Add thinking overhead multiplier for calculated tokens

- Add 'Thinking Overhead' UI input (default 1.21) for adjusting calculated token counts
- Apply overhead to completion_tokens and cache_creation_tokens when using 'Calculated' source
- Overhead compensates for tokenizer differences and message formatting overhead
- Fix cache calculation to include assistant responses in cache_creation
- Recalculate graphs when overhead value changes

app.py

@@ -9,9 +9,13 @@ import pandas as pd
 import plotly.express as px
 import plotly.graph_objects as go
 import requests
+import tiktoken
 
 from src.download_swebench_leaderboard import download_leaderboard
 
+# Tokenizer cache
+_tokenizer_cache = {}
+
 DATA_DIR = Path("data")
 TRAJS_DIR = DATA_DIR / "swebench_trajs"
 LEADERBOARD_CACHE = DATA_DIR / "swebench_leaderboard_latest.json"
@@ -21,6 +25,173 @@ LITELLM_PRICES_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/mod
 
 _litellm_prices_cache = None
 _trajectories_cache = {}
+_calculated_tokens_cache = {}
+
+
+def get_tokenizer(model_name: str):
+    """Get appropriate tokenizer for model. Returns (tokenizer_func, name)"""
+    global _tokenizer_cache
+    
+    model_lower = model_name.lower() if model_name else ""
+    
+    # Determine tokenizer type
+    if "gpt-4o" in model_lower or "o1" in model_lower or "o3" in model_lower:
+        tokenizer_name = "o200k_base"
+    elif "gpt" in model_lower or "claude" in model_lower or "anthropic" in model_lower:
+        tokenizer_name = "cl100k_base"
+    elif "gemini" in model_lower or "google" in model_lower:
+        # Gemini uses ~3.23 chars per token (calculated from actual API responses)
+        return lambda text: int(len(text) / 3.23), "gemini_approx"
+    else:
+        # Default to cl100k_base for unknown models
+        tokenizer_name = "cl100k_base"
+    
+    if tokenizer_name not in _tokenizer_cache:
+        _tokenizer_cache[tokenizer_name] = tiktoken.get_encoding(tokenizer_name)
+    
+    enc = _tokenizer_cache[tokenizer_name]
+    return lambda text: len(enc.encode(text)), tokenizer_name
+
+
+def calculate_tokens_from_trajectory(traj_path: Path, model_name: str) -> dict:
+    """
+    Calculate tokens from trajectory messages simulating API behavior.
+    
+    API counts prompt_tokens cumulatively for each call (full context each time).
+    With caching: cache_read = previous context, cache_creation = new content.
+    
+    Returns dict with:
+    - prompt_tokens: total input tokens (cumulative across all API calls)
+    - completion_tokens: total output tokens  
+    - cache_read_tokens: tokens read from cache
+    - cache_creation_tokens: tokens written to cache
+    - api_calls: number of assistant responses
+    """
+    with open(traj_path, "r", encoding="utf-8") as f:
+        data = json.load(f)
+    
+    messages = data.get("messages", [])
+    if not messages:
+        return {"prompt_tokens": 0, "completion_tokens": 0, "cache_read_tokens": 0, "cache_creation_tokens": 0, "api_calls": 0}
+    
+    count_tokens, _ = get_tokenizer(model_name)
+    
+    # Calculate tokens for each message
+    message_tokens = []
+    for msg in messages:
+        content = msg.get("content", "")
+        if isinstance(content, list):
+            content = json.dumps(content)
+        tokens = count_tokens(str(content))
+        message_tokens.append({
+            "role": msg.get("role", "user"),
+            "tokens": tokens
+        })
+    
+    # Simulate API behavior: each call sends full context
+    # LLM APIs cache full context including assistant responses
+    prompt_tokens = 0  # Cumulative prompt tokens across all API calls
+    completion_tokens = 0
+    cache_read_tokens = 0
+    cache_creation_tokens = 0
+    api_calls = 0
+    
+    context_so_far = 0  # Total tokens in context (including assistant responses)
+    cached_context = 0  # Tokens that are cached from previous API calls
+
+    for i, mt in enumerate(message_tokens):
+        if mt["role"] == "assistant":
+            completion_tokens += mt["tokens"]
+            api_calls += 1
+            context_so_far += mt["tokens"]
+        else:
+            context_so_far += mt["tokens"]
+
+            next_is_assistant = (i + 1 < len(message_tokens) and message_tokens[i + 1]["role"] == "assistant")
+            is_last = (i == len(message_tokens) - 1)
+
+            if next_is_assistant:
+                prompt_tokens += context_so_far
+                cache_read_tokens += cached_context
+
+                assistant_tokens = message_tokens[i + 1]["tokens"]
+                cache_creation_tokens += (context_so_far - cached_context) + assistant_tokens
+                cached_context = context_so_far + assistant_tokens
+            elif is_last:
+                prompt_tokens += context_so_far
+                cache_read_tokens += cached_context
+                cache_creation_tokens += context_so_far - cached_context
+    
+    return {
+        "prompt_tokens": prompt_tokens,
+        "completion_tokens": completion_tokens,
+        "cache_read_tokens": cache_read_tokens,
+        "cache_creation_tokens": cache_creation_tokens,
+        "api_calls": api_calls,
+    }
+
+
+def apply_thinking_overhead(df: pd.DataFrame, overhead: float) -> pd.DataFrame:
+    """Apply thinking overhead multiplier to completion tokens"""
+    if df.empty or overhead == 1.0:
+        return df
+
+    df = df.copy()
+    df["completion_tokens"] = (df["completion_tokens"] * overhead).astype(int)
+    df["total_tokens"] = df["prompt_tokens"] + df["completion_tokens"]
+    df["cache_creation_tokens"] = (df["cache_creation_tokens"] * overhead).astype(int)
+    return df
+
+
+def load_all_trajectories_calculated(folder: str) -> pd.DataFrame:
+    """Load trajectories with self-calculated token counts"""
+    global _calculated_tokens_cache
+    
+    cache_key = f"calculated_{folder}"
+    if cache_key in _calculated_tokens_cache:
+        return _calculated_tokens_cache[cache_key]
+    
+    output_dir = TRAJS_DIR / folder
+    
+    traj_files = list(output_dir.glob("*/*.traj.json"))
+    if not traj_files:
+        traj_files = list(output_dir.glob("*.traj.json"))
+    if not traj_files:
+        traj_files = list(output_dir.glob("*.json"))
+    
+    # Get model name from first trajectory
+    model_name = ""
+    if traj_files:
+        try:
+            with open(traj_files[0], "r") as f:
+                first_data = json.load(f)
+                config = first_data.get("info", {}).get("config", {}).get("model", {})
+                model_name = config.get("cost_calc_model_override", config.get("model_name", ""))
+        except Exception:
+            pass
+    
+    rows = []
+    for traj_path in traj_files:
+        try:
+            tokens = calculate_tokens_from_trajectory(traj_path, model_name)
+            
+            rows.append({
+                "instance_id": traj_path.stem.replace(".traj", ""),
+                "model_name": model_name,
+                "api_calls": tokens["api_calls"],
+                "instance_cost": 0,  # Will be calculated from prices
+                "prompt_tokens": tokens["prompt_tokens"],
+                "completion_tokens": tokens["completion_tokens"],
+                "total_tokens": tokens["prompt_tokens"] + tokens["completion_tokens"],
+                "cache_read_tokens": tokens["cache_read_tokens"],
+                "cache_creation_tokens": tokens["cache_creation_tokens"],
+            })
+        except Exception as e:
+            print(f"Error calculating tokens for {traj_path}: {e}")
+    
+    df = pd.DataFrame(rows)
+    _calculated_tokens_cache[cache_key] = df
+    return df
 
 
 def get_litellm_prices() -> dict:
@@ -306,6 +477,81 @@ def create_cost_by_type_chart(df: pd.DataFrame, input_price: float, cache_read_p
     return fig
 
 
+def create_token_charts(df: pd.DataFrame, input_price: float, cache_read_price: float, cache_creation_price: float, completion_price: float):
+    """Create only token-related charts (for source switching)"""
+    if df.empty:
+        return None, None, None
+
+    total_completion = df["completion_tokens"].sum()
+    total_cache_read = df["cache_read_tokens"].sum()
+    total_cache_creation = df["cache_creation_tokens"].sum()
+    df_temp = df.copy()
+    df_temp["uncached_input"] = (df_temp["prompt_tokens"] - df_temp["cache_read_tokens"] - df_temp["cache_creation_tokens"]).clip(lower=0)
+    total_uncached_input = df_temp["uncached_input"].sum()
+
+    token_data = pd.DataFrame({
+        "Token Type": ["Uncached Input", "Cache Read", "Cache Creation", "Completion"],
+        "Total Tokens": [total_uncached_input, total_cache_read, total_cache_creation, total_completion],
+    })
+
+    fig_tokens = px.bar(
+        token_data,
+        x="Token Type",
+        y="Total Tokens",
+        title="Total Tokens by Type",
+        color="Token Type",
+        color_discrete_sequence=["#EF553B", "#19D3F3", "#FFA15A", "#AB63FA"],
+    )
+    fig_tokens.update_layout(
+        xaxis_title="Token Type",
+        yaxis_title="Total Tokens",
+        showlegend=False,
+        margin=dict(l=40, r=20, t=40, b=40),
+    )
+    total_all = token_data["Total Tokens"].sum()
+    fig_tokens.add_annotation(
+        text=f"Total: {total_all:,.0f}",
+        xref="paper", yref="paper",
+        x=0.95, y=0.95, showarrow=False,
+        font=dict(size=12),
+    )
+
+    fig_tokens_cost = create_cost_by_type_chart(df, input_price, cache_read_price, cache_creation_price, completion_price)
+
+    # Stacked bar chart
+    df_sorted = df.sort_values("cache_read_tokens", ascending=False).reset_index(drop=True)
+    df_sorted["instance_idx"] = range(len(df_sorted))
+    df_sorted["uncached_input_tokens"] = (df_sorted["prompt_tokens"] - df_sorted["cache_read_tokens"] - df_sorted["cache_creation_tokens"]).clip(lower=0)
+
+    fig_stacked = go.Figure()
+    fig_stacked.add_trace(go.Bar(
+        name="Uncached Input", x=df_sorted["instance_idx"], y=df_sorted["uncached_input_tokens"],
+        marker_color="#EF553B", hovertemplate="Instance: %{x}<br>Uncached Input: %{y:,.0f}<extra></extra>",
+    ))
+    fig_stacked.add_trace(go.Bar(
+        name="Cache Read", x=df_sorted["instance_idx"], y=df_sorted["cache_read_tokens"],
+        marker_color="#19D3F3", hovertemplate="Instance: %{x}<br>Cache Read: %{y:,.0f}<extra></extra>",
+    ))
+    fig_stacked.add_trace(go.Bar(
+        name="Cache Creation", x=df_sorted["instance_idx"], y=df_sorted["cache_creation_tokens"],
+        marker_color="#FFA15A", hovertemplate="Instance: %{x}<br>Cache Creation: %{y:,.0f}<extra></extra>",
+    ))
+    fig_stacked.add_trace(go.Bar(
+        name="Completion", x=df_sorted["instance_idx"], y=df_sorted["completion_tokens"],
+        marker_color="#AB63FA", hovertemplate="Instance: %{x}<br>Completion: %{y:,.0f}<extra></extra>",
+    ))
+    fig_stacked.update_layout(
+        barmode="stack",
+        title="Billable Tokens per Instance (stacked)",
+        xaxis_title="Instance (sorted by cache read)",
+        yaxis_title="Tokens",
+        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
+        margin=dict(l=50, r=20, t=60, b=40),
+    )
+
+    return fig_tokens, fig_tokens_cost, fig_stacked
+
+
 def create_basic_histograms(df: pd.DataFrame, input_price: float, cache_read_price: float, cache_creation_price: float, completion_price: float):
     if df.empty:
         return None, None, None, None, None
@@ -674,6 +920,19 @@ def build_app():
                 price_cache_creation = gr.Number(label="💲 Cache Creation", value=0, precision=2)
                 price_completion = gr.Number(label="💲 Completion", value=0, precision=2)
 
+                gr.Markdown("---")
+                gr.Markdown("### 📊 Token Count Source")
+                token_source = gr.Radio(
+                    choices=["Metadata", "Calculated"],
+                    value="Metadata",
+                )
+                thinking_overhead = gr.Number(
+                    label="🧠 Thinking Overhead (multiplier)",
+                    value=1.21,
+                    precision=2,
+                    info="Applies to Calculated tokens",
+                )
+
         leaderboard_table.select(
             fn=on_row_select,
             inputs=[leaderboard_table],
@@ -686,11 +945,11 @@ def build_app():
             outputs=[download_status, analyze_btn],
         )
 
-        def load_and_analyze(folder, input_price, cache_read_price, cache_creation_price, completion_price):
+        def load_and_analyze(folder, input_price, cache_read_price, cache_creation_price, completion_price, source, overhead):
             empty_result = (
                 gr.update(visible=False),
                 None, None, None, None, None, None,
-                None,  # trajectories_state
+                None,
             )
 
             if not folder:
@@ -703,7 +962,12 @@ def build_app():
                 None,
             )
 
-            df = load_all_trajectories(folder)
+            if source == "Metadata":
+                df = load_all_trajectories(folder)
+            else:
+                df = load_all_trajectories_calculated(folder)
+                df = apply_thinking_overhead(df, overhead)
+
             if df.empty:
                 yield empty_result
                 return
@@ -716,12 +980,12 @@ def build_app():
             yield (
                 gr.update(visible=True),
                 fig_steps, fig_cost, fig_tokens, fig_tokens_cost, fig_stacked, fig_cost_breakdown,
-                df,  # save to state
+                df,
             )
 
         analyze_btn.click(
             fn=load_and_analyze,
-            inputs=[selected_folder, price_input, price_cache_read, price_cache_creation, price_completion],
+            inputs=[selected_folder, price_input, price_cache_read, price_cache_creation, price_completion, token_source, thinking_overhead],
             outputs=[
                 analysis_section,
                 plot_steps, plot_cost, plot_tokens, plot_tokens_cost, plot_stacked, plot_cost_breakdown,
@@ -744,6 +1008,39 @@ def build_app():
         price_cache_creation.change(fn=recalculate_costs, inputs=price_inputs, outputs=price_outputs)
         price_completion.change(fn=recalculate_costs, inputs=price_inputs, outputs=price_outputs)
 
+        def on_source_change(folder, input_price, cache_read_price, cache_creation_price, completion_price, source, overhead):
+            """Recalculate only token-dependent charts when source changes"""
+            if not folder:
+                return None, None, None, None
+
+            if source == "Metadata":
+                df = load_all_trajectories(folder)
+            else:
+                df = load_all_trajectories_calculated(folder)
+                df = apply_thinking_overhead(df, overhead)
+
+            if df.empty:
+                return None, None, None, None
+
+            fig_tokens, fig_tokens_cost, fig_stacked = create_token_charts(
+                df, input_price, cache_read_price, cache_creation_price, completion_price
+            )
+            fig_cost_breakdown = create_cost_breakdown(df, input_price, cache_read_price, cache_creation_price, completion_price)
+
+            return fig_tokens, fig_tokens_cost, fig_stacked, fig_cost_breakdown
+
+        token_source.change(
+            fn=on_source_change,
+            inputs=[selected_folder, price_input, price_cache_read, price_cache_creation, price_completion, token_source, thinking_overhead],
+            outputs=[plot_tokens, plot_tokens_cost, plot_stacked, plot_cost_breakdown],
+        )
+
+        thinking_overhead.change(
+            fn=on_source_change,
+            inputs=[selected_folder, price_input, price_cache_read, price_cache_creation, price_completion, token_source, thinking_overhead],
+            outputs=[plot_tokens, plot_tokens_cost, plot_stacked, plot_cost_breakdown],
+        )
+
     return app
 
 

pyproject.toml

@@ -11,6 +11,7 @@ dependencies = [
     "plotly>=5.18.0",
     "requests>=2.31.0",
     "python-dotenv>=1.0.0",
+    "tiktoken>=0.12.0",
 ]
 
 [project.optional-dependencies]

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 version = "2.32.5"
@@ -1330,6 +1437,7 @@ dependencies = [
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     { name = "python-dotenv" },
     { name = "requests" },
+    { name = "tiktoken" },
 ]
 
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     { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.8.0" },
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