{ "schema": "arena-speed-lab/1", "registryVersion": "2026-10-07T19:24:57Z", "note": "Speed lab. games: equal-skill bots that differ only in the delay of their answers; a cell is the row delay r against the column delay c, in ms, with the row bot's points share (win 1, draw 0.5) and a Wilson 95% interval, and the cell (c, r) is its complement. presets: the latencies we can cite for each model and machine. latencyModels: call time = a + b x input tokens, fitted on the clean latency pass. macToGpu: our Mac median over the Decision Index one-GPU median.", "delaysMs": [ 0, 40, 80, 150, 300, 600, 1200, 2400 ], "games": { "pong": { "player": "perfect", "gamesPerPair": 40, "cells": [ { "r": 0, "c": 0, "n": 40, "share": 0.5, "lo": 0.352, "hi": 0.648, "w": 20, "d": 0, "l": 20 }, { "r": 0, "c": 40, "n": 40, "share": 0.7, "lo": 0.5457, "hi": 0.81925, "w": 28, "d": 0, "l": 12 }, { "r": 0, "c": 80, "n": 40, "share": 0.8, "lo": 0.65243, "hi": 0.895, "w": 32, "d": 0, "l": 8 }, { "r": 0, "c": 150, "n": 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"l": 41 }, { "r": 2400, "c": 1200, "n": 60, "share": 0.4, "lo": 0.28569, "hi": 0.52634, "w": 17, "d": 14, "l": 29 }, { "r": 2400, "c": 2400, "n": 60, "share": 0.5, "lo": 0.37735, "hi": 0.62265, "w": 19, "d": 22, "l": 19 } ] } }, "presets": [ { "model": "jev", "label": "Hosted, from Houston", "ms": 143.1, "kind": "hosted", "source": "Arena latency pass: 120 seeded items, one call at a time, nothing else running; modeled at the median of 830 input tokens from 120 answered calls (a 139.525 ms + 0.004326 ms per token); recorded median 137 ms", "includes": "The network round trip from Houston, Texas, and the model. Median 830 input tokens per call. Poor fit (R squared 0.0038): input size explains little of the call time.", "machine": "hosted-houston", "basis": "measured" }, { "model": "jev", "label": "Hosted, third-party lab", "ms": 524.1, "kind": "hosted", "source": "Decision Index 0.2.1, snapshot 2026-10-01, lab median, hosted API, third party", "includes": "The network round trip from the board's lab, and the model.", "machine": "hosted-third-party-lab", "basis": "anchor" }, { "model": "jev", "label": "OpenRouter (TypeSafe)", "ms": 170, "kind": "gateway", "source": "OpenRouter, accessed 2026-10-06, p50 for the TypeSafe endpoint, third party", "includes": "The gateway's path and the provider's service, as OpenRouter measures it over its own traffic. It changes daily.", "machine": "openrouter-typesafe", "basis": "anchor" }, { "model": "clef", "label": "This Mac (M3 Ultra)", "ms": 1880.6, "kind": "mac", "source": "Arena latency pass: 120 seeded items, one call at a time, nothing else running; modeled at the median of 577 input tokens from 115 answered calls (a 120.38 ms + 3.05069 ms per token); recorded median 1919 ms", "includes": "The model alone, no network. A Mac Studio, Apple M3 Ultra, 96 GB. Median 577 input tokens per call.", "machine": "mac-m3-ultra", "basis": "measured" }, { "model": "clef", "label": "One RTX PRO 6000", "ms": 209.3, "kind": "gpu", "source": "Decision Index 0.2.1, snapshot 2026-10-01, one-GPU median, third party", "includes": "The model alone, no network, on one NVIDIA RTX PRO 6000 in the board's harness. The workload and its token sizes are the board's, not ours.", "machine": "one-rtx-pro-6000", "basis": "anchor" }, { "model": "clef", "label": "OpenRouter (Workers AI)", "ms": 500, "kind": "gateway", "source": "OpenRouter, accessed 2026-10-06, p50 for the Workers AI endpoint, third party", "includes": "The gateway's path and the provider's service, as OpenRouter measures it over its own traffic. It changes daily.", "machine": "openrouter-workers-ai", "basis": "anchor" }, { "model": "clef-flash", "label": "This Mac (M3 Ultra)", "ms": 557.4, "kind": "mac", "source": "Arena latency pass: 120 seeded items, one call at a time, nothing else running; modeled at the median of 577 input tokens from 120 answered calls (a 54.421 ms + 0.871633 ms per token); recorded median 566 ms", "includes": "The model alone, no network. A Mac Studio, Apple M3 Ultra, 96 GB. Median 577 input tokens per call.", "machine": "mac-m3-ultra", "basis": "measured" }, { "model": "clef-flash", "label": "One RTX PRO 6000", "ms": 38.8, "kind": "gpu", "source": "Decision Index 0.2.1, snapshot 2026-10-01, one-GPU median, third party", "includes": "The model alone, no network, on one NVIDIA RTX PRO 6000 in the board's harness. The workload and its token sizes are the board's, not ours.", "machine": "one-rtx-pro-6000", "basis": "anchor" }, { "model": "clef-flash", "label": "OpenRouter (Workers AI)", "ms": 590, "kind": "gateway", "source": "OpenRouter, accessed 2026-10-06, p50 for the Workers AI endpoint, third party", "includes": "The gateway's path and the provider's service, as OpenRouter measures it over its own traffic. It changes daily.", "machine": "openrouter-workers-ai", "basis": "anchor" }, { "model": "lev", "label": "This Mac (M3 Ultra)", "ms": 414.7, "kind": "mac", "source": "Arena latency pass: 120 seeded items, one call at a time, nothing else running; modeled at the median of 1076 input tokens from 120 answered calls (a 133.219 ms + 0.261573 ms per token); recorded median 428 ms", "includes": "The model alone, no network. A Mac Studio, Apple M3 Ultra, 96 GB. Median 1076 input tokens per call.", "machine": "mac-m3-ultra", "basis": "measured" }, { "model": "lev", "label": "One RTX PRO 6000", "ms": 70.8, "kind": "gpu", "source": "Decision Index 0.2.1, snapshot 2026-10-01, one-GPU median, third party", "includes": "The model alone, no network, on one NVIDIA RTX PRO 6000 in the board's harness. The workload and its token sizes are the board's, not ours.", "machine": "one-rtx-pro-6000", "basis": "anchor" }, { "model": "kev-4b", "label": "This Mac (M3 Ultra)", "ms": 329.9, "kind": "mac", "source": "Arena latency pass: 120 seeded items, one call at a time, nothing else running; modeled at the median of 462 input tokens from 120 answered calls (a 101.666 ms + 0.49394 ms per token); recorded median 307 ms", "includes": "The model alone, no network. A Mac Studio, Apple M3 Ultra, 96 GB. Median 462 input tokens per call. Poor fit (R squared 0.2742): input size explains little of the call time.", "machine": "mac-m3-ultra", "basis": "measured" }, { "model": "kev-4b", "label": "One RTX PRO 6000", "ms": 52.1, "kind": "gpu", "source": "Decision Index 0.2.1, snapshot 2026-10-01, one-GPU median, third party", "includes": "The model alone, no network, on one NVIDIA RTX PRO 6000 in the board's harness. The workload and its token sizes are the board's, not ours.", "machine": "one-rtx-pro-6000", "basis": "anchor" }, { "model": "kev-4b", "label": "OpenRouter (SiliconFlow)", "ms": 4080, "kind": "gateway", "source": "OpenRouter, accessed 2026-10-06, p50 for the SiliconFlow endpoint, third party", "includes": "The gateway's path and the provider's service, as OpenRouter measures it over its own traffic. It changes daily.", "machine": "openrouter-siliconflow", "basis": "anchor" }, { "model": "laya", "label": "This Mac (M3 Ultra)", "ms": 44.8, "kind": "mac", "source": "Arena latency pass: 120 seeded items, one call at a time, nothing else running; modeled at the median of 474 input tokens from 120 answered calls (a 11.32 ms + 0.070535 ms per token); recorded median 43 ms", "includes": "The model alone, no network. A Mac Studio, Apple M3 Ultra, 96 GB. Median 474 input tokens per call.", "machine": "mac-m3-ultra", "basis": "measured" }, { "model": "laya", "label": "One RTX PRO 6000", "ms": 5.8, "kind": "gpu", "source": "Decision Index 0.2.1, snapshot 2026-10-01, one-GPU median, third party", "includes": "The model alone, no network, on one NVIDIA RTX PRO 6000 in the board's harness. The workload and its token sizes are the board's, not ours.", "machine": "one-rtx-pro-6000", "basis": "anchor" }, { "model": "julia-1", "label": "This Mac (M3 Ultra)", "ms": 14.2, "kind": "mac", "source": "Arena latency pass: 120 seeded items, one call at a time, nothing else running; modeled at the median of 444 input tokens from 120 answered calls (a 5.723 ms + 0.019139 ms per token); recorded median 13 ms", "includes": "The model alone, no network. A Mac Studio, Apple M3 Ultra, 96 GB. Median 444 input tokens per call. Poor fit (R squared 0.3055): input size explains little of the call time.", "machine": "mac-m3-ultra", "basis": "measured" }, { "model": "julia-1", "label": "One RTX PRO 6000", "ms": 5.8, "kind": "gpu", "source": "Decision Index 0.2.1, snapshot 2026-10-01, one-GPU median, third party", "includes": "The model alone, no network, on one NVIDIA RTX PRO 6000 in the board's harness. The workload and its token sizes are the board's, not ours.", "machine": "one-rtx-pro-6000", "basis": "anchor" } ], "latencyModels": [ { "model": "jev", "kind": "hosted", "a": 139.53, "b": 0.0043, "r2": 0.0038, "n": 120, "medianTokens": 830, "medianMs": 137, "floorMs": 108 }, { "model": "clef", "kind": "local", "a": 120.38, "b": 3.0507, "r2": 0.999, "n": 115, "medianTokens": 577, "medianMs": 1919 }, { "model": "clef-flash", "kind": "local", "a": 54.42, "b": 0.8716, "r2": 0.9544, "n": 120, "medianTokens": 577, "medianMs": 566 }, { "model": "lev", "kind": "local", "a": 133.22, "b": 0.2616, "r2": 0.6848, "n": 120, "medianTokens": 1076, "medianMs": 428 }, { "model": "kev-4b", "kind": "local", "a": 101.67, "b": 0.4939, "r2": 0.2742, "n": 120, "medianTokens": 462, "medianMs": 307 }, { "model": "laya", "kind": "local", "a": 11.32, "b": 0.0705, "r2": 0.5173, "n": 120, "medianTokens": 474, "medianMs": 43 }, { "model": "julia-1", "kind": "local", "a": 5.72, "b": 0.0191, "r2": 0.3055, "n": 120, "medianTokens": 444, "medianMs": 13 } ], "macToGpu": [ { "model": "clef", "macMs": 1919, "gpuMs": 209.3, "ratio": 9.17 }, { "model": "clef-flash", "macMs": 566, "gpuMs": 38.8, "ratio": 14.59 }, { "model": "lev", "macMs": 428, "gpuMs": 70.8, "ratio": 6.05 }, { "model": "kev-4b", "macMs": 307, "gpuMs": 52.1, "ratio": 5.89 }, { "model": "laya", "macMs": 43, "gpuMs": 5.8, "ratio": 7.41 }, { "model": "julia-1", "macMs": 13, "gpuMs": 5.8, "ratio": 2.24 } ], "spread": { "min": 2.24, "max": 14.59 }, "caveats": [ "The speed grid is bot-only: no model was called. Both bots in a cell have the same skill, so a result shows what the delay of the answer costs, not how good a model is.", "The bots do not allow for their own delay. A late bot plays on a board that is out of date. A player that predicted the board could lose less.", "In Tron and Snake the world moves in 250 ms ticks, so a delay of 40, 80 or 150 ms lands one tick later in all three cases. Those three rows measure one delay, and any difference between them is noise. Pong ticks at 16.7 ms, so every delay differs.", "In Tron a bot with a delay of 600 ms or more mostly drives straight until it crashes (mean survival 16.27 to 16.41 ticks at 600, 1200 and 2400 ms). The bots are close to blind there, and the order of the rows is not monotone: 67 of 224 row pairs are out of order, 29 by more than 2 standard errors. Read the Tron grid as a rough picture. In Pong and Snake the order holds within noise.", "Games come in pairs with the same seed and the sides swapped. The intervals treat the games as independent, so they are approximate.", "Jev's call time does not depend on input size (R squared 0.0038 over 120 calls of 338 to 2363 tokens): it is the network, not compute. Local models grow with input size, from R squared 0.2742 (kev-4b) to 0.999 (clef). The lines are a guide fitted on the clean latency pass, not a promise.", "The Mac-to-GPU ratio runs from 2.24x to 14.59x between models. It compares our latency pass with the third-party Decision Index medians, whose workload and token sizes are not ours, so it is indicative only. One multiplier does not turn Mac times into GPU times, so recorded Mac times are not scaled; the grid shows outcomes across the whole delay range instead.", "Decision Index and OpenRouter numbers were not measured by us. OpenRouter medians change daily.", "A chip for a machine we measured is the modeled call time at that model's median input size (the line of its latency model); the recorded median is in its source line. A chip for a third-party number is one median, whatever the input size. The latency registry (clock-registry.json) holds every model, machine and ratio." ] }