Charts
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Daily-tracked signals from across the AI capability race — model trajectories, enterprise routing shifts, benchmark events, training compute. New charts added as the data moves.
The Frontier's Lead Lives on the Hard Tests
Read the article →The frontier's lead over the pack, by benchmark difficulty, week by week
Source: Synopticon score store · 2026-08-15. Model availability from registry release dates (score date as fallback, so a slice of benchmarks enters when we first scraped them, visible as the jump in early 2026). SDI is each benchmark's difficulty in the current fit, held constant, so nothing rescales; harder benchmarks enter as new dots on the right (the hard band grows from 10 to 54). Above SDI 168 too few models score to be reliable, so the axis stops there. Frames before 2025 are too thin to show. Standalone player, MP4 and GIF: synopticonresearch.com/embed/frontier-vs-pack/ (the player has a copy-embed button).
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The Harness Moves the Score
Read the article →Median harness Δ 15.6 pp, max 48 pp, on the same model
Sources: HAL leaderboards · swe-bench/experiments.
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The gap varies by benchmark, biggest where a purpose-built scaffold faces a generic agent
Sources: HAL leaderboards · swe-bench/experiments.
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y > x: the specialised scaffold wins, on the same model
Source: HAL leaderboards (GAIA, SWE-bench Verified Mini, TAU-bench Airline, CORE-Bench Hard, ScienceAgentBench).
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Claude Code adds 18–36 pp over a generic agent on the same Claude model
Source: HAL CORE-Bench Hard. Same model + Claude Code (Anthropic) vs same model + CORE-Agent (Princeton).
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Cost efficiency: harness Δ vs USD per task, log-scale x
Source: HAL leaderboards,
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COST (USD) column. 43 of 64 pairs have published cost for both harnesses.Smarter models do not get a smaller harness lift
AAII source: Artificial Analysis snapshot 2026-05-09. Harness Δ from this dataset (38 of 64 pairs matched).
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The gap is widening, not narrowing
Release dates from Artificial Analysis and model release notes.
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The premium grew from +20 pp to +23 pp
Sources: HAL leaderboards (Princeton) · swe-bench/experiments. Smoothing: LOWESS, 60% span over day-precise release dates.
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Two benchmarks where the harness is winning, one where the model is
Sources: HAL leaderboards (Princeton) · swe-bench/experiments. Bootstrap CIs computed on this dataset (n = 4 to 14 per benchmark).
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How Public Markets Already Own the AI Frontier
Read the article →Public exposure to Anthropic
Source: Synopticon ownership cards. Stake values gross of tax leakage.
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Public exposure to OpenAI
Source: Synopticon ownership cards. Microsoft fair value; carrying value materially lower (see next section).
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How Long Can Claude Mythos Work Alone?
Read the article →METR horizon forecast
View in article →Reasoning-era models: Mythos predicted at ~16-hour task complexity
Data: METR-Horizon-v1.1 × Self-reported IRT (Ho et al.). Band = 90% bootstrap CI (400 resamples). Y-axis = human-equivalent task duration.
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Full dataset (n=19): linear and quadratic diverge on older models
View in article →At the 80% reliability bar, Mythos predicted at ~2–3 task-hours
View in article →Six benchmarks predict 5–18 task-hours (median 10.5h)
View in article →Moonshot Built the Engine. Cursor Sold the Car.
Read the article →API pricing across coding models
OpenAI: openai.com/api/pricing ·
Anthropic: docs.anthropic.com ·
DeepSeek: api-docs.deepseek.com ·
Fireworks: fireworks.ai/kimi ·
Cursor: cursor.com/docs/models-and-pricing ·
Google: ai.google.dev
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Competitive on coding benchmarks, despite the price gap
K2.5: HuggingFace,
arXiv 2602.02276 ·
Composer 2: VentureBeat ·
SWE-bench: swebench.com ·
Terminal-Bench: tbench.ai ·
LiveCodeBench: livecodebench.github.io
Caveats: K2.5 benchmarks are self-reported from model card/paper, not yet on public SWE-bench or Aider leaderboards. SWE-bench Verified excluded: OpenAI retired it in February 2026 citing contamination.
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Caveats: K2.5 benchmarks are self-reported from model card/paper, not yet on public SWE-bench or Aider leaderboards. SWE-bench Verified excluded: OpenAI retired it in February 2026 citing contamination.
Each step in the pipeline adds measurable performance
K2 Instruct: HuggingFace model card,
arXiv 2507.20534,
tbench.ai leaderboard ·
K2.5: arXiv 2602.02276 ·
Composer 2: Cursor,
VentureBeat
All scores confirmed from model cards, tech reports, or public leaderboards. K2 Terminal-Bench 2.0 score (27.8%) from tbench.ai (Terminus 2 agent). SWE-bench Multilingual K2 score (47.3%) from K2 tech report (agentic mode).
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All scores confirmed from model cards, tech reports, or public leaderboards. K2 Terminal-Bench 2.0 score (27.8%) from tbench.ai (Terminus 2 agent). SWE-bench Multilingual K2 score (47.3%) from K2 tech report (agentic mode).
How model mix would affect margin (thought experiment)
Cursor ARR: TechCrunch,
SaaStr ·
Pricing: Cursor,
Anthropic ·
SaaS benchmark: Bessemer
All figures illustrative. Actual cost structure not publicly disclosed.
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All figures illustrative. Actual cost structure not publicly disclosed.