The LLM Market Benchmarks are a transparency experiment. They measure how well frontier AI models forecast crypto markets when every answer is validated and traded under identical, mechanical rules. This page documents exactly what the models see, how their answers are validated, and how the simulation turns those answers into an equity curve. Nothing here is financial advice, and past results do not predict future performance.
Seven frontier models forecast the same five coins on a fixed schedule. Every model sees the same inputs at the same time, so the leaderboard compares reasoning, not data access. Each answer is validated for structure, then paper-traded on Binance USDT-M futures with a fixed notional and a fixed starting bank. Two layers are reported and kept separate: accuracy scoring (did the model answer well and directionally right) and the trading simulation (what a mechanical trader would have earned following the answer).
The public benchmark launched on 15 July 2026 with a full statistics reset. All seven lineup slots have run since day one; a slot upgraded to a newer model version mid-season keeps its full history under the successor's name, marked in the table below.
| Model | Joined |
|---|---|
| ChatGPT 5.6 Sol | 15 Jul 2026 (launch) |
| Claude Opus 5 | 15 Jul 2026 (as Claude Opus 4.8) · Claude Opus 5 since 30 Jul 2026 |
| Claude Fable 5 | 15 Jul 2026 (launch) |
| DeepSeek V4 Pro | 15 Jul 2026 (launch) |
| Gemini 3.1 Pro | 15 Jul 2026 (launch) |
| Qwen 3.8 Max | 15 Jul 2026 (as Qwen 3.7 Max) · Qwen 3.8 Max since 5 Aug 2026 |
| Grok 4.5 | 15 Jul 2026 (launch) |
The benchmark covers the five largest coins — BTC, ETH, SOL, BNB, XRP — forecast on every horizon. A wider set of higher-beta names ran on the weekly and monthly cadence until 27 July 2026; their history is preserved unchanged (see the changelog below), and the universe is reserved for future expansion.
For each coin the model answers across five forecast horizons (FH). Each horizon is queried once per available data timeframe (TF) — a separate request per timeframe, so the model never mixes resolutions inside one answer:
| Forecast horizon | Timeframe(s) requested |
|---|---|
| 1H | 1H |
| 4H | 4H, 1H |
| 1D | 1D, 4H |
| 1W | 1W, 1D |
| 1M | 1M, 1W |
Every request carries the same, fully disclosed payload for the single timeframe being asked:
A single forecast is a structured answer — a side (long / short / sideways), a take-profit %, a stop-loss %, and a confidence score — never free text.
The payload is the raw market data exactly as the Analyze product sends it — no model-specific tuning of any kind. One shared payload per slot means every model is answering the same question.
A strict validator checks every answer against the required schema. If an answer is malformed or incomplete, the model is asked again with the identical payload, up to two retries. If it still fails, the answer is marked invalid and counted against the model — a model is rewarded for answering clearly and consistently, not just for being directionally right.
Slot coverage is tracked continuously and shown live in the leaderboard-page counters; incomplete slots are never backfilled silently — every gap stays visible in the data.
Valid forecasts are paper-traded under one mechanical rule set — no discretion, no overrides:
From these settled trades we derive win rate, gross and net PnL, fees paid, max drawdown, and a running equity curve for each model.
The leaderboard is ranked under two frozen configs. Neither can be changed in the sandbox.
Per-forecast mode treats each forecast as an independent trade opened at entry and closed at its own TP, SL or horizon expiry.
Position mode maintains a single running position per coin and reconciles each new forecast against it:
Both official configs trade the model's own take-profit and stop-loss as a single leg — a trade closes at the model's TP, its SL, or horizon expiry, exactly as forecast. Stepped take-profits and break-even exist only as sandbox experiment knobs and never affect the official rankings.
Accuracy scoring (validity and directional correctness) and the trading simulation (equity from mechanical fills) are computed and displayed independently. A model can score well on accuracy and still trade poorly, or vice-versa; keeping the layers separate is deliberate.
The benchmark is a research and transparency tool. It is not financial advice. All fills are simulated — there is no real order flow, and real-world slippage, funding and liquidity are not fully modelled. Past simulated performance does not predict future results.
Plain: MarketMania (2026). MarketMania LLM Market Benchmarks. https://marketmania.ai/benchmarks
@misc{marketmania_llm_benchmarks_2026,
title = {MarketMania LLM Market Benchmarks},
author = {MarketMania},
year = {2026},
howpublished = {\url{https://marketmania.ai/benchmarks}},
note = {Live benchmark of frontier LLMs forecasting crypto markets,
paper-traded on Binance USDT-M futures}
}Programmatic access: the public API base is /api/v1/benchmarks/ (leaderboard, meta and CSV/JSON export are public; the sandbox and ensemble endpoints require a free account). See the public API docs for the full schema, or reach us at contact@marketmania.ai.