diff --git a/docs/case_studies/deep-research-bench-pareto-analysis/notebook.ipynb b/docs/case_studies/deep-research-bench-pareto-analysis/notebook.ipynb new file mode 100644 index 00000000..ce6305c7 --- /dev/null +++ b/docs/case_studies/deep-research-bench-pareto-analysis/notebook.ipynb @@ -0,0 +1,719 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Understanding `agent_map` Effort Levels\n", + "\n", + "Every call to [`agent_map`](/reference/agent-map) dispatches an AI agent per row of your DataFrame. The `effort_level` parameter controls **what kind of agent** runs and **which model** powers it:\n", + "\n", + "| Effort | Model | Iterations | Web Research | What it does |\n", + "|--------|-------|-----------|--------------|-------------------|\n", + "| `LOW` (default) | Gemini 3 Flash (minimal) | 0 | No | Single LLM call — quick classification or extraction |\n", + "| `MEDIUM` | Gemini 3 Flash (low) | up to 5 | Yes | Agentic research — searches the web, reads pages, synthesises |\n", + "| `HIGH` | Claude 4.6 Opus (low) | up to 10 | Yes | Deep research — more iterations, stronger model |\n", + "\n", + "`LOW` is fundamentally different from the other two: it's a single model call with no tool use, no web browsing, and no iterative reasoning. It's the right choice when you just need an LLM to look at a row and produce a quick answer.\n", + "\n", + "`MEDIUM` and `HIGH` run full research agents that search, read, and cross-reference sources. For these, model selection matters a lot — and we choose models based on their position on the **Pareto frontier** of accuracy, cost, and speed. This notebook shows how." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Fetch benchmark data\n", + "\n", + "The [Deep Research Bench](https://evals.futuresearch.ai) (DRB) evaluates models on agentic web-research tasks: finding datasets, compiling numbers, validating claims, and other information-retrieval-heavy work. This is exactly what `MEDIUM` and `HIGH` effort agents do, which makes DRB the right benchmark for picking those models.\n", + "\n", + "(DRB scores are less meaningful for `LOW`, since that effort level doesn't do any research — it's just a single LLM call.)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 22 model configurations from Deep Research Bench\n" + ] + }, + { + "data": { + "text/html": [ + "
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model_nameaverage_scoretotal_runsaverage_spendaverage_runtime_seconds
0Claude 4.6 Opus (high)0.5500083350.552964460.783344
1Claude 4.5 Opus (low)0.5485093290.312314412.932676
2Claude 4.5 Opus (high)0.5478843370.457780375.158008
3Claude 4.6 Opus (low)0.5308573300.242862158.676590
4Gemini 3 Flash (minimal)0.5036443380.102952680.680925
5Gemini 3 Flash (low)0.4993143300.050674389.948922
6GPT-5 (low)0.4963113300.251314525.672959
7GPT-5 (minimal)0.4887443290.235946502.485720
8GPT-5 (medium)0.4856942990.345617468.550553
9Claude 4.1 Opus (medium)0.4830283291.203487246.791378
10GPT-5 (high)0.4814043270.388968582.383317
11Gemini 3 Flash (high)0.4786043380.141666630.898910
12Claude 4.5 Sonnet (low)0.4751722940.356755625.251226
13Grok 40.4725583200.485631726.129258
14Claude 4 Opus (medium)0.4679303291.635857284.182346
15Claude 4 Sonnet (medium)0.4655193290.454494280.194073
16Gemini 3 Pro (low)0.462971330NaN392.279528
17Claude 4.5 Haiku (low)0.4545023290.098306431.052201
18o30.4521553290.118776240.988206
19GPT-5.1 (low)0.4279023270.039637271.585667
20Gemini 2.5 Pro (dynamic)0.4151923100.110789240.193460
21GPT-5.2 (low)0.4111993220.261318483.193635
\n", + "
" + ], + "text/plain": [ + " model_name average_score total_runs average_spend \\\n", + "0 Claude 4.6 Opus (high) 0.550008 335 0.552964 \n", + "1 Claude 4.5 Opus (low) 0.548509 329 0.312314 \n", + "2 Claude 4.5 Opus (high) 0.547884 337 0.457780 \n", + "3 Claude 4.6 Opus (low) 0.530857 330 0.242862 \n", + "4 Gemini 3 Flash (minimal) 0.503644 338 0.102952 \n", + "5 Gemini 3 Flash (low) 0.499314 330 0.050674 \n", + "6 GPT-5 (low) 0.496311 330 0.251314 \n", + "7 GPT-5 (minimal) 0.488744 329 0.235946 \n", + "8 GPT-5 (medium) 0.485694 299 0.345617 \n", + "9 Claude 4.1 Opus (medium) 0.483028 329 1.203487 \n", + "10 GPT-5 (high) 0.481404 327 0.388968 \n", + "11 Gemini 3 Flash (high) 0.478604 338 0.141666 \n", + "12 Claude 4.5 Sonnet (low) 0.475172 294 0.356755 \n", + "13 Grok 4 0.472558 320 0.485631 \n", + "14 Claude 4 Opus (medium) 0.467930 329 1.635857 \n", + "15 Claude 4 Sonnet (medium) 0.465519 329 0.454494 \n", + "16 Gemini 3 Pro (low) 0.462971 330 NaN \n", + "17 Claude 4.5 Haiku (low) 0.454502 329 0.098306 \n", + "18 o3 0.452155 329 0.118776 \n", + "19 GPT-5.1 (low) 0.427902 327 0.039637 \n", + "20 Gemini 2.5 Pro (dynamic) 0.415192 310 0.110789 \n", + "21 GPT-5.2 (low) 0.411199 322 0.261318 \n", + "\n", + " average_runtime_seconds \n", + "0 460.783344 \n", + "1 412.932676 \n", + "2 375.158008 \n", + "3 158.676590 \n", + "4 680.680925 \n", + "5 389.948922 \n", + "6 525.672959 \n", + "7 502.485720 \n", + "8 468.550553 \n", + "9 246.791378 \n", + "10 582.383317 \n", + "11 630.898910 \n", + "12 625.251226 \n", + "13 726.129258 \n", + "14 284.182346 \n", + "15 280.194073 \n", + "16 392.279528 \n", + "17 431.052201 \n", + "18 240.988206 \n", + "19 271.585667 \n", + "20 240.193460 \n", + "21 483.193635 " + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import requests\n", + "import pandas as pd\n", + "\n", + "url = \"https://rguraxphqescakvvzmju.supabase.co/rest/v1/rpc/get_average_scores_by_model\"\n", + "\n", + "PUBLIC_API_KEY = \"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6InJndXJheHBocWVzY2FrdnZ6bWp1Iiwicm9sZSI6ImFub24iLCJpYXQiOjE3NDAwNjkyODUsImV4cCI6MjA1NTY0NTI4NX0.8O7hbuvWSGGSuvjANtgrf3BihWP-aLdan3x48sc7kHk\"\n", + "\n", + "headers = {\n", + " \"apikey\": PUBLIC_API_KEY,\n", + " \"authorization\": f\"Bearer {PUBLIC_API_KEY}\",\n", + " \"content-type\": \"application/json\",\n", + " \"content-profile\": \"public\",\n", + "}\n", + "\n", + "response = requests.post(url, headers=headers, json={\"min_num_of_distinct_instances\": 150})\n", + "response.raise_for_status()\n", + "\n", + "df = pd.DataFrame(response.json())\n", + "\n", + "# Friendly display names (same mapping used on evals.futuresearch.ai)\n", + "MODEL_NAME_MAP = {\n", + " \"GROK_4\": \"Grok 4\",\n", + " \"CLAUDE_3_7_SONNET_THINKING\": \"Claude 3.7 Sonnet (medium)\",\n", + " \"CLAUDE_4_SONNET_THINKING\": \"Claude 4 Sonnet (medium)\",\n", + " \"CLAUDE_4_OPUS_THINKING\": \"Claude 4 Opus (medium)\",\n", + " \"CLAUDE_4_1_OPUS_THINKING\": \"Claude 4.1 Opus (medium)\",\n", + " \"CLAUDE_4_5_OPUS_LOW\": \"Claude 4.5 Opus (low)\",\n", + " \"CLAUDE_4_6_OPUS_LOW\": \"Claude 4.6 Opus (low)\",\n", + " \"CLAUDE_4_6_OPUS_HIGH\": \"Claude 4.6 Opus (high)\",\n", + " \"CLAUDE_4_5_OPUS_HIGH\": \"Claude 4.5 Opus (high)\",\n", + " \"CLAUDE_4_5_OPUS_THINKING\": \"Claude 4.5 Opus (medium)\",\n", + " \"CLAUDE_4_5_SONNET_MINIMAL\": \"Claude 4.5 Sonnet (low)\",\n", + " \"CLAUDE_4_5_HAIKU_MINIMAL\": \"Claude 4.5 Haiku (low)\",\n", + " \"GEMINI_2_5_PRO\": \"Gemini 2.5 Pro (dynamic)\",\n", + " \"GEMINI_3_FLASH_MINIMAL\": \"Gemini 3 Flash (minimal)\",\n", + " \"GEMINI_3_FLASH_LOW\": \"Gemini 3 Flash (low)\",\n", + " \"GEMINI_3_FLASH_HIGH\": \"Gemini 3 Flash (high)\",\n", + " \"GEMINI_3_PREVIEW_LOW\": \"Gemini 3 Pro (low)\",\n", + " \"O3\": \"o3\",\n", + " \"GPT_5_MINIMAL\": \"GPT-5 (minimal)\",\n", + " \"GPT_5_LOW\": \"GPT-5 (low)\",\n", + " \"GPT_5\": \"GPT-5 (medium)\",\n", + " \"GPT_5_HIGH\": \"GPT-5 (high)\",\n", + " \"GPT_5_1_LOW\": \"GPT-5.1 (low)\",\n", + " \"GPT_5_2_LOW\": \"GPT-5.2 (low)\",\n", + "}\n", + "df[\"model_name\"] = df[\"model_name\"].map(lambda x: MODEL_NAME_MAP.get(x, x))\n", + "\n", + "print(f\"Loaded {len(df)} model configurations from Deep Research Bench\")\n", + "df.sort_values(\"average_score\", ascending=False).reset_index(drop=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Compute Pareto frontiers\n", + "\n", + "A model sits on the **Pareto frontier** when no other model is both cheaper (or faster) *and* more accurate. Everything off the frontier is strictly dominated.\n", + "\n", + "After computing the raw frontier we apply a pruning step: if moving to a cheaper/faster model only saves a tiny amount on the x-axis but sacrifices a disproportionate amount of accuracy, we drop it. This uses a normalised-slope threshold (the same approach used on the [DRB dashboard](https://evals.futuresearch.ai)) so the frontier traces a meaningful trade-off curve." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "def pareto_frontier(df, x_col, y_col, max_normalised_slope=4):\n", + " \"\"\"Compute and prune a Pareto frontier (lower x is better, higher y is better).\"\"\"\n", + " valid = df.dropna(subset=[x_col, y_col]).copy()\n", + " if valid.empty:\n", + " return pd.DataFrame(), pd.DataFrame()\n", + "\n", + " # Raw frontier: sort by x ascending, keep points that set a new best y\n", + " sorted_df = valid.sort_values(x_col)\n", + " raw_frontier = []\n", + " best_y = -np.inf\n", + " for _, row in sorted_df.iterrows():\n", + " if row[y_col] > best_y:\n", + " best_y = row[y_col]\n", + " raw_frontier.append(row)\n", + " raw_frontier_df = pd.DataFrame(raw_frontier)\n", + "\n", + " # Prune: walk from best score downward, drop points whose normalised\n", + " # score-loss / x-savings ratio exceeds the threshold\n", + " x_range = valid[x_col].max() - valid[x_col].min() or 1\n", + " y_range = valid[y_col].max() - valid[y_col].min() or 1\n", + "\n", + " descending = raw_frontier_df.sort_values(x_col, ascending=False)\n", + " kept = [descending.iloc[0]]\n", + " for i in range(1, len(descending)):\n", + " prev = kept[-1]\n", + " curr = descending.iloc[i]\n", + " dx = (prev[x_col] - curr[x_col]) / x_range\n", + " dy = (prev[y_col] - curr[y_col]) / y_range\n", + " if dx > 0 and dy / dx <= max_normalised_slope:\n", + " kept.append(curr)\n", + " pruned = pd.DataFrame(kept).sort_values(x_col)\n", + "\n", + " return valid, pruned" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Best accuracy per dollar\n", + "\n", + "The green rings mark the models used by everyrow's `MEDIUM` and `HIGH` effort levels. Both sit on or near the Pareto frontier — you get the best accuracy available at their price points." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from matplotlib.lines import Line2D\n", + "\n", + "EVERYROW_MODELS = {\n", + " \"Gemini 3 Flash (low)\": \"MEDIUM\",\n", + " \"Claude 4.6 Opus (low)\": \"HIGH\",\n", + "}\n", + "\n", + "\n", + "def plot_pareto(all_pts, frontier, x_col, y_col, x_label, y_label, title):\n", + " fig, ax = plt.subplots(figsize=(11, 7))\n", + "\n", + " frontier_names = set(frontier[\"model_name\"])\n", + " non_frontier = all_pts[~all_pts[\"model_name\"].isin(frontier_names)]\n", + "\n", + " # Non-frontier points\n", + " ax.scatter(\n", + " non_frontier[x_col], non_frontier[y_col],\n", + " color=\"#636EFA\", s=60, alpha=0.7, zorder=2,\n", + " )\n", + " # Frontier points + line\n", + " ax.plot(\n", + " frontier[x_col].values, frontier[y_col].values,\n", + " color=\"#EF553B\", linestyle=\"--\", linewidth=2, zorder=3,\n", + " )\n", + " ax.scatter(\n", + " frontier[x_col], frontier[y_col],\n", + " color=\"#EF553B\", s=90, marker=\"D\", zorder=4,\n", + " )\n", + "\n", + " # Highlight everyrow defaults\n", + " for model_name, effort in EVERYROW_MODELS.items():\n", + " row = all_pts[all_pts[\"model_name\"] == model_name]\n", + " if not row.empty:\n", + " ax.scatter(\n", + " row[x_col], row[y_col],\n", + " facecolors=\"none\", edgecolors=\"#2CA02C\", s=260, linewidths=2.5,\n", + " marker=\"o\", zorder=5,\n", + " )\n", + "\n", + " # Labels\n", + " x_min, x_max = all_pts[x_col].min(), all_pts[x_col].max()\n", + " x_range = x_max - x_min or 1\n", + " for _, row in all_pts.iterrows():\n", + " frac = (row[x_col] - x_min) / x_range\n", + " ha = \"left\" if frac < 0.25 else (\"right\" if frac > 0.75 else \"center\")\n", + " label = row[\"model_name\"]\n", + " if label in EVERYROW_MODELS:\n", + " label = f\"{label} [{EVERYROW_MODELS[label]}]\"\n", + " ax.annotate(\n", + " label, (row[x_col], row[y_col]),\n", + " fontsize=7.5, ha=ha, va=\"bottom\",\n", + " xytext=(0, 6), textcoords=\"offset points\",\n", + " bbox=dict(facecolor=\"white\", edgecolor=\"none\", alpha=0.7, pad=1),\n", + " )\n", + "\n", + " ax.set_xlabel(x_label)\n", + " ax.set_ylabel(y_label)\n", + " ax.set_title(title)\n", + " ax.grid(True, alpha=0.3)\n", + "\n", + " legend_elements = [\n", + " Line2D([0], [0], marker=\"o\", color=\"#636EFA\", linestyle=\"None\", markersize=7, label=\"Models\"),\n", + " Line2D([0], [0], marker=\"D\", color=\"#EF553B\", linestyle=\"--\", markersize=7, label=\"Pareto frontier\"),\n", + " Line2D([0], [0], marker=\"o\", color=\"#2CA02C\", linestyle=\"None\",\n", + " markersize=10, markerfacecolor=\"none\", markeredgewidth=2,\n", + " label=\"everyrow defaults (MEDIUM / HIGH)\"),\n", + " ]\n", + " ax.legend(handles=legend_elements, loc=\"lower right\")\n", + " fig.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "all_spend, frontier_spend = pareto_frontier(df, \"average_spend\", \"average_score\")\n", + "\n", + "plot_pareto(\n", + " all_spend, frontier_spend,\n", + " x_col=\"average_spend\", y_col=\"average_score\",\n", + " x_label=\"Average Cost ($)\", y_label=\"Average Score\",\n", + " title=\"Best Accuracy per Dollar on DRB\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Best accuracy per second\n", + "\n", + "When you're running `agent_map` over thousands of rows, wall-clock time matters as much as cost. Claude 4.6 Opus (low) — the `HIGH` default — is the fastest high-accuracy model on DRB, finishing in under 3 minutes on average.\n", + "\n", + "Caveats: \n", + "1. Wall-clock time is influenced by a few relatively arbitrary factors like which deployment is being used, our rate limits, TPM quotas, whether we happened to have a lot of traffic when evals were run, etc. So it should be considered to be somewhat noisy. Pay less attention to this than to cost.\n", + "2. Lower reasoning models may take less time to generate an response to a single query, but in some cases being less clever translates to having to use more tools and hence more iterations; so while it may look counterintuitive that some \"slow\" models (higher reasoning or Opus vs Sonnet) are faster, they may just make better use of their tool calls." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "all_time, frontier_time = pareto_frontier(df, \"average_runtime_seconds\", \"average_score\")\n", + "\n", + "plot_pareto(\n", + " all_time, frontier_time,\n", + " x_col=\"average_runtime_seconds\", y_col=\"average_score\",\n", + " x_label=\"Average Runtime (s)\", y_label=\"Average Score\",\n", + " title=\"Best Accuracy per Second on DRB\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Comparing the effort levels\n", + "\n", + "Let's pull out the numbers for the three default models side-by-side, alongside Claude 4.6 Opus (high) — the strongest model on DRB — to show what you'd gain (and pay) by overriding." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Effort LevelModelDRB ScoreAvg CostAvg Runtime
LOWGemini 3 Flash (minimal)0.504$0.103681s
MEDIUMGemini 3 Flash (low)0.499$0.051390s
HIGHClaude 4.6 Opus (low)0.531$0.243159s
(override)Claude 4.6 Opus (high)0.550$0.553461s
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "EFFORT_MODELS = [\n", + " (\"LOW\", \"Gemini 3 Flash (minimal)\"),\n", + " (\"MEDIUM\", \"Gemini 3 Flash (low)\"),\n", + " (\"HIGH\", \"Claude 4.6 Opus (low)\"),\n", + " (\"(override)\", \"Claude 4.6 Opus (high)\"),\n", + "]\n", + "\n", + "rows = []\n", + "for effort, model in EFFORT_MODELS:\n", + " match = df[df[\"model_name\"] == model]\n", + " if not match.empty:\n", + " r = match.iloc[0]\n", + " rows.append({\n", + " \"Effort Level\": effort,\n", + " \"Model\": model,\n", + " \"DRB Score\": f\"{r['average_score']:.3f}\",\n", + " \"Avg Cost\": f\"${r['average_spend']:.3f}\" if pd.notna(r.get(\"average_spend\")) else \"—\",\n", + " \"Avg Runtime\": f\"{r['average_runtime_seconds']:.0f}s\" if pd.notna(r.get(\"average_runtime_seconds\")) else \"—\",\n", + " })\n", + "\n", + "comparison = pd.DataFrame(rows)\n", + "comparison.style.hide(axis=\"index\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Choosing the right effort level\n", + "\n", + "**`LOW`** is the default, and it's the right choice for most tasks that don't require web research — classifying rows, extracting fields, reformatting data. It runs a single LLM call with no tool use, so it's fast and cheap. Because DRB measures agentic information retrieval, the DRB score for the `LOW` model isn't very meaningful here: in practice `LOW` doesn't do research at all.\n", + "\n", + "**`MEDIUM`** turns on the research agent. Gemini 3 Flash (low) sits on the cost Pareto frontier — it's the cheapest model that delivers strong research accuracy. Use this when you need agents to look things up on the web but want to keep costs down.\n", + "\n", + "**`HIGH`** uses Claude 4.6 Opus (low), which sits on both the cost and speed Pareto frontiers. It's the fastest high-accuracy model on DRB and delivers the best score-per-dollar among top-tier models. Use this when accuracy matters and you're willing to pay more per row.\n", + "\n", + "**Want the absolute best accuracy?** You can override the model directly:\n", + "\n", + "```python\n", + "from everyrow.ops import agent_map\n", + "from everyrow.task import LLM\n", + "\n", + "result = await agent_map(\n", + " task=\"Find each company's latest funding round\",\n", + " input=companies_df,\n", + " llm=LLM.CLAUDE_4_6_OPUS_HIGH,\n", + ")\n", + "```\n", + "\n", + "Claude 4.6 Opus (high) is the top-scoring model on DRB, but it costs roughly twice as much and takes about three times as long as the `HIGH` default. For most workloads the `HIGH` preset already captures the bulk of that accuracy at a fraction of the price — but the option is there when you need it.\n", + "\n", + "We re-run these benchmarks as new models launch, so the model behind each effort level may change over time. You always get the current best trade-off without changing your code." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}