonnet-4-5"]
+    as_of: "2026-09-27"
+    source: null
+  "anthropic/claude-sonnet-5.5":
+    price: {input: 2, output: 10}
+    as_of: null
+    source: null
+  "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B":
+    price: {input: 0.18, output: 0.85}
+    as_of: "2026-09-27"
+    source: null
+  "deepseek-v3.1":
+    price: {input: 1.23, output: 4.94}
+    as_of: "2026-09-27"
+    source: null
+  "deepseek/deepseek-chat":
+    price: {input: 0.27, output: 1.1}
+    aliases: ["deepseek-chat"]
+    as_of: "2026-09-27"
+    source: null
+  "deepseek/deepseek-reasoner":
+    price: {input: 0.28, output: 0.42}
+    aliases: ["deepseek-reasoner"]
+    as_of: "2026-09-27"
+    source: null
+  "deepseek/deepseek-v3.2":
+    price: {input: 0.26, output: 0.38}
+    aliases: ["deepseek-v3.2"]
+    as_of: "2026-09-27"
+    source: null
+  "deepseek/deepseek-v4-flash":
+    price: {input: 0.14, output: 0.28}
+    as_of: "2026-09-27"
+    source: null
+  "deepseek/deepseek-v4-flash-0731":
+    price: {input: 0.14, output: 0.28}
+    aliases: ["deepseek-ai/DeepSeek-V4-Flash-0731"]
+    as_of: null
+    source: null
+  "deepseek/deepseek-v4-pro":
+    price: {input: 0.435, output: 0.87}
+    as_of: "2026-09-27"
+    source: null
+  "deepseek/deepseek-v4.1-flash":
+    price: {input: 0.09, output: 0.18}
+    aliases: ["deepseek/deepseek-v4.1-flash@high", "deepseek/deepseek-v4.1-flash@low"]
+    as_of: null
+    source: null
+  "gemini-2.0-flash-001":
+    price: {input: 0.1, output: 0.4}
+    as_of: "2026-09-27"
+    source: null
+  "gemini-3-pro-preview":
+    price: {input: 2, output: 12}
+    as_of: "2026-09-27"
+    source: null
+  "gemini-3.0-pro":
+    price: {input: 2, output: 12}
+    as_of: "2026-09-27"
+    source: null
+  "glm-4-air":
+    price: {input: 0.07, output: 0.07}
+    aliases: ["glm-4-air-250414"]
+    as_of: "2026-09-27"
+    source: null
+    note: "Alias glm-4-air was previously matched by substring; kept at this price."
+  "google/gemini-2.5-flash":
+    price: {input: 0.3, output: 2.5}
+    aliases: ["gemini-2.5-flash"]
+    as_of: "2026-09-27"
+    source: null
+  "google/gemini-2.5-flash-lite":
+    price: {input: 0.1, output: 0.4}
+    aliases: ["gemini-2.5-flash-lite"]
+    as_of: "2026-09-27"
+    source: null
+  "google/gemini-2.5-pro":
+    price: {input: 1.25, output: 10}
+    as_of: "2026-09-27"
+    source: null
+  "google/gemini-3-flash-preview":
+    price: {input: 0.5, output: 3}
+    aliases: ["gemini-3-flash-preview"]
+    as_of: "2026-09-27"
+    source: null
+  "google/gemini-3.1-flash-lite":
+    price: {input: 0.25, output: 1.5}
+    as_of: "2026-09-27"
+    source: null
+  "google/gemini-3.1-flash-lite-preview":
+    price: {input: 0.1, output: 0.4}
+    aliases: ["gemini-3.1-flash-lite-preview"]
+    as_of: "2026-09-27"
+    source: null
+  "google/gemini-3.1-pro-preview":
+    price: {input: 2, output: 12}
+    aliases: ["gemini-3.1-pro-preview"]
+    as_of: "2026-09-27"
+    source: null
+  "google/gemini-3.5-flash-lite":
+    price: {input: 0.3, output: 2.5}
+    as_of: null
+    source: null
+  "google/gemini-3.7-flash":
+    price: {input: 0.75, output: 3.75}
+    as_of: null
+    source: null
+  "google/gemini-3.8-flash":
+    price: {input: 0.75, output: 3.75}
+    aliases: ["gemini-3.8-flash"]
+    as_of: null
+    source: null
+  "google/gemma-3n-e4b-it":
+    price: {input: 0.02, output: 0.04}
+    as_of: "2026-09-27"
+    source: null
+  "google/gemma-4-31b-it":
+    price: {input: 0.08, output: 0.35}
+    as_of: "2026-09-27"
+    source: null
+  "gpt-5-chat":
+    price: {input: 1.25, output: 10}
+    as_of: "2026-09-27"
+    source: null
+  "gpt-5.3-chat":
+    price: {input: 1.75, output: 14}
+    as_of: "2026-09-27"
+    source: null
+  "grok-4":
+    price: {input: 5.5, output: 27.5}
+    as_of: "2026-09-27"
+    source: null
+  "inclusionai/ling-3.0-flash":
+    price: {input: 0.021, output: 0.062}
+    aliases: ["lingdt-3.0-flash"]
+    as_of: null
+    source: null
+    note: "lingdt-3.0-flash: priced as its public base model, per its submitter (#241)."
+  "llama-4-maverick-17b-128e-instruct-fp8":
+    price: {input: 0.25, output: 1}
+    as_of: "2026-09-27"
+    source: null
+  "meta-llama/llama-3.3-70b-instruct":
+    price: {input: 0.1, output: 0.32}
+    aliases: ["meta-llama_llama-3.3-70b-instruct"]
+    as_of: "2026-09-27"
+    source: null
+  "meta-llama_llama-3.1-405b-instruct":
+    price: {input: 3.5, output: 3.5}
+    as_of: "2026-09-27"
+    source: null
+  "minimax/minimax-m3":
+    price: {input: 0.6, output: 2.4}
+    aliases: ["MiniMax-M3", "MiniMaxAI/MiniMax-M3"]
+    as_of: "2026-09-27"
+    source: null
+  "mistralai/ministral-3-14b-2512":
+    price: {input: 0.2, output: 0.2}
+    as_of: "2026-09-27"
+    source: null
+  "mistralai/ministral-3-3b-2512":
+    price: {input: 0.1, output: 0.1}
+    as_of: "2026-09-27"
+    source: null
+  "mistralai/ministral-3-8b-2512":
+    price: {input: 0.15, output: 0.15}
+    as_of: "2026-09-27"
+    source: null
+  "mistralai/ministral-3b-2512":
+    price: {input: 0.1, output: 0.1}
+    as_of: "2026-09-27"
+    source: null
+  "mistralai/ministral-8b-2512":
+    price: {input: 0.15, output: 0.15}
+    aliases: ["ministral-8b-latest"]
+    as_of: null
+    source: null
+  "mistralai_devstral-2512:free":
+    price: {input: 0.05, output: 0.22}
+    as_of: "2026-09-27"
+    source: null
+  "moonshotai/kimi-k2.5":
+    price: {input: 0.6, output: 3}
+    as_of: "2026-09-27"
+    source: null
+  "moonshotai/kimi-k2.6":
+    price: {input: 0.95, output: 4}
+    aliases: ["moonshotai/Kimi-K2.6"]
+    as_of: null
+    source: null
+  "moonshotai/kimi-k3":
+    price: {input: 3, output: 15}
+    as_of: null
+    source: null
+  "nvidia/nemotron-3-nano-30b-a3b":
+    price: {input: 0.06, output: 0.24}
+    aliases: ["nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B"]
+    as_of: null
+    source: null
+    note: "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B: same model, Hugging Face spelling."
+  "o4-mini":
+    price: {input: 1.1, output: 4.4}
+    aliases: ["o4-mini-2025-04-16"]
+    as_of: "2026-09-27"
+    source: null
+  "openai/gpt-4.1":
+    price: {input: 2, output: 8}
+    aliases: ["gpt-4.1"]
+    as_of: "2026-09-27"
+    source: null
+  "openai/gpt-4.1-mini":
+    price: {input: 0.4, output: 1.6}
+    aliases: ["gpt-4.1-mini"]
+    as_of: "2026-09-27"
+    source: null
+  "openai/gpt-4.1-nano":
+    price: {input: 0.1, output: 0.4}
+    aliases: ["gpt-4.1-nano"]
+    as_of: "2026-09-27"
+    source: null
+  "openai/gpt-4o":
+    price: {input: 2.5, output: 10}
+    aliases: ["gpt-4o"]
+    as_of: "2026-09-27"
+    source: null
+  "openai/gpt-4o-mini":
+    price: {input: 0.15, output: 0.6}
+    aliases: ["gpt-4o-mini"]
+    as_of: "2026-09-27"
+    source: null
+  "openai/gpt-5":
+    price: {input: 1.25, output: 10}
+    aliases: ["gpt-5"]
+    as_of: "2026-09-27"
+    source: null
+  "openai/gpt-5-mini":
+    price: {input: 0.25, output: 2}
+    aliases: ["gpt-5-mini"]
+    as_of: "2026-09-27"
+    source: null
+  "openai/gpt-5-nano":
+    price: {input: 0.05, output: 0.4}
+    aliases: ["gpt-5-nano", "gpt-5-nano-2025-08-07"]
+    as_of: "2026-09-27"
+    source: null
+  "openai/gpt-5.1":
+    price: {input: 1.25, output: 10}
+    aliases: ["gpt-5.1", "gpt-5.1-None"]
+    as_of: "2026-09-27"
+    source: null
+  "openai/gpt-5.2":
+    price: {input: 1.75, output: 14}
+    aliases: ["gpt-5.2"]
+    as_of: "2026-09-27"
+    source: null
+  "openai/gpt-5.2-chat":
+    price: {input: 1.75, output: 14}
+    aliases: ["gpt-5.2-chat"]
+    as_of: "2026-09-27"
+    source: null
+  "openai/gpt-5.4":
+    price: {input: 2.5, output: 15}
+    aliases: ["gpt-5.4"]
+    as_of: "2026-09-27"
+    source: null
+  "openai/gpt-5.4-mini":
+    price: {input: 0.4, output: 1.6}
+    aliases: ["gpt-5.4-mini"]
+    as_of: "2026-09-27"
+    source: null
+  "openai/gpt-5.4-nano":
+    price: {input: 0.2, output: 1.25}
+    aliases: ["gpt-5.4-nano"]
+    as_of: "2026-09-27"
+    source: null
+  "openai/gpt-5.5":
+    price: {input: 5, output: 30}
+    aliases: ["gpt-5.5", "gpt-5.5-2026-04-23"]
+    as_of: "2026-09-27"
+    source: null
+  "openai/gpt-5.6-luna":
+    price: {input: 0.2, output: 1.2}
+    aliases: ["gpt-5.6-luna"]
+    as_of: null
+    source: null
+  "openai/gpt-5.6-sol":
+    price: {input: 4, output: 20}
+    as_of: null
+    source: null
+  "openai/gpt-5.6-terra":
+    price: {input: 2, output: 12}
+    as_of: null
+    source: null
+  "openai/gpt-6-luna":
+    price: {input: 0.1, output: 0.5}
+    as_of: null
+    source: null
+  "openai/gpt-6-sol":
+    price: {input: 2, output: 10}
+    as_of: null
+    source: null
+  "openai/gpt-6.1-sol":
+    price: {input: 2, output: 10}
+    as_of: null
+    source: null
+  "openai/gpt-oss-120b":
+    price: {input: 0.15, output: 0.6}
+    aliases: ["gpt-oss-120b", "openai_gpt-oss-120b"]
+    as_of: "2026-09-27"
+    source: null
+  "openai/gpt-oss-20b":
+    price: {input: 0.03, output: 0.13}
+    aliases: ["gpt-oss-20b"]
+    as_of: "2026-09-27"
+    source: null
+  "qwen/qwen3-235b-a22b-2507":
+    price: {input: 0.071, output: 0.1}
+    aliases: ["alibaba/qwen3-235b-a22b-instruct-2507", "qwen3-235b-a22b-instruct-2507", "qwen_qwen3-235b-a22b-2507"]
+    as_of: "2026-09-27"
+    source: null
+    note: "Unified spellings; previous prices: qwen3-235b-a22b-instruct-2507 0.5/2. alibaba/qwen3-235b-a22b-instruct-2507: same model, Alibaba's spelling."
+  "qwen/qwen3-30b-a3b-instruct-2507":
+    price: {input: 0.15, output: 0.6}
+    aliases: ["alibaba/qwen3-30b-a3b-instruct-2507", "qwen3-30b-a3b-instruct-2507"]
+    as_of: "2026-09-27"
+    source: null
+    note: "Unified spellings; previous prices: qwen/qwen3-30b-a3b-instruct-2507 0.08/0.33."
+  "qwen/qwen3-coder":
+    price: {input: 0.22, output: 0.95}
+    aliases: ["qwen_qwen3-coder"]
+    as_of: "2026-09-27"
+    source: null
+  "qwen/qwen3-coder-30b-a3b-instruct":
+    price: {input: 0.07, output: 0.27}
+    as_of: "2026-09-27"
+    source: null
+  "qwen/qwen3-coder-next":
+    price: {input: 0.07, output: 0.3}
+    aliases: ["Qwen/Qwen3-Coder-Next"]
+    as_of: "2026-09-27"
+    source: null
+  "qwen/qwen3-next-80b-a3b-instruct":
+    price: {input: 0.09, output: 1.1}
+    as_of: "2026-09-27"
+    source: null
+  "qwen/qwen3-vl-235b-a22b-instruct":
+    price: {input: 0.2, output: 1.2}
+    aliases: ["qwen_qwen3-vl-235b-a22b-instruct"]
+    as_of: "2026-09-27"
+    source: null
+  "qwen/qwen3.5-9b":
+    price: {input: 0.05, output: 0.15}
+    as_of: "2026-09-27"
+    source: null
+  "qwen/qwen3.5-flash-02-23":
+    price: {input: 0.065, output: 0.26}
+    as_of: "2026-09-27"
+    source: null
+  "qwen_qwen3-vl-235b-a22b-thinking":
+    price: {input: 0.3, output: 1.2}
+    as_of: "2026-09-27"
+    source: null
+  "qwen_qwen3-vl-32b-instruct":
+    price: {input: 0.5, output: 1.5}
+    as_of: "2026-09-27"
+    source: null
+  "test":
+    price: {input: 1, output: 2}
+    as_of: "2026-09-27"
+    source: null
+  "THUDM/GLM-4-9B-0414":
+    price: {input: 0.05, output: 0.61}
+    as_of: "2026-09-27"
+    source: null
+  "upstage/solar-mini":
+    price: {input: 0.15, output: 0.15}
+    as_of: null
+    source: null
+  "upstage/solar-pro-2":
+    price: {input: 0.15, output: 0.6}
+    as_of: null
+    source: null
+  "upstage/solar-pro3":
+    price: {input: 0.15, output: 0.6}
+    as_of: null
+    source: null
+  "upstage/solar-pro4":
+    price: {input: 0.3, output: 1.2}
+    as_of: null
+    source: null
+  "x-ai/grok-4.1-fast":
+    price: {input: 0.2, output: 0.5}
+    aliases: ["grok-4-1-fast-reasoning", "x-ai_grok-4.1-fast"]
+    as_of: "2026-09-27"
+    source: null
+    note: "grok-4-1-fast-reasoning: retired from OpenRouter; kept at its last listed price."
+  "x-ai/grok-4.3":
+    price: {input: 1.25, output: 2.5}
+    aliases: ["grok-4.3"]
+    as_of: "2026-09-27"
+    source: null
+  "x-ai_grok-code-fast-1":
+    price: {input: 0.2, output: 1.5}
+    as_of: "2026-09-27"
+    source: null
+  "xiaomi/mimo-v2.5":
+    price: {input: 0.14, output: 0.28}
+    as_of: "2026-09-27"
+    source: null
+  "xiaomi_mimo-v2-flash:free":
+    price: {input: 0.1, output: 0.3}
+    as_of: "2026-09-27"
+    source: null
+  "z-ai/glm-4.5-air":
+    price: {input: 0.1, output: 0.3}
+    aliases: ["glm-4.5-air"]
+    as_of: "2026-09-27"
+    source: null
+  "z-ai/glm-4.6":
+    price: {input: 0.28, output: 1.12}
+    aliases: ["glm-4.6"]
+    as_of: "2026-09-27"
+    source: null
+  "z-ai/glm-4.7":
+    price: {input: 0.4, output: 1.5}
+    aliases: ["z-ai_glm-4.7"]
+    as_of: "2026-09-27"
+    source: null
+  "z-ai/glm-5":
+    price: {input: 1, output: 3.2}
+    as_of: "2026-09-27"
+    source: null
+  "z-ai/glm-5.3":
+    price: {input: 0.079, output: 1.4}
+    aliases: ["z-ai/glm-5.3@high", "z-ai/glm-5.3@low"]
+    as_of: null
+    source: null
+  "z-ai/glm-5.3-flash":
+    price: {input: 0.041, output: 0.077}
+    aliases: ["glm-5.3-flash", "z-ai/glm-5.3-flash@default"]
+    as_of: null
+    source: null
+    note: "Unified spellings; previous prices: glm-5.3-flash 0.15/0.5; z-ai/glm-5.3-flash 0.15/0.5."
diff --git a/router_inference/check_config_prediction_files.py b/router_inference/check_config_prediction_files.py
index f4d09823..c1c7857d 100644
--- a/router_inference/check_config_prediction_files.py
+++ b/router_inference/check_config_prediction_files.py
@@ -176,49 +176,46 @@ def check_model_costs(
     for model in config_models:
         used_models.add(model)
 
-    # Check each model has cost configuration
+    # Check each model has a price profile (exact id or alias match only; see
+    # model_cost/model_profiles.yaml). A model without one is not rejected: the
+    # evaluator charges it the highest price in the table, so we warn loudly.
     missing_costs = []
-    for model_name in used_models:
+    for model_name in sorted(used_models):
         try:
             # Convert to universal name for cost lookup
             universal_name = model_manager.get_universal_name(model_name)
+        except Exception:
+            universal_name = model_name
 
-            # Remove _batch suffix if present
-            cost_lookup_name = universal_name
-            if universal_name.endswith("_batch"):
-                cost_lookup_name = universal_name[:-6]
+        # Remove _batch suffix if present
+        cost_lookup_name = universal_name
+        if universal_name.endswith("_batch"):
+            cost_lookup_name = universal_name[:-6]
 
-            # Check if cost exists (exact match or partial match)
-            has_cost = False
-            if cost_lookup_name in cost_config:
-                has_cost = True
-            else:
-                # Try partial matches
-                for config_name in cost_config.keys():
-                    if (
-                        config_name in cost_lookup_name
-                        or cost_lookup_name in config_name
-                    ):
-                        has_cost = True
-                        break
-
-            if not has_cost:
-                missing_costs.append(f"{model_name} (universal: {cost_lookup_name})")
-        except Exception as e:
-            # If we can't convert, try original name
-            if model_name not in cost_config:
-                missing_costs.append(f"{model_name} (conversion failed: {str(e)})")
+        if cost_lookup_name not in cost_config:
+            missing_costs.append(f"{model_name} (looked up as: {cost_lookup_name})")
 
     if missing_costs:
-        errors.append(f"Missing cost configuration for {len(missing_costs)} model(s):")
+        max_in = max(
+            float(v.get("input_token_price_per_million", 0.0))
+            for v in cost_config.values()
+        )
+        max_out = max(
+            float(v.get("output_token_price_per_million", 0.0))
+            for v in cost_config.values()
+        )
+        errors.append(f"No price profile for {len(missing_costs)} model(s):")
         for model in missing_costs:
             errors.append(f"  - {model}")
         errors.append(
-            "\nPlease add cost configuration to model_cost/model_cost.json or update "
-            "your router config to use models with existing cost configurations."
+            f"These rows will be charged the maximum price (${max_in}/${max_out} per "
+            "1M tokens). To avoid that, add a profile (with a source link) to "
+            "model_cost/model_profiles.yaml and run "
+            "`python scripts/pricing/build_model_cost.py`."
         )
 
-    return len(missing_costs) == 0, errors
+    # Missing prices are a warning, not a failure.
+    return True, errors
 
 
 # Model slugs that upstream providers have retired and silently redirect to a
@@ -659,7 +656,10 @@ def main():
     try:
         if predictions is not None and config is not None:
             cost_valid, cost_errors = check_model_costs(predictions, config)
-            if cost_valid:
+            if cost_valid and cost_errors:
+                for warning in cost_errors:
+                    print(f"  ⚠ {warning}")
+            elif cost_valid:
                 cost_config = load_cost_config()
                 print(
                     f"✓ All models have cost configurations ({len(cost_config)} models in cost file)"
diff --git a/scripts/pricing/build_model_cost.py b/scripts/pricing/build_model_cost.py
new file mode 100644
index 00000000..dcbd3c50
--- /dev/null
+++ b/scripts/pricing/build_model_cost.py
@@ -0,0 +1,124 @@
+# SPDX-FileCopyrightText: Copyright contributors to the RouterArena project
+# SPDX-License-Identifier: Apache-2.0
+
+"""Generate ``model_cost/model_cost.json`` from ``model_cost/model_profiles.yaml``.
+
+``model_profiles.yaml`` is the source of truth for model prices: one profile per
+model, with optional aliases (other spellings of the same model). The evaluator
+and the submission checks still read ``model_cost.json``, so this script expands
+every profile into one JSON entry for its id and one for each alias, all at the
+profile's price.
+
+Usage:
+    python scripts/pricing/build_model_cost.py          # rewrite model_cost.json
+    python scripts/pricing/build_model_cost.py --check  # exit 1 if it is out of date
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+import yaml
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+PROFILES_PATH = REPO_ROOT / "model_cost" / "model_profiles.yaml"
+COST_PATH = REPO_ROOT / "model_cost" / "model_cost.json"
+
+
+def load_profiles(path: Path = PROFILES_PATH) -> Dict[str, Dict[str, Any]]:
+    """Load and validate the profile file. Raises ValueError on a bad entry."""
+    with open(path, "r", encoding="utf-8") as f:
+        data = yaml.safe_load(f) or {}
+    models = data.get("models")
+    if not isinstance(models, dict) or not models:
+        raise ValueError(f"{path}: expected a non-empty 'models' mapping")
+
+    seen: Dict[str, str] = {}
+    errors: List[str] = []
+    for model_id, profile in models.items():
+        if not isinstance(profile, dict):
+            errors.append(f"{model_id}: profile must be a mapping")
+            continue
+        price = profile.get("price") or {}
+        for side in ("input", "output"):
+            value = price.get(side)
+            if (
+                not isinstance(value, (int, float))
+                or isinstance(value, bool)
+                or value < 0
+            ):
+                errors.append(f"{model_id}: price.{side} must be a number >= 0")
+        aliases = profile.get("aliases") or []
+        if not isinstance(aliases, list) or not all(
+            isinstance(a, str) for a in aliases
+        ):
+            errors.append(f"{model_id}: aliases must be a list of strings")
+            aliases = []
+        for name in [model_id, *aliases]:
+            if name in seen:
+                errors.append(
+                    f"{name!r} is used by both {seen[name]!r} and {model_id!r}"
+                )
+            seen[name] = model_id
+    if errors:
+        raise ValueError(f"{path}: invalid profiles:\n  " + "\n  ".join(errors))
+    return models
+
+
+def build_cost_table(models: Dict[str, Dict[str, Any]]) -> Dict[str, Dict[str, Any]]:
+    """Expand profiles into the flat ``model_cost.json`` mapping."""
+    table: Dict[str, Dict[str, Any]] = {}
+    for model_id, profile in models.items():
+        entry = {
+            "input_token_price_per_million": profile["price"]["input"],
+            "output_token_price_per_million": profile["price"]["output"],
+        }
+        for name in [model_id, *(profile.get("aliases") or [])]:
+            table[name] = dict(entry)
+    return table
+
+
+def render(table: Dict[str, Dict[str, Any]]) -> str:
+    return json.dumps(table, indent=2, ensure_ascii=False) + "\n"
+
+
+def main(argv: Optional[List[str]] = None) -> int:
+    parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+    parser.add_argument(
+        "--check",
+        action="store_true",
+        help="Do not write; exit 1 if model_cost.json differs from the profiles.",
+    )
+    args = parser.parse_args(argv)
+
+    try:
+        expected = render(build_cost_table(load_profiles()))
+    except ValueError as e:
+        print(e, file=sys.stderr)
+        return 1
+
+    current = COST_PATH.read_text(encoding="utf-8") if COST_PATH.exists() else ""
+    if args.check:
+        if current != expected:
+            print(
+                "model_cost/model_cost.json is out of date with model_profiles.yaml. "
+                "Run: python scripts/pricing/build_model_cost.py",
+                file=sys.stderr,
+            )
+            return 1
+        print("model_cost.json is up to date.")
+        return 0
+
+    COST_PATH.write_text(expected, encoding="utf-8")
+    print(
+        f"Wrote {COST_PATH.relative_to(REPO_ROOT)} ({len(json.loads(expected))} entries)."
+    )
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/tests/test_pricing.py b/tests/test_pricing.py
new file mode 100644
index 00000000..f6ceb848
--- /dev/null
+++ b/tests/test_pricing.py
@@ -0,0 +1,93 @@
+# SPDX-FileCopyrightText: Copyright contributors to the RouterArena project
+# SPDX-License-Identifier: Apache-2.0
+
+"""Tests for model price profiles and the evaluator's price lookup."""
+
+import json
+import sys
+from pathlib import Path
+
+import pytest
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(REPO_ROOT))
+sys.path.insert(0, str(REPO_ROOT / "llm_evaluation"))
+sys.path.insert(0, str(REPO_ROOT / "scripts" / "pricing"))
+
+import build_model_cost  # noqa: E402
+from evaluate_models import ModelEvaluator  # noqa: E402
+
+USAGE = {
+    "input_tokens": 1_000_000,
+    "output_tokens": 1_000_000,
+    "total_tokens": 2_000_000,
+}
+
+
+@pytest.fixture(scope="module")
+def evaluator():
+    return ModelEvaluator(cached_results_dir=str(REPO_ROOT / "cached_results"))
+
+
+def test_model_cost_json_is_generated_from_profiles():
+    profiles = build_model_cost.load_profiles()
+    expected = build_model_cost.render(build_model_cost.build_cost_table(profiles))
+    actual = (REPO_ROOT / "model_cost" / "model_cost.json").read_text(encoding="utf-8")
+    assert actual == expected, "run: python scripts/pricing/build_model_cost.py"
+
+
+def test_every_name_is_unique_across_ids_and_aliases(tmp_path):
+    bad = tmp_path / "profiles.yaml"
+    bad.write_text(
+        "models:\n"
+        "  a: {price: {input: 1, output: 2}, aliases: [b]}\n"
+        "  b: {price: {input: 1, output: 2}}\n"
+    )
+    with pytest.raises(ValueError, match="used by both"):
+        build_model_cost.load_profiles(bad)
+
+
+def test_negative_or_missing_price_is_rejected(tmp_path):
+    bad = tmp_path / "profiles.yaml"
+    bad.write_text("models:\n  a: {price: {input: -1}}\n")
+    with pytest.raises(ValueError, match="price.input"):
+        build_model_cost.load_profiles(bad)
+
+
+def test_alias_is_charged_the_profile_price(evaluator):
+    # Lynkr names this model "openai/gpt-oss-120b"; its profile id is "gpt-oss-120b".
+    assert evaluator.has_price("openai/gpt-oss-120b")
+    assert evaluator.calculate_inference_cost(
+        "openai/gpt-oss-120b", USAGE
+    ) == pytest.approx(evaluator.calculate_inference_cost("gpt-oss-120b", USAGE))
+
+
+def test_no_substring_matching(evaluator):
+    # Used to match "gpt-oss-120b" because that key is a substring of the name.
+    assert not evaluator.has_price("gpt-oss-120b-some-finetune")
+    assert not evaluator.has_price("gpt")
+
+
+def test_unmatched_model_is_charged_the_maximum_price(evaluator):
+    max_price = evaluator.max_price_info()
+    table = json.loads((REPO_ROOT / "model_cost" / "model_cost.json").read_text())
+    assert max_price["input_token_price_per_million"] == max(
+        v["input_token_price_per_million"] for v in table.values()
+    )
+    assert max_price["output_token_price_per_million"] == max(
+        v["output_token_price_per_million"] for v in table.values()
+    )
+    cost = evaluator.calculate_inference_cost("not-a-registered-model", USAGE)
+    assert cost == pytest.approx(
+        max_price["input_token_price_per_million"]
+        + max_price["output_token_price_per_million"]
+    )
+    assert evaluator.unpriced_models.get("not-a-registered-model", 0) >= 1
+
+
+def test_unmatched_model_is_never_cheaper_than_a_registered_one(evaluator):
+    table = json.loads((REPO_ROOT / "model_cost" / "model_cost.json").read_text())
+    unmatched = evaluator.calculate_inference_cost("not-a-registered-model", USAGE)
+    assert all(
+        evaluator.calculate_inference_cost(name, USAGE) <= unmatched for name in table
+    )