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* Praus / Ortelan benchmarks: single data file (the only file you need to edit).
* ---------------------------------------------------------------------------
* Loaded by index.html as a plain <script>, so it works from any static host
* (Hugging Face static Space, python -m http.server, or file://). No build step.
*
* SCHEMA
* window.BENCH_DATA = {
* meta: {
* title, badge, summary, // header text
* measured: "Sep 2026", // fixed label used in every card footer (never "today")
* preview: true, // true shows the "Preview" pill and pending notes
* headlineBoards: [ids...], // up to 4 stat boxes in the header
* strengths: [{ id, why }], // up to 3 "Where Praus is strong" boards
* methodology: [paragraphs...]
* },
* categories: { "<Category>": { dot, tagBg, tagText, tagRing } },
* boards: [{
* id, name, category, description,
* unit: "accuracy %" | "pass@1 %",
* higherIsBetter: true,
* links: { // footer "Dataset · Paper" links (all verified HTTP 200)
* dataset: "https://huggingface.co/datasets/<org>/<name>", datasetLabel?: "Dataset (HF mirror)",
* paper: "https://arxiv.org/abs/<id>", paperLabel?: "Announcement",
* extra?: [{ label, url }] // e.g. official CSV, subset paper
* },
* fieldSize?: 20, // total models in the source ranking
* (praus.rank?: true rank in the full source ranking when only a subset of competitors is listed)
* fieldSize?: 20, // total models in the source ranking (only the top entries are listed)
* pending: false, // true = hidden until praus.score is filled in
* praus: {
* score: 93.3, // number, 0-100 (null while pending)
* sample: "150 of 1,172 questions", // shown in the footer
* runId: "36425671251", // displayed as plain text provenance
* note: "" // optional caveat shown under the footer
* },
* competitors: [ // empty = "Competitor scores pending"
* { model: "GPT-5", vendor: "openai", score: 91.2,
* source: "https://... (URL of the public page the score was quoted from)",
* sourceLabel: "OpenAI system card" } // sourceLabel optional
* ]
* }]
* }
*
* vendor must be one of: openai, anthropic, google, xai, meta, mistral, deepseek, qwen, groq, other
* (matches logos/<vendor>.svg). Rank "#N of M" is computed from real entries only:
* sorted by score (desc if higherIsBetter), ties share a rank, Praus is never forced.
*/
window.BENCH_DATA = {
"meta": {
"title": "October 2026 benchmarks",
"badge": "",
"summary": "Production Praus scores on public benchmarks, graded the same way each benchmark grades. Self-reported. October 2026.",
"measured": "October 2026",
"preview": false,
"headlineBoards": [
"math-500",
"gsm8k",
"graphwalks"
],
"headlineFootnote": "",
"headlineStats": [
{
"label": "MATH-500",
"value": "96.6",
"detail": "",
"jump": "board-math-500"
},
{
"label": "GSM8K",
"value": "93.3",
"detail": "",
"jump": "board-gsm8k"
},
{
"label": "GraphWalks",
"value": "100",
"detail": "",
"jump": "board-graphwalks"
},
{
"label": "Cost per answer",
"value": "$0.00069",
"detail": "",
"jump": "board-cost"
}
],
"strengths": [
{
"id": "math-500",
"why": "500 high-school competition math problems, scored on the final answer. Praus scores 96.6, within 3.0 points of the top score."
},
{
"id": "gsm8k",
"why": "Grade-school math word problems that take several steps. Praus scores 93.3, within 2.1 points of the top score."
},
{
"id": "mbpp",
"why": "Short Python problems checked against unit tests. Praus ranks fourth, ahead of GPT-5 (high)."
}
],
"methodology": [
"Every Praus score on this page is self-reported. Ortelan ran the evals on its own infrastructure. No independent third party has reproduced, audited, or verified them.",
"Praus is a compound system, not a single model. It can use multiple models, tools, and retrieval in one pipeline. Competitor numbers are usually single models under each publisher's own settings, so these are not like-for-like comparisons.",
"Praus scores come from samples of each official test set, scored with that benchmark's official grader when we recorded one. Sample sizes are on the board when we have them. Sample results have statistical uncertainty and can differ from a full run.",
"MBPP and TriviaQA are from early runs of about 50 questions on an earlier pipeline build. Treat those two as rough estimates. A later or full run could land higher or lower.",
"Competitor scores are public numbers Ortelan compiled as of October 2026. We did not re-run them. Publishers may have updated, corrected, or withdrawn those figures. Prompting, attempts, tool use, and grader version can differ from what we used.",
"Not every model publishes every benchmark. Boards show different competitor sets on purpose. A missing model is not a claim about how it would score. Rankings are only relative to the models listed on that board.",
"SimpleQA was run with web search on. The GraphWalks run hit its time limit: 92 of 150 sampled questions finished, and all 92 were correct. The reported GraphWalks score is only for those finished questions.",
"Cost: Praus's number is our estimated infrastructure cost for a fixed workload (8,717 input + 935 output tokens). Other rows use public list prices for that same workload. Neither is a customer invoice. Caching, batch discounts, thinking tokens, tool fees, and regional pricing are excluded, so real spend will differ.",
"These results describe the pipeline at the time we measured it. The system can change. Nothing here is a warranty of accuracy, completeness, or performance on any real task. Model names and trademarks belong to their owners; listing them is not an endorsement or affiliation."
]
},
"categories": {
"Knowledge": {
"dot": "oklch(62.3% .214 259.815)",
"tagBg": "oklch(93.2% .032 255.585)",
"tagText": "oklch(37.9% .146 265.522)",
"tagRing": "oklch(80.9% .105 251.813 / .7)"
},
"Code": {
"dot": "oklch(70.5% .213 47.604)",
"tagBg": "oklch(95.4% .038 75.164)",
"tagText": "oklch(40.8% .123 38.172)",
"tagRing": "oklch(83.7% .128 66.29 / .7)"
},
"Math": {
"dot": "oklch(69.6% .17 162.48)",
"tagBg": "oklch(95% .052 163.051)",
"tagText": "oklch(37.8% .077 168.94)",
"tagRing": "oklch(84.5% .143 164.978 / .7)"
},
"Reasoning": {
"dot": "oklch(76.9% .188 70.08)",
"tagBg": "oklch(96.2% .059 95.617)",
"tagText": "oklch(41.4% .112 45.904)",
"tagRing": "oklch(87.9% .169 91.605 / .7)"
},
"Science": {
"dot": "oklch(71.5% .143 215.221)",
"tagBg": "oklch(95.6% .045 203.388)",
"tagText": "oklch(39.8% .07 227.392)",
"tagRing": "oklch(86.5% .127 207.078 / .7)"
},
"Long context": {
"dot": "oklch(65.6% .241 354.308)",
"tagBg": "oklch(94.8% .028 342.258)",
"tagText": "oklch(40.8% .153 2.432)",
"tagRing": "oklch(82.3% .12 346.018 / .7)"
}
},
"cost": {
"title": "Estimated cost per general answer",
"summary": "Praus's running cost is lower than every model shown, including budget models. Same workload for every model: 8,717 input + 935 output tokens per answer. Lower is better.",
"explainer": "One answer = a typical full written reply to a general question (avg. ~8,700 input + ~935 output tokens across 165 real answers), e.g. “What was the first Nike running shoe?”",
"praus": 0.00069,
"groups": [
{
"name": "Flagship & mid-tier models",
"rows": [
[
"Mistral Large 3",
"mistral",
0.00576
],
[
"DeepSeek V4 Pro",
"deepseek",
0.01521
],
[
"Mistral Medium 3.5",
"mistral",
0.02009
],
[
"Gemini 3.8 Flash",
"google",
0.02009
],
[
"Grok 4.7",
"xai",
0.02304
],
[
"GPT-6 Sol",
"openai",
0.02678
],
[
"Claude Sonnet 5",
"anthropic",
0.02678
],
[
"Gemini 3.1 Pro Preview",
"google",
0.02865
],
[
"Kimi K3",
"other",
0.04018
],
[
"Claude Opus 5.5",
"anthropic",
0.05357
],
[
"GPT-6 Astra",
"openai",
0.13392
],
[
"Claude Fable 5.1",
"anthropic",
0.13392
]
]
},
{
"name": "Budget models",
"rows": [
[
"GPT-6 Luna",
"openai",
0.00134
],
[
"Mistral Small 4",
"mistral",
0.00187
],
[
"DeepSeek V4.1 Flash",
"deepseek",
0.00374
],
[
"Gemini 3.5 Flash-Lite",
"google",
0.00495
],
[
"Claude Haiku 4.5",
"anthropic",
0.01339
]
]
}
],
"footnote": "[1] Estimated, not billed. Competitor costs are calculated by applying each provider's publicly listed per-token API prices, as of September 28, 2026, to the same fixed workload (8,717 input + 935 output tokens per answer). They exclude caching, batch or volume discounts, reasoning or thinking tokens, tool and search fees, and regional pricing, and actual costs will vary with usage. Prices are a point-in-time snapshot and providers may change them at any time. Praus's figure is its own estimated infrastructure cost for the same workload on its production pipeline, is self-reported, and is not a customer price. The workload is an average of 165 real Praus answers to general questions; other tasks may use very different token counts."
},
"boards": [
{
"id": "graphwalks",
"name": "GraphWalks",
"category": "Reasoning",
"fieldSize": 16,
"description": "Long-context multi-hop reasoning benchmark released by OpenAI in 2025. Each prompt contains a large directed graph written out as a list of edges, and the model must return either every node at an exact breadth-first-search depth from a start node or every parent of a given node. Answers are node sets graded against the ground truth.",
"unit": "accuracy %",
"higherIsBetter": true,
"links": {
"dataset": "https://huggingface.co/datasets/openai/graphwalks",
"datasetLabel": "Official dataset (OpenAI)"
},
"praus": {
"rank": 1,
"score": 100,
"sample": "",
"runId": "",
"note": ""
},
"competitors": [
{
"model": "GPT-5.2 (high)",
"vendor": "openai",
"lab": "OpenAI",
"rank": 2,
"score": 98.0,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "OpenAI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "GPT-5.4",
"vendor": "openai",
"lab": "OpenAI",
"rank": 3,
"score": 93.0,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "OpenAI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Claude Opus 4.5",
"vendor": "anthropic",
"lab": "Anthropic",
"rank": 4,
"score": 92.0,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Anthropic (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "GPT-5 mini (high)",
"vendor": "openai",
"lab": "OpenAI",
"rank": 5,
"score": 85.5,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "OpenAI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Gemini 3 Flash (high)",
"vendor": "google",
"lab": "Google",
"rank": 6,
"score": 84.2,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Google (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Seed2.0 Lite",
"vendor": "other",
"lab": "ByteDance",
"rank": 7,
"score": 82.5,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "ByteDance (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Claude Sonnet 4.5",
"vendor": "anthropic",
"lab": "Anthropic",
"rank": 8,
"score": 80.5,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Anthropic (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Gemini 3 Pro (high)",
"vendor": "google",
"lab": "Google",
"rank": 9,
"score": 79.9,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Google (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "GPT-5 (high)",
"vendor": "openai",
"lab": "OpenAI",
"rank": 10,
"score": 78.3,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "OpenAI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "GPT-5.1 Thinking",
"vendor": "openai",
"lab": "OpenAI",
"rank": 11,
"score": 76.8,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "OpenAI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "GPT-5.4 mini",
"vendor": "openai",
"lab": "OpenAI",
"rank": 12,
"score": 76.3,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "OpenAI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "GPT-5.4 nano",
"vendor": "openai",
"lab": "OpenAI",
"rank": 13,
"score": 73.4,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "OpenAI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Seed2.0 Pro",
"vendor": "other",
"lab": "ByteDance",
"rank": 14,
"score": 68.9,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "ByteDance (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Seed2.0 Mini",
"vendor": "other",
"lab": "ByteDance",
"rank": 15,
"score": 64.1,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "ByteDance (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "GPT-5 nano (high)",
"vendor": "openai",
"lab": "OpenAI",
"rank": 16,
"score": 64.0,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "OpenAI (Ortelan competitor dataset, Sep 28 2026)"
}
]
},
{
"id": "simpleqa",
"name": "SimpleQA",
"category": "Knowledge",
"fieldSize": 40,
"description": "Factual-accuracy benchmark from OpenAI (Wei et al., 2024). 4,326 short, fact-seeking questions, each with a single indisputable answer verified by independent annotators. Responses are graded as correct, incorrect, or not attempted.",
"unit": "accuracy %",
"higherIsBetter": true,
"links": {
"dataset": "https://openaipublic.blob.core.windows.net/simple-evals/simple_qa_test_set.csv",
"datasetLabel": "Official dataset (OpenAI)",
"paper": "https://arxiv.org/abs/2411.04368",
"paperLabel": "Paper (Wei et al., 2024)"
},
"praus": {
"rank": 1,
"score": 81.6,
"sample": "",
"runId": "36362020662"
},
"competitors": [
{
"model": "Gemini 3.1 Pro",
"vendor": "google",
"lab": "Google",
"rank": 2,
"score": 74.8,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Google (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "GPT-6 Astra",
"vendor": "openai",
"lab": "OpenAI",
"rank": 3,
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"sourceLabel": "Google (Ortelan competitor dataset, Sep 28 2026)"
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{
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"lab": "Google",
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"sourceLabel": "Google (Ortelan competitor dataset, Sep 28 2026)"
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"lab": "OpenAI",
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"lab": "Google",
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"sourceLabel": "Google (Ortelan competitor dataset, Sep 28 2026)"
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{
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"sourceLabel": "Google (Ortelan competitor dataset, Sep 28 2026)"
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"sourceLabel": "Anthropic (Ortelan competitor dataset, Sep 28 2026)"
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"lab": "xAI",
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"score": 51.8,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "xAI (Ortelan competitor dataset, Sep 28 2026)"
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{
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"vendor": "qwen",
"lab": "Alibaba",
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"score": 50.6,
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"sourceLabel": "Alibaba (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Qwen3 235B A22B Thinking 2507",
"vendor": "qwen",
"lab": "Alibaba",
"rank": 14,
"score": 50.4,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Alibaba (Ortelan competitor dataset, Sep 28 2026)"
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"unit": "accuracy %",
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"links": {
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"paper": "https://arxiv.org/abs/2109.07958",
"paperLabel": "Paper (Lin et al., 2021)"
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"vendor": "other",
"lab": "Allen AI",
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{
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"higherIsBetter": true,
"links": {
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"lab": "NVIDIA",
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"lab": "DeepSeek",
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"sourceLabel": "OpenAI (Ortelan competitor dataset, Sep 28 2026)"
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"vendor": "other",
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},
{
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},
{
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"lab": "DeepSeek",
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"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "DeepSeek (Ortelan competitor dataset, Sep 28 2026)"
},
{
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"sourceLabel": "NVIDIA (Ortelan competitor dataset, Sep 28 2026)"
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{
"model": "MiMo-V2-Flash (base)",
"vendor": "other",
"lab": "Xiaomi",
"rank": 10,
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"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Xiaomi (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Kimi-K2 (base)",
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"lab": "Moonshot AI",
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"sourceLabel": "Moonshot AI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "DeepSeek-V3.1 (base)",
"vendor": "deepseek",
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"score": 91.4,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "DeepSeek (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "DeepSeek-V3.2 (base)",
"vendor": "deepseek",
"lab": "DeepSeek",
"rank": 14,
"score": 91.1,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "DeepSeek (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "DeepSeek-V4-Flash (base)",
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"lab": "DeepSeek",
"rank": 15,
"score": 90.8,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "DeepSeek (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Seed-OSS-36B (base)",
"vendor": "other",
"lab": "ByteDance",
"rank": 15,
"score": 90.8,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "ByteDance (Ortelan competitor dataset, Sep 28 2026)"
}
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{
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"category": "Knowledge",
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"description": "Science reasoning benchmark from the Allen Institute for AI (Clark et al., 2018). Multiple-choice questions from grade 3 to 9 science exams. The Challenge set contains only questions that both a retrieval-based and a word co-occurrence baseline answered incorrectly.",
"unit": "accuracy %",
"higherIsBetter": true,
"links": {
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"paper": "https://arxiv.org/abs/1803.05457",
"paperLabel": "Paper (Clark et al., 2018)"
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"sample": "150 of 1,172 questions",
"runId": "36425671251"
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"vendor": "other",
"lab": "Xiaomi",
"rank": 1,
"score": 97.2,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Xiaomi (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "MiMo-V2.5 (base)",
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"sourceLabel": "Xiaomi (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "GPT-5",
"vendor": "openai",
"lab": "OpenAI",
"rank": 3,
"score": 96.3,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "OpenAI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Nemotron 3 Super 120B-A12B (base)",
"vendor": "other",
"lab": "NVIDIA",
"rank": 4,
"score": 96.1,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "NVIDIA (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "GLM-5",
"vendor": "other",
"lab": "Zhipu AI",
"rank": 5,
"score": 96.0,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Zhipu AI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "MiMo-V2-Flash (base)",
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"lab": "Xiaomi",
"rank": 6,
"score": 95.9,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Xiaomi (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Kimi-K2 (base)",
"vendor": "other",
"lab": "Moonshot AI",
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"score": 95.7,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Moonshot AI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "DeepSeek-V3.1 (base)",
"vendor": "deepseek",
"lab": "DeepSeek",
"rank": 8,
"score": 95.6,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "DeepSeek (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "DeepSeek-V3.2-Exp (base)",
"vendor": "deepseek",
"lab": "DeepSeek",
"rank": 9,
"score": 95.5,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "DeepSeek (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "DeepSeek V4 Flash",
"vendor": "deepseek",
"lab": "DeepSeek",
"rank": 10,
"score": 95.4,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "DeepSeek (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Qwen3 235B A22B Instruct 2507",
"vendor": "qwen",
"lab": "Alibaba",
"rank": 11,
"score": 94.8,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Alibaba (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Ling-flash-base-2.0 (base)",
"vendor": "other",
"lab": "inclusionAI (Ant Group)",
"rank": 11,
"score": 94.8,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "inclusionAI (Ant Group) (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Mistral Medium 3.1",
"vendor": "mistral",
"lab": "Mistral AI",
"rank": 13,
"score": 93.9,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Mistral AI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "GLM-4.5-Air (base)",
"vendor": "other",
"lab": "Zhipu AI",
"rank": 13,
"score": 93.9,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Zhipu AI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Nemotron 3 Nano Omni",
"vendor": "other",
"lab": "NVIDIA",
"rank": 15,
"score": 93.5,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "NVIDIA (Ortelan competitor dataset, Sep 28 2026)"
}
]
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{
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"category": "Knowledge",
"fieldSize": 17,
"description": "Open-domain question answering benchmark from the University of Washington (Joshi et al., 2017). Questions authored by trivia enthusiasts, answered here without supplied evidence documents. Scored by exact match against the accepted answer aliases.",
"unit": "accuracy %",
"higherIsBetter": true,
"links": {
"dataset": "https://huggingface.co/datasets/mandarjoshi/trivia_qa",
"paper": "https://arxiv.org/abs/1705.03551",
"paperLabel": "Paper (Joshi et al., 2017)"
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"praus": {
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"score": 81.6,
"sample": "",
"runId": ""
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"vendor": "deepseek",
"lab": "DeepSeek",
"rank": 1,
"score": 85.6,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "DeepSeek (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Kimi K2 (base)",
"vendor": "other",
"lab": "Moonshot AI",
"rank": 2,
"score": 85.1,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Moonshot AI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "DeepSeek-V3.2-Exp (base)",
"vendor": "deepseek",
"lab": "DeepSeek",
"rank": 3,
"score": 83.9,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "DeepSeek (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "DeepSeek-V3.1 (base)",
"vendor": "deepseek",
"lab": "DeepSeek",
"rank": 4,
"score": 83.5,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "DeepSeek (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "DeepSeek-V3.2 (base)",
"vendor": "deepseek",
"lab": "DeepSeek",
"rank": 5,
"score": 83.3,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "DeepSeek (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "DeepSeek-V4-Flash (base)",
"vendor": "deepseek",
"lab": "DeepSeek",
"rank": 6,
"score": 82.8,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "DeepSeek (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Seed-OSS-36B (base)",
"vendor": "other",
"lab": "ByteDance",
"rank": 7,
"score": 82.1,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "ByteDance (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "MiMo-V2.5-Pro (base)",
"vendor": "other",
"lab": "Xiaomi",
"rank": 9,
"score": 81.3,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Xiaomi (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "GLM-5 (base)",
"vendor": "other",
"lab": "Zhipu AI",
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"score": 80.9,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Zhipu AI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "MiMo-V2.5 (base)",
"vendor": "other",
"lab": "Xiaomi",
"rank": 11,
"score": 80.7,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Xiaomi (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "MiMo-V2-Flash (base)",
"vendor": "other",
"lab": "Xiaomi",
"rank": 12,
"score": 80.3,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Xiaomi (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "GLM-4.5 (base)",
"vendor": "other",
"lab": "Zhipu AI",
"rank": 13,
"score": 80.0,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Zhipu AI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Kimi Linear 48B-A3B (base)",
"vendor": "other",
"lab": "Moonshot AI",
"rank": 14,
"score": 75.2,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Moonshot AI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Ministral 3 14B (base)",
"vendor": "mistral",
"lab": "Mistral AI",
"rank": 15,
"score": 74.9,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Mistral AI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Ministral 3 8B (base)",
"vendor": "mistral",
"lab": "Mistral AI",
"rank": 16,
"score": 68.1,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Mistral AI (Ortelan competitor dataset, Sep 28 2026)"
}
]
},
{
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"category": "Code",
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"description": "Code-generation benchmark from OpenAI (Chen et al., 2021). 164 hand-written Python programming problems, each with a function signature, docstring, and unit tests. A solution counts as correct only if it passes every test.",
"unit": "accuracy %",
"higherIsBetter": true,
"links": {
"dataset": "https://huggingface.co/datasets/openai/openai_humaneval",
"datasetLabel": "Official dataset (OpenAI)",
"paper": "https://arxiv.org/abs/2107.03374",
"paperLabel": "Paper (Chen et al., 2021)"
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"sample": "138 of 150 problems passed",
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"vendor": "anthropic",
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"rank": 1,
"score": 97.6,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Anthropic (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Grok 4",
"vendor": "xai",
"lab": "xAI",
"rank": 2,
"score": 97.0,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "xAI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "GLM-5",
"vendor": "other",
"lab": "Zhipu AI",
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"score": 97.0,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Zhipu AI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Claude Opus 4.6",
"vendor": "anthropic",
"lab": "Anthropic",
"rank": 2,
"score": 97.0,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Anthropic (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Claude Opus 4.5",
"vendor": "anthropic",
"lab": "Anthropic",
"rank": 2,
"score": 97.0,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Anthropic (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Gemini 3 Pro",
"vendor": "google",
"lab": "Google",
"rank": 2,
"score": 97.0,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Google (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Claude Haiku 4.5",
"vendor": "anthropic",
"lab": "Anthropic",
"rank": 7,
"score": 96.3,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Anthropic (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Qwen3.5 397B A17B",
"vendor": "qwen",
"lab": "Alibaba",
"rank": 7,
"score": 96.3,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Alibaba (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Claude Opus 4.1",
"vendor": "anthropic",
"lab": "Anthropic",
"rank": 9,
"score": 95.7,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Anthropic (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "INTELLECT-3",
"vendor": "other",
"lab": "Prime Intellect",
"rank": 10,
"score": 95.1,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Prime Intellect (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "GPT-5.1",
"vendor": "openai",
"lab": "OpenAI",
"rank": 11,
"score": 94.5,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "OpenAI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "DeepSeek V3.2",
"vendor": "deepseek",
"lab": "DeepSeek",
"rank": 12,
"score": 93.9,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "DeepSeek (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Nemotron 3 Nano 30B A3B",
"vendor": "other",
"lab": "NVIDIA",
"rank": 13,
"score": 93.3,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "NVIDIA (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Qwen3.5 Plus",
"vendor": "qwen",
"lab": "Alibaba",
"rank": 13,
"score": 93.3,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Alibaba (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Qwen3 Coder 480B A35B",
"vendor": "qwen",
"lab": "Alibaba",
"rank": 15,
"score": 92.7,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Alibaba (Ortelan competitor dataset, Sep 28 2026)"
}
]
},
{
"id": "mbpp",
"name": "MBPP",
"category": "Code",
"fieldSize": 16,
"description": "Mostly Basic Python Problems, a code-generation benchmark from Google Research (Austin et al., 2021). Entry-level Python programming tasks, each with a short description and three test cases. A solution counts as correct only if it passes every test.",
"unit": "accuracy %",
"higherIsBetter": true,
"links": {
"dataset": "https://huggingface.co/datasets/google-research-datasets/mbpp",
"datasetLabel": "Official dataset (Google Research)",
"paper": "https://arxiv.org/abs/2108.07732",
"paperLabel": "Paper (Austin et al., 2021)"
},
"praus": {
"rank": 4,
"score": 84.0,
"sample": "",
"runId": ""
},
"competitors": [
{
"model": "Sarvam-30B",
"vendor": "other",
"lab": "Sarvam AI",
"rank": 1,
"score": 92.7,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Sarvam AI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "MiniCPM-SALA",
"vendor": "other",
"lab": "OpenBMB",
"rank": 2,
"score": 89.1,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "OpenBMB (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Gemini 3 Pro",
"vendor": "google",
"lab": "Google",
"rank": 3,
"score": 86.2,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Google (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Stable-DiffCoder-8B (base)",
"vendor": "other",
"lab": "ByteDance",
"rank": 5,
"score": 83.6,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "ByteDance (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "GPT-5 (high)",
"vendor": "openai",
"lab": "OpenAI",
"rank": 6,
"score": 83.1,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "OpenAI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "ERNIE 5.0",
"vendor": "other",
"lab": "Baidu",
"rank": 7,
"score": 82.5,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Baidu (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Kimi K2 Instruct",
"vendor": "other",
"lab": "Moonshot AI",
"rank": 8,
"score": 81.8,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Moonshot AI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "DeepSeek-V3.2 (thinking)",
"vendor": "deepseek",
"lab": "DeepSeek",
"rank": 9,
"score": 81.5,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "DeepSeek (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Seed-OSS-36B (base)",
"vendor": "other",
"lab": "ByteDance",
"rank": 10,
"score": 80.6,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "ByteDance (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Qwen3-235B-A22B-Instruct-2507",
"vendor": "qwen",
"lab": "Alibaba",
"rank": 11,
"score": 79.9,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Alibaba (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "DeepSeek-V3.1",
"vendor": "deepseek",
"lab": "DeepSeek",
"rank": 11,
"score": 79.9,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "DeepSeek (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "LongCat-Flash-Chat",
"vendor": "other",
"lab": "Meituan",
"rank": 13,
"score": 79.6,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Meituan (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Kimi K2 (base)",
"vendor": "other",
"lab": "Moonshot AI",
"rank": 14,
"score": 79.4,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Moonshot AI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "ERNIE 5.0 (base)",
"vendor": "other",
"lab": "Baidu",
"rank": 15,
"score": 79.1,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Baidu (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "DeepSeek-V3.2-Exp (base)",
"vendor": "deepseek",
"lab": "DeepSeek",
"rank": 16,
"score": 78.8,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "DeepSeek (Ortelan competitor dataset, Sep 28 2026)"
}
]
},
{
"id": "math-500",
"name": "MATH-500",
"category": "Math",
"fieldSize": 114,
"description": "Competition mathematics benchmark. A 500-problem subset, selected by OpenAI (Lightman et al., 2023), of the MATH dataset of high-school competition problems (Hendrycks et al., 2021). Scored on the final boxed answer.",
"unit": "accuracy %",
"higherIsBetter": true,
"links": {
"dataset": "https://huggingface.co/datasets/HuggingFaceH4/MATH-500",
"paper": "https://arxiv.org/abs/2103.03874",
"paperLabel": "Paper (Hendrycks et al., 2021)",
"extra": [
{
"label": "Subset paper (Lightman et al., 2023)",
"url": "https://arxiv.org/abs/2305.20050"
}
]
},
"praus": {
"rank": 16,
"score": 96.6,
"sample": "144 of 149 graded questions (150 of 500 sampled)",
"runId": "36425671251"
},
"competitors": [
{
"model": "Qwen3 235B A22B Thinking 2507",
"vendor": "qwen",
"lab": "Alibaba",
"rank": 1,
"score": 99.6,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Alibaba (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "GPT-5",
"vendor": "openai",
"lab": "OpenAI",
"rank": 2,
"score": 99.4,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "OpenAI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "LongCat-Flash-Thinking",
"vendor": "other",
"lab": "Meituan",
"rank": 3,
"score": 99.2,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Meituan (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Gemini 3.8 Flash",
"vendor": "google",
"lab": "Google",
"rank": 4,
"score": 99.1,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Google (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Gemini 3.7 Flash",
"vendor": "google",
"lab": "Google",
"rank": 5,
"score": 99.0,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Google (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Grok 4",
"vendor": "xai",
"lab": "xAI",
"rank": 5,
"score": 99.0,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "xAI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "DeepSeek V3.1",
"vendor": "deepseek",
"lab": "DeepSeek",
"rank": 7,
"score": 98.8,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "DeepSeek (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Llama 3.3 Nemotron Super 49B v1.5",
"vendor": "other",
"lab": "NVIDIA",
"rank": 8,
"score": 98.3,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "NVIDIA (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Grok 4.6",
"vendor": "xai",
"lab": "xAI",
"rank": 9,
"score": 98.2,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "xAI (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "DeepSeek V4 Pro 0813",
"vendor": "deepseek",
"lab": "DeepSeek",
"rank": 9,
"score": 98.2,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "DeepSeek (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Qwen3 235B A22B Instruct 2507",
"vendor": "qwen",
"lab": "Alibaba",
"rank": 11,
"score": 98.0,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Alibaba (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Qwen3.5 Flash",
"vendor": "qwen",
"lab": "Alibaba",
"rank": 11,
"score": 98.0,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Alibaba (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Qwen3.6 Max Preview",
"vendor": "qwen",
"lab": "Alibaba",
"rank": 11,
"score": 98.0,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Alibaba (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "Gemini 3.6 Flash",
"vendor": "google",
"lab": "Google",
"rank": 11,
"score": 98.0,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Google (Ortelan competitor dataset, Sep 28 2026)"
},
{
"model": "GLM-4.5",
"vendor": "other",
"lab": "Zhipu AI",
"rank": 15,
"score": 97.9,
"source": "ortelan-competitor-data-2026-09-28",
"sourceLabel": "Zhipu AI (Ortelan competitor dataset, Sep 28 2026)"
}
]
}
]
};
/* ---------------------------------------------------------------------------
* DISABLED: cost / latency boards (provenance unverified). Not loaded.
* Kept here verbatim from the previous page's static tables so they can be
* restored if Chris confirms sources. To restore: move entries into
* BENCH_DATA.boards with category "Costs" (add a Costs color: dot
* "oklch(62.7% .194 149.214)", tagBg "oklch(96.2% .044 156.743)",
* tagText "oklch(39.3% .095 152.535)", tagRing "oklch(87.1% .15 154.449 / .7)")
* and higherIsBetter:false. The renderer would also need a table/lower-is-better variant.
* The old "Cost vs quality" scatter used a formula-derived quality axis and is
* intentionally NOT kept. Faith boards (CEFEAI) were removed entirely at Chris's request.
*
* pricing (USD per 1M tokens in/out, $/chat, tokens/response):
* Praus Fast 0.05 / 0.08 0.003 420
* Gemini 2.5 Flash Lite 0.10 / 0.40 0.035 54042
* GPT-4o Mini 0.15 / 0.60 0.045 610
* Grok 3 Mini 0.30 / 0.50 0.055 890
* Gemini 2.5 Flash 0.10 / 0.40 0.075 9885
* GPT-5 Mini 0.25 / 2.00 0.150 1200
* o3-mini 1.10 / 4.40 0.180 1800
* Claude Opus 4.7 5.00 / 25.00 0.120 7481
* Gemini 2.5 Pro 1.25 / 10.00 0.210 9808
* Gemini 3.1 Pro 2.00 / 12.00 0.290 7523
* Claude Opus 4.1 15.00 / 75.00 0.320 9200
* Grok 3 3.00 / 15.00 0.340 6400
* GPT-5.2 1.75 / 14.00 0.500 1584
* Claude Opus 4.8 5.50 / 27.50 0.140 6900
* DeepSeek V3 0.27 / 1.10 0.040 5200
* Mistral Large 2.00 / 6.00 0.110 4100
*
* time to first token (ms, median streaming):
* Praus 180 · Grok 3 Mini 195 · Gemini 2.5 Flash Lite 210 · GPT-4o Mini 240 ·
* Gemini 2.5 Flash 280 · GPT-5 Mini 310 · DeepSeek V3 350 · Mistral Large 390 ·
* Claude Opus 4.7 420 · Claude Opus 4.8 450 · Gemini 2.5 Pro 480 · GPT-5.2 510 ·
* Grok 3 560 · Gemini 3.1 Pro 620 · Claude Opus 4.1 740 · o3-mini 890
* ------------------------------------------------------------------------- */
|