uurtech · Oct 14, 2026 · Oct 14, 2026
diff --git a/README.md b/README.md
@@ -380,21 +380,21 @@ R@1k tok = answer found within the first 1,000 tokens of retrieved context (chun
 **RAG cost — 1,000 PDF files vs 1,000 JDF files.** Same pipeline, only the input format differs: chunks → embeddings → vector store → top-5 context → LLM. Tokens counted from the chunks each pipeline produces, dollars from published prices (`bench/prices.json`); the benchmark never calls a paid API.
 
 <!-- bench:cost:start -->
-| Per 1,000 documents | JDF · jdf chunk (section) | PDF · pdfplumber extract_text() · fixed 1000/200 |
+| Per 10,000 documents | JDF · jdf chunk (section) | PDF · pdfplumber extract_text() · fixed 1000/200 |
 |---|---:|---:|
 | Accuracy · answer in first 1,000 tokens (BAAI/bge-small-en-v1.5) | **96.9%** | 75.0% |
 | Accuracy · top-1 hit | **46.9%** | 34.9% |
-| Chunks | **8,000** | 6,249 |
-| Embedding tokens, initial index | **1,305,820** | 1,385,673 |
-| Embedding cost · OpenAI text-embedding-3-small | **$0.0261** | $0.0277 |
-| Local embedding time · bge-small-en-v1.5 (measured throughput) | **17.5 s** | 6.4 s |
-| Vector-store payload | **5.8 MB** | 5.6 MB |
-| Re-embed tokens when one paragraph changes in every document | **91,000** | 1,402,000 |
-| Re-index cost · OpenAI text-embedding-3-small | **$0.0018** | $0.0280 |
-| LLM input tokens per 1,000,000 queries (top-5 context) | **716,317,708** | 1,210,218,750 |
-| LLM input cost · Claude Sonnet 5 input | **$1,432.64** | $2,420.44 |
-
-1,000 files per format (24-document corpus cycled); tokens counted from each pipeline's chunks, embedding time measured on x86_64 + NVIDIA L40S, 2026-09-28. Other volumes (10,000 documents, 10M queries) are linear estimates, not measurements. Prices: [`bench/prices.json`](bench/prices.json). Method: [`bench/README.md`](bench/README.md).
+| Chunks | **80,000** | 62,499 |
+| Embedding tokens, initial index | **13,057,945** | 13,857,048 |
+| Embedding cost · OpenAI text-embedding-3-small | **$0.2612** | $0.2771 |
+| Local embedding time · bge-small-en-v1.5 (measured throughput) | **8.4 min** | 6.4 min |
+| Vector-store payload | **57.5 MB** | 55.9 MB |
+| Re-embed tokens when one paragraph changes in every document | **910,000** | 14,020,000 |
+| Re-index cost · OpenAI text-embedding-3-small | **$0.0182** | $0.2804 |
+| LLM input tokens per 10,000,000 queries (top-5 context) | **7,163,177,083** | 12,102,187,500 |
+| LLM input cost · Claude Sonnet 5 input | **$14,326.35** | $24,204.38 |
+
+10,000 files per format (24-document corpus cycled); tokens counted from each pipeline's chunks, embedding time measured on Apple M5, 2026-10-14. Other volumes are linear estimates, not measurements. Prices: [`bench/prices.json`](bench/prices.json). Method: [`bench/README.md`](bench/README.md).
 <!-- bench:cost:end -->
 
 Reproduce it — Python only, no Node toolchain:

diff --git a/bench/README.md b/bench/README.md
@@ -57,7 +57,7 @@ Nothing from the corpus leaves the machine. The cost benchmark never calls a pai
 
 **JDF-only properties.** Every committed chunk's `hash` is checked against `sha256(text)`; one paragraph of one document is edited and re-chunked ([`corpus/jdf-chunks.d01-edited.jsonl`](corpus/jdf-chunks.d01-edited.jsonl)) — the number of chunks whose hash changed is what `jdf embed --incremental` re-embeds. A PDF pipeline has no stable chunk identity, so any edit means re-extract, re-chunk, re-embed.
 
-**RAG cost** (`cost_bench.py`). The corpus is cycled to N files per format (default 1,000). Both sides run the same pipeline — chunks → embeddings → vector store → top-5 context → LLM — and only the input format differs; the PDF column is the PDF pipeline that scored best in the accuracy run. Counted: chunks and embedding tokens for the initial index (ceil(chars/4), both sides), vector-store payload, re-index tokens when one paragraph changes in every document (JDF: only hash-changed chunks; PDF: the whole document), LLM input tokens for 1M queries with top-5 context. Measured: local embedding throughput on a real sample of each pipeline's chunks. Dollar rows use `prices.json`. Accuracy from the same run is repeated in the table so cost and quality sit side by side.
+**RAG cost** (`cost_bench.py`). The corpus is cycled to N files per format (default 1,000; the published run uses `--files 10000`). Both sides run the same pipeline — chunks → embeddings → vector store → top-5 context → LLM — and only the input format differs; the PDF column is the PDF pipeline that scored best in the accuracy run. Counted: chunks and embedding tokens for the initial index (ceil(chars/4), both sides), vector-store payload, re-index tokens when one paragraph changes in every document (JDF: only hash-changed chunks; PDF: the whole document), LLM input tokens for the query volume (`--queries`, default 1M; the published run uses 10M) with top-5 context. Measured: local embedding throughput on a real sample of each pipeline's chunks. Dollar rows use `prices.json`. Accuracy from the same run is repeated in the table so cost and quality sit side by side.
 
 ## Real documents — bring your own corpus (`byoc.py`)
 

diff --git a/bench/results/cost-latest.json b/bench/results/cost-latest.json
@@ -1,26 +1,26 @@
 {
-  "date": "2026-09-28",
+  "date": "2026-10-14",
   "machine": {
-    "cpu": "x86_64",
-    "platform": "Linux-7.0.0-1013-aws-x86_64-with-glibc2.43",
-    "python": "3.14.4"
+    "cpu": "Apple M5",
+    "platform": "macOS-27.0.1-arm64-arm-64bit-Mach-O",
+    "python": "3.14.6"
   },
-  "files": 1000,
+  "files": 10000,
   "corpusDocuments": 24,
   "pdfPipeline": "pdf-pdfplumber-1000",
   "pdfChunking": {
     "chars": 1000,
     "overlap": 200
   },
-  "queries": 1000000,
+  "queries": 10000000,
   "embedding": {
     "provider": "st",
     "model": "BAAI/bge-small-en-v1.5",
-    "version": "sentence-transformers 5.7.0 \u00b7 torch 2.14.0+cu130 \u00b7 cuda:0 (NVIDIA L40S)",
+    "version": "sentence-transformers 6.0.1 \u00b7 torch 2.14.0 \u00b7 mps:0 (mps:0)",
     "measuredOn": "first 2000 unique chunks per pipeline",
     "tokensPerSecond": {
-      "jdf": 74737.4806939267,
-      "pdfplumber": 217005.35584377687
+      "jdf": 25818.729679688102,
+      "pdfplumber": 36230.27857004535
     }
   },
   "prices": {
@@ -77,7 +77,7 @@
       "id": "jdf",
       "label": "JDF \u00b7 jdf chunk (section)",
       "version": "0.2.2",
-      "files": 1000,
+      "files": 10000,
       "perDoc": {
         "d01": {
           "chunks": 8,
@@ -207,42 +207,42 @@
         "mrr10": 0.6455915178571429,
         "retriever": "dense:BAAI/bge-small-en-v1.5"
       },
-      "chunks": 8000,
-      "embedTokens": 1305820,
-      "chunkStoreBytes": 5752468,
+      "chunks": 80000,
+      "embedTokens": 13057945,
+      "chunkStoreBytes": 57523843,
       "embedUsd": {
-        "openai-text-embedding-3-small": 0.0261164,
-        "openai-text-embedding-3-large": 0.1697566,
-        "cohere-embed-v4": 0.1566984
+        "openai-text-embedding-3-small": 0.2611589,
+        "openai-text-embedding-3-large": 1.69753285,
+        "cohere-embed-v4": 1.5669534
       },
-      "localEmbedSeconds": 17.472090146411816,
+      "localEmbedSeconds": 505.7547432425709,
       "reindex": {
         "chunksPerEditedDoc": 1,
         "tokensPerEditedDoc": 91,
-        "tokensAllDocsEdited": 91000,
+        "tokensAllDocsEdited": 910000,
         "usd": {
-          "openai-text-embedding-3-small": 0.00182,
-          "openai-text-embedding-3-large": 0.01183,
-          "cohere-embed-v4": 0.01092
+          "openai-text-embedding-3-small": 0.0182,
+          "openai-text-embedding-3-large": 0.1183,
+          "cohere-embed-v4": 0.1092
         }
       },
       "query": {
         "ctxTokensPerQuery": 716.3177083333334,
-        "queries": 1000000,
-        "inputTokens": 716317708.3333334,
+        "queries": 10000000,
+        "inputTokens": 7163177083.333334,
         "usd": {
-          "claude-sonnet-5": 1432.6354166666667,
-          "claude-haiku-4-5": 716.3177083333334,
-          "claude-opus-5": 3581.588541666667,
-          "openai-gpt-4o-mini": 107.44765625000001
+          "claude-sonnet-5": 14326.354166666668,
+          "claude-haiku-4-5": 7163.177083333334,
+          "claude-opus-5": 35815.88541666667,
+          "openai-gpt-4o-mini": 1074.4765625
         }
       }
     },
     {
       "id": "pdfplumber",
       "label": "PDF \u00b7 pdfplumber extract_text() \u00b7 fixed 1000/200",
       "version": "0.11.10",
-      "files": 1000,
+      "files": 10000,
       "perDoc": {
         "d01": {
           "chunks": 6,
@@ -372,34 +372,34 @@
         "mrr10": 0.5338748346560847,
         "retriever": "dense:BAAI/bge-small-en-v1.5"
       },
-      "chunks": 6249,
-      "embedTokens": 1385673,
-      "chunkStoreBytes": 5588311,
+      "chunks": 62499,
+      "embedTokens": 13857048,
+      "chunkStoreBytes": 55884436,
       "embedUsd": {
-        "openai-text-embedding-3-small": 0.02771346,
-        "openai-text-embedding-3-large": 0.18013749,
-        "cohere-embed-v4": 0.16628075999999997
+        "openai-text-embedding-3-small": 0.27714096000000005,
+        "openai-text-embedding-3-large": 1.8014162400000002,
+        "cohere-embed-v4": 1.66284576
       },
-      "localEmbedSeconds": 6.385432260932547,
+      "localEmbedSeconds": 382.47147267194345,
       "reindex": {
         "chunksPerEditedDoc": 6,
         "tokensPerEditedDoc": 1402,
-        "tokensAllDocsEdited": 1402000,
+        "tokensAllDocsEdited": 14020000,
         "usd": {
-          "openai-text-embedding-3-small": 0.02804,
-          "openai-text-embedding-3-large": 0.18226,
-          "cohere-embed-v4": 0.16823999999999997
+          "openai-text-embedding-3-small": 0.2804,
+          "openai-text-embedding-3-large": 1.8226,
+          "cohere-embed-v4": 1.6824
         }
       },
       "query": {
         "ctxTokensPerQuery": 1210.21875,
-        "queries": 1000000,
-        "inputTokens": 1210218750.0,
+        "queries": 10000000,
+        "inputTokens": 12102187500.0,
         "usd": {
-          "claude-sonnet-5": 2420.4375,
-          "claude-haiku-4-5": 1210.21875,
-          "claude-opus-5": 6051.09375,
-          "openai-gpt-4o-mini": 181.5328125
+          "claude-sonnet-5": 24204.375,
+          "claude-haiku-4-5": 12102.1875,
+          "claude-opus-5": 60510.9375,
+          "openai-gpt-4o-mini": 1815.328125
         }
       }
     }

diff --git a/bench/src/render.mjs b/bench/src/render.mjs
@@ -123,13 +123,13 @@ const costTableHtml = cost ? `        <div class="bench-table-wrap"><table class
 ${costRows().map(([name, f]) => `            <tr><td>${esc(name)}</td>${costSides.map((s) => `<td${s.id === "jdf" ? ' class="is-jdf"' : ""}>${f(s)}</td>`).join("")}</tr>`).join("\n")}
           </tbody>
         </table></div>
-        <p class="rag-bench-note">${int(cost.files)} files per format = the ${cost.corpusDocuments}-document corpus cycled. Tokens are counted from the chunks each pipeline produces (ceil(chars/4), both sides); embedding time is measured throughput on ${esc(machineLabel(cost))} (${cost.date}) applied to the totals. Prices from <code>bench/prices.json</code> (as of ${esc(Object.values(cost.prices.embedding)[0].as_of)}); edit it for your provider — the benchmark never calls a paid API. The PDF column is the PDF pipeline that scored best in the accuracy run. <strong>Scaling:</strong> figures are measured at ${int(cost.files)} documents and ${int(cost.queries)} queries; anything at other volumes (10,000 documents, 10M queries) is a linear estimate, not a measurement — index and re-index costs scale with documents, query cost scales with questions asked. Re-index: JDF re-embeds only chunks whose content hash changed (<code>jdf embed --incremental</code>); a PDF has no chunk identity, so an edit means re-chunking and re-embedding the whole document.</p>` : "";
+        <p class="rag-bench-note">${int(cost.files)} files per format = the ${cost.corpusDocuments}-document corpus cycled. Tokens are counted from the chunks each pipeline produces (ceil(chars/4), both sides); embedding time is measured throughput on ${esc(machineLabel(cost))} (${cost.date}) applied to the totals. Prices from <code>bench/prices.json</code> (as of ${esc(Object.values(cost.prices.embedding)[0].as_of)}); edit it for your provider — the benchmark never calls a paid API. The PDF column is the PDF pipeline that scored best in the accuracy run. <strong>Scaling:</strong> figures are measured at ${int(cost.files)} documents and ${int(cost.queries)} queries; anything at other volumes is a linear estimate, not a measurement — index and re-index costs scale with documents, query cost scales with questions asked. Re-index: JDF re-embeds only chunks whose content hash changed (<code>jdf embed --incremental</code>); a PDF has no chunk identity, so an edit means re-chunking and re-embedding the whole document.</p>` : "";
 
 // Cost summary for the hero card (JDF vs best-accuracy PDF parser = first PDF side).
 const pdfCost = costSides.find((s) => s.id !== "jdf");
 const jdfCost = costSides.find((s) => s.id === "jdf");
 const costData = cost && pdfCost ? {
-  files: cost.files, ek: Object.keys(cost.prices.embedding)[0], lk: Object.keys(cost.prices.llm_input)[0],
+  files: cost.files, queries: cost.queries, ek: Object.keys(cost.prices.embedding)[0], lk: Object.keys(cost.prices.llm_input)[0],
   embedLabel: cost.prices.embedding[Object.keys(cost.prices.embedding)[0]].label, llmLabel: cost.prices.llm_input[Object.keys(cost.prices.llm_input)[0]].label,
   jdf: { acc: jdfCost.accuracy?.recallAt1000Tok, tokens: jdfCost.embedTokens, usd: jdfCost.embedUsd[Object.keys(cost.prices.embedding)[0]], reindexUsd: jdfCost.reindex.usd[Object.keys(cost.prices.embedding)[0]], queryUsd: jdfCost.query?.usd[Object.keys(cost.prices.llm_input)[0]] },
   pdf: { label: pdfCost.label, acc: pdfCost.accuracy?.recallAt1000Tok, tokens: pdfCost.embedTokens, usd: pdfCost.embedUsd[Object.keys(cost.prices.embedding)[0]], reindexUsd: pdfCost.reindex.usd[Object.keys(cost.prices.embedding)[0]], queryUsd: pdfCost.query?.usd[Object.keys(cost.prices.llm_input)[0]] },
@@ -179,7 +179,7 @@ if (cost) {
     `|---|${costSides.map(() => "---:").join("|")}|`,
     ...costRows().map(([name, f]) => `| ${name} | ${costSides.map((s) => (s.id === "jdf" ? `**${f(s)}**` : f(s))).join(" | ")} |`),
     "",
-    `${int(cost.files)} files per format (${cost.corpusDocuments}-document corpus cycled); tokens counted from each pipeline's chunks, embedding time measured on ${machineLabel(cost)}, ${cost.date}. Other volumes (10,000 documents, 10M queries) are linear estimates, not measurements. Prices: [\`bench/prices.json\`](bench/prices.json). Method: [\`bench/README.md\`](bench/README.md).`,
+    `${int(cost.files)} files per format (${cost.corpusDocuments}-document corpus cycled); tokens counted from each pipeline's chunks, embedding time measured on ${machineLabel(cost)}, ${cost.date}. Other volumes are linear estimates, not measurements. Prices: [\`bench/prices.json\`](bench/prices.json). Method: [\`bench/README.md\`](bench/README.md).`,
   ].join("\n");
   md = replaceBlock(md, "cost", mdCost);
 }