✨ feat: support price cost calculating per model

src/aggregators/aggregate_results.py

@@ -17,6 +17,7 @@
 import sys
 sys.path.append(str(Path(__file__).parent.parent.parent))
 from src.errors import is_retryable_error
+from src.aggregators.pricing import compute_cost_usd
 
 
 def discover_tasks() -> Dict[str, List[str]]:
@@ -280,6 +281,12 @@ def get_token_counts(meta: Dict[str, Any]) -> Tuple[int, int, int]:
             avg_pass1 = 0.0
             std_pass1 = 0.0
 
+        # Compute per-run tokens and cost
+        per_run_input_tokens = total_input_tokens / runs_count if runs_count else 0
+        per_run_output_tokens = total_output_tokens / runs_count if runs_count else 0
+        model_for_pricing = actual_model_name or model
+        computed_per_run_cost = compute_cost_usd(model_for_pricing, per_run_input_tokens, per_run_output_tokens)
+
         overall_metrics = {
             "total_tasks": total_tasks,
             "total_agent_execution_time": total_agent_execution_time,
@@ -292,9 +299,9 @@ def get_token_counts(meta: Dict[str, Any]) -> Tuple[int, int, int]:
             "avg_output_tokens": round(avg_output_tokens, 4),
             "avg_total_tokens": round(avg_total_tokens, 4),
             "avg_turns": round(avg_turns, 4),
-            "per_run_input_tokens": total_input_tokens / runs_count if runs_count else 0,
-            "per_run_output_tokens": total_output_tokens / runs_count if runs_count else 0,
-            "per_run_cost": per_run_cost if per_run_cost is not None else None,
+            "per_run_input_tokens": per_run_input_tokens,
+            "per_run_output_tokens": per_run_output_tokens,
+            "per_run_cost": computed_per_run_cost if computed_per_run_cost is not None else (per_run_cost if per_run_cost is not None else None),
             "actual_model_name": actual_model_name or "",
             "pass@1": {
                 "avg": round(avg_pass1, 4),
@@ -386,6 +393,11 @@ def get_token_counts(meta: Dict[str, Any]) -> Tuple[int, int, int]:
                 s_mean = 0.0
                 s_std = 0.0
 
+            # Compute per-run tokens and cost for this service
+            s_per_run_input_tokens = s_total_input_tokens / runs_count if runs_count else 0
+            s_per_run_output_tokens = s_total_output_tokens / runs_count if runs_count else 0
+            s_computed_per_run_cost = compute_cost_usd(model_for_pricing, s_per_run_input_tokens, s_per_run_output_tokens)
+
             service_metrics = {
                 "total_tasks": service_total_tasks,
                 "total_agent_execution_time": s_total_agent_execution_time,
@@ -398,9 +410,9 @@ def get_token_counts(meta: Dict[str, Any]) -> Tuple[int, int, int]:
                 "avg_output_tokens": round(s_avg_output_tokens, 4),
                 "avg_total_tokens": round(s_avg_total_tokens, 4),
                 "avg_turns": round(s_avg_turns, 4),
-                "per_run_input_tokens": s_total_input_tokens / runs_count if runs_count else 0,
-                "per_run_output_tokens": s_total_output_tokens / runs_count if runs_count else 0,
-                "per_run_cost": per_run_cost if per_run_cost is not None else None,
+                "per_run_input_tokens": s_per_run_input_tokens,
+                "per_run_output_tokens": s_per_run_output_tokens,
+                "per_run_cost": s_computed_per_run_cost if s_computed_per_run_cost is not None else (per_run_cost if per_run_cost is not None else None),
                 "actual_model_name": actual_model_name or "",
                 "pass@1": {
                     "avg": round(s_mean, 4),
@@ -484,14 +496,67 @@ def generate_task_results(exp_dir: Path, complete_models: Dict, all_tasks: Dict)
                 for run_name, run_data in model_data[service].items():
                     if task in run_data:
                         meta = run_data[task]
+                        agent_time = float(meta.get("agent_execution_time", 0.0) or 0.0)
+                        token_usage = meta.get("token_usage", {}) or {}
+                        turn_count = int(meta.get("turn_count", 0) or 0)
+                        success = bool(meta.get("execution_result", {}).get("success", False))
                         model_task_data["runs"].append({
                             "run": run_name,
-                            "success": meta.get("execution_result", {}).get("success", False),
-                            "execution_time": meta.get("agent_execution_time", 0),
-                            "token_usage": meta.get("token_usage", {})
+                            "success": success,
+                            "execution_time": agent_time,
+                            "agent_execution_time": agent_time,
+                            "token_usage": token_usage,
+                            "turn_count": turn_count,
                         })
 
                 if model_task_data["runs"]:
+                    # Compute per-model summary across runs for this task
+                    runs_list = model_task_data["runs"]
+                    runs_count = len(runs_list)
+                    successful_runs = sum(1 for r in runs_list if r.get("success"))
+
+                    # Averages
+                    total_agent_time = sum(float(r.get("agent_execution_time", r.get("execution_time", 0.0)) or 0.0) for r in runs_list)
+                    avg_agent_time = round(total_agent_time / runs_count, 2)
+
+                    def _tok(r, key):
+                        tu = r.get("token_usage") or {}
+                        return int(tu.get(key, 0) or 0)
+
+                    total_input_tokens = 0
+                    total_output_tokens = 0
+                    total_total_tokens = 0
+                    for r in runs_list:
+                        in_tok = _tok(r, "input_tokens")
+                        out_tok = _tok(r, "output_tokens")
+                        ttl_tok = int((r.get("token_usage") or {}).get("total_tokens", in_tok + out_tok) or (in_tok + out_tok))
+                        total_input_tokens += in_tok
+                        total_output_tokens += out_tok
+                        total_total_tokens += ttl_tok
+
+                    avg_input_tokens = round(total_input_tokens / runs_count, 1)
+                    avg_output_tokens = round(total_output_tokens / runs_count, 1)
+                    avg_total_tokens = round(total_total_tokens / runs_count, 1)
+
+                    total_turns = sum(int(r.get("turn_count", 0) or 0) for r in runs_list)
+                    avg_turn_count = round(total_turns / runs_count, 2)
+
+                    summary_obj = {
+                        "total_runs": runs_count,
+                        "successful_runs": successful_runs,
+                        "avg_agent_execution_time": avg_agent_time,
+                        "avg_input_tokens": avg_input_tokens,
+                        "avg_output_tokens": avg_output_tokens,
+                        "avg_total_tokens": avg_total_tokens,
+                        "avg_turn_count": avg_turn_count,
+                    }
+
+                    # Include pass@k and pass^k only for multi-run models
+                    if runs_count > 1:
+                        summary_obj[f"pass@{runs_count}"] = 1.0 if successful_runs > 0 else 0.0
+                        summary_obj[f"pass^{runs_count}"] = 1.0 if successful_runs == runs_count else 0.0
+
+                    model_task_data["summary"] = summary_obj
                     task_data["models"][model] = model_task_data
 
             # Save task file
@@ -525,7 +590,9 @@ def render_section(title: str, section_data: Dict[str, Any]) -> List[str]:
         if include_k:
             header += f" Pass@{k} | Pass^{k} |"
             sep += "----------|----------|"
-        # Add Avg Turns and Avg Agent Time (s) at the end
+        # Add Per-Run Cost (USD) and Avg Agent Time (s) at the end
+        header += " Per-Run Cost (USD) |"
+        sep += "---------------------|"
         header += " Avg Agent Time (s) |"
         sep += "--------------------|"
 
@@ -542,6 +609,14 @@ def render_section(title: str, section_data: Dict[str, Any]) -> List[str]:
         for model, metrics in sorted_items:
             pass1_avg, pass1_std = get_pass1_avg_std(metrics)
             avg_time = float(metrics.get("avg_agent_execution_time", 0.0) or 0.0)
+            # Format per-run cost (up to 2 decimal places, trim trailing zeros)
+            cost_val = metrics.get("per_run_cost")
+            if isinstance(cost_val, (int, float)):
+                rounded_cost = round(float(cost_val), 2)
+                formatted_cost = f"{rounded_cost:.2f}".rstrip('0').rstrip('.')
+                cost_str = f"${formatted_cost}"
+            else:
+                cost_str = "/"
             row = (
                 f"| {model} | {metrics.get('total_tasks', 0)} | "
                 f"{pass1_avg * 100:.1f}% ± {pass1_std * 100:.1f}% |"
@@ -552,7 +627,8 @@ def render_section(title: str, section_data: Dict[str, Any]) -> List[str]:
                 else:
                     # Single-run models do not have pass@k or pass^k; show placeholders
                     row += " / | / |"
-            # Append avg agent time at the end
+            # Append cost and avg agent time at the end
+            row += f" {cost_str} |"
             row += f" {avg_time:.1f} |"
             lines_sec.append(row)
 

src/aggregators/pricing.py

@@ -0,0 +1,79 @@
+"""
+Pricing utilities for computing per-run cost from token usage.
+
+All prices are specified per 1,000,000 tokens (M tokens) in USD.
+"""
+
+from __future__ import annotations
+
+from typing import Dict, Optional
+
+
+# Price map keyed by canonical model name (lowercased)
+# Values are dicts with per-M token prices for input and output tokens
+MODEL_PRICES_PER_M: Dict[str, Dict[str, float]] = {
+    # Use exact actual_model_name keys (lowercased) provided by the user
+    # Anthropic
+    "claude-opus-4-1-20250805": {"input": 15.0, "output": 75.0},
+    "claude-sonnet-4-20250514": {"input": 3.0, "output": 15.0},
+
+    # DeepSeek
+    "deepseek-v3.1-non-think": {"input": 0.56, "output": 1.68},
+
+    # Google Gemini
+    "gemini-2.5-pro": {"input": 2.5, "output": 15.0},
+    "gemini-2.5-flash": {"input": 0.3, "output": 2.5},
+
+    # Z.AI
+    "glm-4.5": {"input": 0.33, "output": 1.32},
+
+    # OpenAI
+    "gpt-5-2025-08-07": {"input": 1.25, "output": 10.0},
+    "gpt-5-mini-2025-08-07": {"input": 0.25, "output": 2.0},
+    "gpt-5-nano-2025-08-07": {"input": 0.05, "output": 0.4},
+    "gpt-4.1-2025-04-14": {"input": 2.0, "output": 8.0},
+    "gpt-4.1-mini-2025-04-14": {"input": 0.4, "output": 1.6},
+    "gpt-4.1-nano-2025-04-14": {"input": 0.1, "output": 0.4},
+    "o3-2025-04-16": {"input": 2.0, "output": 8.0},
+    "o4-mini-2025-04-16": {"input": 1.1, "output": 4.4},
+    "gpt-oss-120b": {"input": 0.072, "output": 0.28},
+
+    # Qwen
+    "qwen3-coder-480b-a35b-instruct": {"input": 0.2, "output": 0.8},
+
+    # Xai
+    "grok-4-0709": {"input": 3.0, "output": 15.0},
+    "grok-code-fast-1": {"input": 0.2, "output": 1.5},
+
+    # Moonshot
+    "kimi-k2-0711-preview": {"input": 0.6, "output": 2.5},
+}
+
+
+def normalize_model_name(model_name: str) -> str:
+    """Normalize model name for pricing lookup.
+
+    Lowercases only.
+    """
+    return (model_name or "").strip().lower()
+
+
+def get_price_per_m(model_name: str) -> Optional[Dict[str, float]]:
+    """Return per-M token prices for given model, or None if unknown."""
+    key = normalize_model_name(model_name)
+    return MODEL_PRICES_PER_M.get(key)
+
+
+def compute_cost_usd(model_name: str, input_tokens: float, output_tokens: float) -> Optional[float]:
+    """Compute cost in USD given token usage and model pricing.
+
+    Prices are per 1,000,000 tokens. If pricing unknown, returns None.
+    """
+    prices = get_price_per_m(model_name)
+    if not prices:
+        return None
+    input_cost = (input_tokens / 1_000_000.0) * prices["input"]
+    output_cost = (output_tokens / 1_000_000.0) * prices["output"]
+    return float(round(input_cost + output_cost, 6))
+
+