{
 "body": "\n## What it measures\n\nThis page documents Artificial Analysis's Output Speed metric \u2014 the throughput figure the\npublisher headlines under \"Speed\" on its own site \u2014 as this repository's\n`artificial_analysis_speed_index`. It is a pure performance measurement, not a capability or\ncorrectness score: Artificial Analysis sends a live prompt to a model's public API and times how\nmany tokens per second stream back after the first token arrives. Unlike the Intelligence Index,\nArtificial Analysis does not publish a single blended \"Speed Index\" combining multiple timing\nmetrics into one number; Output Speed is reported alongside, not merged with, separate metrics for\ntime to first token and end-to-end response time. Workloads vary by input length \u2014 about 1,000,\n10,000 or 100,000 input tokens, plus a vision workload of one megapixel image and roughly 1,000\ntext tokens \u2014 because both time-to-first-token and output speed itself shift with prompt length\nand technique such as speculative decoding.\n\n## How it is scored\n\nOutput Speed is the average number of tokens received per second, counted from after the first\nstreamed chunk arrives to the last: total tokens minus first-chunk tokens, divided by the elapsed\ntime between the first and last chunk. Standard workloads are tested roughly every three hours; a\n10-concurrent-request parallel-load workload runs once a day, and the 100k-input-token workload\nruns once a week. Reported figures are the median (P50) across the trailing 72 hours, except the\n100k workload, which uses a trailing 14-day median. Tokens are counted with OpenAI's `o200k_base`\ntokenizer for every model, specifically so speeds are comparable across models with different\nnative tokenizers. Figures represent a model's first-party API where one exists, or the median\nacross providers when it does not.\n\n## Dataset and licence\n\nThere is no fixed benchmark dataset: every test run uses a freshly generated prompt built from\nlong-form source content paired with a task \u2014 summarization, question generation, comparative\nanalysis, translation or visual-artifact generation \u2014 sized to hit the target input-token budget\nfor that workload. Artificial Analysis publishes its prompt-generation approach and a downloadable\nset of reasoning-token-estimation prompts on its methodology page. No formal licence for this\nprompt set or for Artificial Analysis's own speed measurements was established from a source read\nfor this page.\n\n## Who publishes it\n\nArtificial Analysis, the same independent benchmarking company behind the Intelligence Index,\ndesigns and runs this measurement itself against providers' public endpoints; no third-party or\nacademic co-author was credited on the methodology page read for this page.\n\n## Lineage\n\nOutput Speed has no separate predecessor or successor of its own; it is one of several performance\nmetrics \u2014 alongside time to first token, total response time for 100 output tokens, and\nend-to-end response time \u2014 documented on the same Performance Benchmarking methodology page and\nrevised together, most recently in a March 2026 update that changed the default workload from\n1,000 to 10,000 input tokens and refreshed the prompt set. It belongs to the `artificial_analysis`\nfamily alongside `artificial_analysis_quality_index`. Artificial Analysis's related Endpoint\nAccuracy Index, which scores how much of a model's accuracy a specific provider endpoint\npreserves, is a distinct, accuracy-focused metric with no page in this repository yet.\n\n## Saturation and contamination\n\nAn uncapped throughput measurement does not saturate the way an accuracy score does: there is no\nmaximum tokens-per-second ceiling, and specialised inference hardware providers continue to post\nmaterially higher output speeds than typical general-purpose API hosting, so the field keeps\nseparating rather than converging. Contamination in the train/test sense does not apply, since\nthis measures live infrastructure performance rather than a model's response to a knowable\nquestion set. The comparable integrity risk is a provider serving Artificial Analysis's own\ntraffic differently from ordinary customer traffic; Artificial Analysis addresses this with\npublished Integrity Terms that bar detecting and special-casing its traffic, and it reserves the\nright to re-test from independent accounts, withhold results, or delist a provider that fails\ncompliance checks.\n\n## How to run it\n\nThere is no independent harness a third party runs; Artificial Analysis tests every endpoint\nitself from a primary server in Google Cloud's `us-central1-a` zone, using the official OpenAI\nPython library for OpenAI-compatible APIs and each provider's recommended client otherwise.\nBecause time-to-first-token specifically is sensitive to network distance from that single test\nlocation, Artificial Analysis's own methodology page names server location as a known limitation\nof the comparison.\n\n## Reading the numbers\n\nA high Output Speed number means a model's API streams answer tokens quickly once it starts\nresponding \u2014 it says nothing about whether those tokens are correct, so read it alongside a\ncapability figure such as `artificial_analysis_quality_index` rather than in isolation. Speed for\nthe same model can vary a great deal by provider and hosting configuration, which is why\nArtificial Analysis reports it per endpoint rather than once per model; check time to first token\nseparately, since a model can stream quickly once started but still feel slow to a user if it\ntakes a long time to begin. Prefer figures measured at the input-token length closest to your own\nreal workload, since both speed and time-to-first-token shift with prompt length.\n",
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 "page": {
  "aliases": [
   "Artificial Analysis Speed Index",
   "AA Output Speed",
   "Output Tokens per Second"
  ],
  "category": "composite",
  "contamination": {
   "note": "Not applicable in the train/test sense: this measures live infrastructure performance rather than a model's response to a knowable question set, so there is nothing for a model to have memorized in advance. The comparable integrity risk is a provider serving Artificial Analysis's own traffic differently from ordinary traffic, which Artificial Analysis addresses through published Integrity Terms rather than dataset controls.\n",
   "risk": "low"
  },
  "dataset": {
   "languages": [],
   "license": "",
   "modalities": [
    "text",
    "image"
   ],
   "public_test_set": null,
   "size": null,
   "size_note": "Not a fixed dataset: prompts are freshly generated for each test run from a mix of long-form source content (such as articles) paired with a task \u2014 summarization, question generation, comparative analysis, translation or visual-artifact generation \u2014 sized to hit the target input-token budget for that workload (about 1k, 10k or 100k tokens, or a vision workload).\n",
   "splits": "",
   "url": "https://artificialanalysis.ai/downloads/methodology/performance-prompts.xlsx"
  },
  "freshness": {
   "researched": "2026-09-08",
   "researched_by": "sonnet-5 agent, batch 1b, slice J",
   "reviewed": "",
   "reviewed_by": ""
  },
  "harness": {
   "bigbench": "",
   "helm": "",
   "inspect_evals": "",
   "lm_eval": "",
   "opencompass": "",
   "other": "No independent harness. Artificial Analysis tests each endpoint itself, roughly every 3 hours for standard workloads (once daily for a 10-parallel-prompt load test, once weekly for the 100k-token workload), using the official OpenAI Python client for OpenAI-compatible APIs and each provider's own recommended client otherwise, from a primary server in Google Cloud's us-central1-a zone.\n"
  },
  "id": "artificial_analysis_speed_index",
  "last_updated": "2026-03",
  "leaderboard_url": "https://artificialanalysis.ai/",
  "lineage": {
   "family": "artificial_analysis",
   "predecessor": "",
   "successors": [],
   "variants": []
  },
  "measures": "This page documents Artificial Analysis's Output Speed metric \u2014 the throughput figure the publisher headlines under \"Speed\" on its own site \u2014 as this repository's artificial_analysis_speed_index. It is a pure performance measurement, not a capability or correctness score: Artificial Analysis sends a live prompt to a model's public API and times how many tokens per second stream back after the first token arrives. Unlike the Intelligence Index, Artificial Analysis does not publish a single blended \"Speed Index\" combining multiple timing metrics into one number; Output Speed is reported alongside, not merged with, separate metrics for time to first token and end-to-end response time. Workloads vary by input length (about 1,000, 10,000 or 100,000 input tokens, plus a vision workload of one megapixel image and roughly 1,000 text tokens) because both time-to-first-token and output speed itself shift with prompt length and technique such as speculative decoding.\n",
  "metric": {
   "baseline_note": "No fixed maximum or baseline: output speed is an uncapped, provider- and hardware-dependent throughput measurement, not a score against a fixed answer key. Higher is better because it means faster generation, in contrast with Artificial Analysis's latency metrics (time to first token, end-to-end response time), which are lower-is-better.\n",
   "direction": "higher_is_better",
   "human_baseline": null,
   "max_score": null,
   "name": "Output Speed (output tokens/second)",
   "random_baseline": null,
   "unit": "tokens/second"
  },
  "name": "Artificial Analysis Output Speed",
  "page_kind": "benchmark",
  "paper": {
   "arxiv": "",
   "title": "",
   "url": "",
   "year": null
  },
  "publisher": {
   "authors": [],
   "org": "Artificial Analysis",
   "url": "https://artificialanalysis.ai"
  },
  "released": "",
  "repo_url": "",
  "saturation": {
   "as_of": "",
   "note": "An uncapped throughput measurement has no fixed ceiling; specialised inference hardware providers continue to post materially higher output speeds than typical general-purpose API hosting, so the field keeps separating rather than converging. No single current top-speed figure was captured in numeric form from a source read for this page.\n",
   "status": "open",
   "top_score": null
  },
  "sources": [
   {
    "accessed": "2026-09-08",
    "title": "Artificial Analysis Language Model API Performance Benchmarking Methodology",
    "url": "https://artificialanalysis.ai/methodology/performance-benchmarking"
   },
   {
    "accessed": "2026-09-08",
    "title": "Artificial Analysis (homepage, Speed & Latency section)",
    "url": "https://artificialanalysis.ai/"
   }
  ],
  "status": "active",
  "subcategory": "inference throughput / output speed",
  "summary": "Artificial Analysis's live-measured output speed for a model's API: tokens generated per second, a performance measure, not a correctness or quality score.",
  "tags": [
   "composite",
   "performance",
   "throughput",
   "artificial-analysis"
  ],
  "task_format": "Live API calls: Artificial Analysis sends standardized, freshly generated prompts of a fixed input-token length to a model's public endpoint and streams the response, timing token arrivals to derive tokens-per-second and latency figures.\n"
 }
}