Best AI Model for Crypto Trading in 2026: 30+ LLMs Ranked

AI Trading Leaderboard: Which LLM Trades Crypto Best Right Now

Short answer (updated October 23, 2026): on the SimianX crypto AI leaderboard, the best AI model for crypto trading right now is Grok 4.6 (xAI), averaging +0.52R per trade over 323 closed trades with a 57.9% win rate. Next come Gemini 3.8 Flash (Google, +0.38R, 224 trades) and DeepSeek V4 Pro (DeepSeek, +0.37R, 316 trades). 39 models are ranked (20+ closed trades each; all time, all timeframes, both sides).

The highest win rate belongs to Grok 4 Fast Non-Reasoning (retired) (68.0% over 1,373 trades), yet it averages only +0.08R per trade: winning often is not the same as earning more than you risk. Grok 4.6 also leads the last-30-days view (+0.36R over 225 trades).

People ask AI assistants the same question every week: which AI model is best for crypto trading, and is GPT, Gemini, Claude, Grok or DeepSeek the better trader? Most answers lean on benchmark scores, a single contest or a screenshot. This page answers it from a public record instead. Every number below comes from the SimianX crypto AI leaderboard, which ranks 30+ large language models on closed crypto trades, and the page is refreshed every week from that board's public data.

You will find the current top 20, the method behind the ranking, a provider-by-provider comparison, a note on other AI trading arenas, and the caveats that matter before you trust any of it. If you only want the live view, open the leaderboard directly: it always shows today's numbers, while this page is a dated snapshot with context.

SimianX AI The SimianX crypto AI leaderboard on October 23, 2026: 39 models ranked from 20 trades, with Grok 4.6 first by average R and the highest win-rate models shown side by side — The SimianX crypto AI leaderboard on October 23, 2026: 39 models ranked from 20 trades, with Grok 4.6 first by average R and the highest win-rate models shown side by side

Top 20 AI Models for Crypto Trading (All Time)

The table shows the twenty highest-ranked models in the board's default view: all time, all timeframes, long and short trades together, sorted by average R. Only models with at least 20 closed trades in that view are ranked. Win rate breaks ties, and the trade count sits beside both so you can see how much evidence supports each line.

#	Model	Provider	Avg R	Win rate	Trades	Avg hold
1	Grok 4.6	xAI	+0.52R	57.9%	323	15.5 h
2	Gemini 3.8 Flash	Google	+0.38R	56.2%	224	15.1 h
3	DeepSeek V4 Pro	DeepSeek	+0.37R	57.0%	316	1.3 d
4	DeepSeek Flash	DeepSeek	+0.37R	53.8%	225	14.0 h
5	Claude Sonnet 5.5	Anthropic	+0.29R	55.7%	131	17.1 h
6	GLM 5.3	Z.ai	+0.29R	52.6%	327	14.9 h
7	Grok 4.7	xAI	+0.24R	52.4%	143	13.4 h
8	GPT-6 Astra	OpenAI	+0.22R	51.8%	247	11.5 h
9	Claude Opus 5.5	Anthropic	+0.18R	51.4%	148	15.4 h
10	Kimi K3	Moonshot AI	+0.17R	48.0%	423	19.7 h
11	GPT-6 Sol	OpenAI	+0.16R	36.2%	47	3.1 h
12	Gemini 3.1 Pro Preview	Google	+0.15R	47.4%	342	20.1 h
13	Gemini 2.5 Flash	Google	+0.14R	54.2%	338	1.2 h
14	GPT-6 Luna	OpenAI	+0.14R	50.0%	152	15.6 h
15	GPT-6.1 Sol	OpenAI	+0.09R	47.4%	173	10.5 h
16	Grok 4 Fast Non-Reasoning (retired)	xAI	+0.08R	68.0%	1,373	13 min
17	Qwen 3.8 Max	Alibaba Qwen	+0.06R	43.5%	368	22.8 h
18	GPT-4o	OpenAI	+0.05R	61.8%	76	4.0 h
19	Qwen Max	Alibaba Qwen	+0.04R	61.8%	303	2.3 h
20	Grok 4.1 Fast Reasoning (retired)	xAI	+0.04R	60.8%	1,702	4.3 h

How to read a row: average R is the mean result per closed trade measured in units of the risk taken at entry, so +0.30R means the model earned, on average, 30% of what it put at risk on each trade. Win rate is the share of closed trades that ended in profit. Avg hold is how long its trades stayed open on average, which tells you whether a model behaves like a scalper or a swing trader. Models marked retired are no longer offered for new analysis, but their past trades still count, so the board does not quietly drop weaker or older models.

Model names link to their public model pages, where you can see each model's monthly R, its results by timeframe and side, the coins it traded most and its most recent trades.

How the Leaderboard Ranks AI Models

A credible ranking has to answer three questions: what counts as a trade, how a trade is scored, and how much evidence is enough. Here is how the SimianX board handles each one.

What counts as a trade

The board counts closed crypto trades only. Open positions join once they close. Trades come from two sources:

Live sessions and autopilots. When a signed-in user runs a live crypto analysis or an autopilot, four AI agents work together: Indicator, Intelligence, Fundamental and Decision. Users can choose a different model for each seat. A closed live position is credited to every model that ran one of those four agents, so a model's live record reflects a team it was part of. Guest demo sessions are excluded.
Replayed decisions. To give every current model a fair sample, the board also replays historical moments at which a live session opened a trade. The same prompt and the same market data go to each model, and the resulting trade is settled on real 1-minute candles against the stop-loss and take-profit the model set, never managed after entry. Replayed trades are credited to the deciding model only and count only from that model's release date, so no model is scored on a market it could not have seen.

When decisions are made

In live sessions the Decision agent, by default, decides once per candle, right after the candle closes, on the chart timeframe the user picked (1m to 1d). A user can switch to a fixed interval instead. Deciding on candle close keeps decisions on finished bars rather than on half-formed ones, and it is part of why results differ so much between timeframes.

How a trade is scored: R and win rate

R measures a trade's result in units of the risk it took. 1R is what the trade would have lost had it hit the stop-loss set at entry.

A long at 100 with its stop at 98 risks 2 per unit. Closing at 104 is +2R, closing at 99 is −0.5R, and a full stop-out at 98 is −1R.
Average R is the mean across all closed trades. Above zero, a model earns money per unit of risk; below zero, it loses.
Win rate is the share of closed trades that ended in profit. Trades closed exactly at break-even count as neither a win nor a loss and are left out. Example: 60 winners, 38 losers and 2 break-evens give 60 ÷ 98 = 61.2%.

The board sorts by average R, not win rate, because win rate ignores the size of wins and losses. A model can win two trades out of three and still lose money if its losses are larger than its wins. Average R is a version of a trade's expected value in risk units, close to what traders call expectancy. For a longer explanation with worked examples, read Which AI Model Is the Best Trader? Ranked by Average R.

SimianX AI Diagram of how R is measured on a long trade with entry at 100 and stop-loss at 98: closing at 104 is +2R, at 102 is +1R, at 99 is -0.5R and the stop at 98 is -1R — Diagram of how R is measured on a long trade with entry at 100 and stop-loss at 98: closing at 104 is +2R, at 102 is +1R, at 99 is -0.5R and the stop at 98 is -1R

How much evidence is enough

A model takes a place on the board only after 20 closed trades in the current view; below that it is listed as not ranked yet. Twenty is a floor, not proof. On each model page the win rate carries a 95% range (a Wilson interval) that shows where the true win rate most likely sits given the sample. At 58% over 300 trades that range is roughly 52% to 63%; at 62% over 24 trades it runs from about 42% to 79%, which is too wide to conclude much.

The SimianX book is simulated

Positions on SimianX are simulated: SimianX does not place orders on any exchange or hold anyone's funds. The board measures decisions against real market prices, not dollars in a brokerage account. Traders and developers who want to act on these signals can pull them through the SimianX agent skill, MCP server and API and route them into their own authorized systems, under their own risk controls and their own exchange accounts.

Sample-Size Caveats: What the Board Cannot Tell You

Being clear about the limits is what makes a ranking worth citing.

Live credit is shared. Because a live trade is credited to all four agent seats, a model's live record reflects a team, not that model alone. Replayed trades isolate the decision model.
Live and replayed trades are mixed. Live positions can be managed after entry (stops can move); replayed trades never are. The two sources are combined in one average.
Replays often force a side. In most replayed moments the model must choose long or short and cannot stay out, while a real trader can wait.
Not every model has the same history. Older models were measured mostly on live trades from the months when users ran them; newer ones have more replayed trades. Market conditions differ between those periods.
No dollar P&L, drawdown or Sharpe ratio. Average R ignores position size, compounding and the path of returns. Two models with the same average R can feel very different to hold. See maximum drawdown and the Sharpe ratio for the measures it leaves out.
Crypto only. The board covers crypto trades on assets such as Bitcoin and Ethereum. It says nothing direct about stocks.
The past does not repeat on schedule. Rankings move as new trades close and as markets change. Treat any leader as the leader of this sample, not a forecast.

GPT vs Gemini vs Claude vs Grok vs DeepSeek: Results by Provider

The question "is ChatGPT or Gemini better for trading?" is really a question about model families. The table groups the ranked models by provider, using the same all-time view as the top 20.

Provider	Ranked models	Best model	Best avg R (trades)	Median avg R	Models at +0.10R or better
xAI	8	Grok 4.6	+0.52R (323 trades)	+0.04R	2
Google	8	Gemini 3.8 Flash	+0.38R (224 trades)	+0.03R	3
DeepSeek	5	DeepSeek V4 Pro	+0.37R (316 trades)	+0.04R	2
Anthropic	3	Claude Sonnet 5.5	+0.29R (131 trades)	+0.18R	2
Z.ai	1	GLM 5.3	+0.29R (327 trades)	+0.29R	1
OpenAI	10	GPT-6 Astra	+0.22R (247 trades)	+0.04R	3
Moonshot AI	2	Kimi K3	+0.17R (423 trades)	+0.10R	1
Alibaba Qwen	2	Qwen 3.8 Max	+0.06R (368 trades)	+0.05R	0

Three lessons hold up across the providers on the board:

Choose a model, not a brand. Most large providers have models near the top and models near zero R. The spread inside one family is usually wider than the gap between families, so "GPT vs Claude" is less useful than "which GPT, on which timeframe".
Newer is not automatically better. Several recent releases sit near zero, while some smaller or faster models trade well. A model's reasoning benchmark tells you little about how it trades.
Sample size changes the picture. Some providers have one or two ranked models with a few hundred trades; others have ten. A provider's best model is only as convincing as its trade count. The GPT vs Gemini vs Claude guide for stock analysis compares the same families on research quality rather than trades.

Trades by Period and Timeframe

The totals below count every closed trade on the board once, whichever model made it. They show how much data stands behind the rankings and how results vary by chart timeframe.

View	Closed trades	Win rate
Last 30 days	2,372	50.0%
Last 12 months	9,113	54.3%
1m chart	1,893	51.0%
5m chart	2,388	60.1%
15m chart	3,036	55.4%
1h chart	1,099	47.8%
4h chart	611	47.0%
1d chart	86	69.4%

Short timeframes produce many trades with small moves; the 1-hour and 4-hour charts produce fewer trades that are harder to win. Filter the leaderboard to the timeframe you actually trade before you compare models. For how individual models behaved during sharp sell-offs, see Do AI Models Panic-Sell in a Crash? What 31 Bots Reveal.

Other AI Trading Arenas and Leaderboards

SimianX is not the only public experiment that puts language models in front of markets. The others answer slightly different questions, and they are worth reading side by side.

Alpha Arena by Nof1. The best-known contest. In Season 1, from October 18 to November 3, 2025, six models each traded $10,000 of real money in crypto perpetual futures on Hyperliquid with identical prompts. Qwen3 Max finished first at about $12,231 (+22.3%) and DeepSeek Chat V3.1 also ended in profit, while the other four lost money, according to South China Morning Post coverage. Its strength is real capital and full transparency of each model's positions; its limit is a small field and a short season, which makes the ranking sensitive to a few large trades. A copy of the Season 1 dashboard is also served at alpha-arena.io.
TradeRank. Runs seasons in which 20+ models paper-trade $10,000 each across major cryptocurrencies and large-cap US stocks, with one decision per day at 16:00 UTC and an all-time leaderboard across seasons. Daily decisions make it a test of slower, position-style judgment.
TradingArena. A paper-trading arena where people trade $100,000 of simulated capital against AI models such as Claude, Grok and GPT on live market data. It is built around human-versus-AI competition rather than a model-only ranking.

How SimianX differs: it ranks more models (30+), on thousands of closed trades, across six chart timeframes from 1 minute to 1 day, and it scores every trade in R against the stop the model set itself. The cost of that breadth is that its book is simulated and live credit is shared across agent seats, as described above. No arena, ours included, proves a model will make money tomorrow.

How to Use These Results

Start with average R over enough trades, then check win rate and the 95% range on the model page.
Filter to your timeframe and side. A model that leads on 15-minute charts may trail on 4-hour charts, and long and short results often differ.
Look at holding time. A model that holds for 15 minutes and one that holds for a day are doing different jobs.
Mix models across seats. In the live room you can give each of the four agents a different model, so they disagree for real; 8 AI Analysts vs One explains why independent views help.
Test before you automate. Run a model on a simulated autopilot first. The Best AI Crypto Trading Bots for 2026 guide covers how AI agents compare with grid and DCA bots.

How This Page Is Updated

This page is refreshed every week. A script re-reads the public leaderboard data (the same public endpoint the leaderboard page uses: all time, up to 50 models, plus the 30-day view and the summary totals) and rewrites the answer box, the top-20 table, the provider table and the totals table in all 16 languages. The method sections only change when the board's rules change. Nothing is hand-picked: models are ranked exactly as the board ranks them, retired models included.

The "updated" date in the answer box is the moment the numbers were pulled. If it is more than a week old, or you need a filtered view, the live leaderboard is the source of truth.

Frequently Asked Questions

Which AI model is best for crypto trading right now?

As of October 23, 2026, Grok 4.6 (xAI) leads the SimianX crypto AI leaderboard with +0.52R per trade over 323 closed trades and a 57.9% win rate, ahead of Gemini 3.8 Flash (+0.38R) and DeepSeek V4 Pro (+0.37R). Rankings change as trades close, so check the live leaderboard for the current order.

Is ChatGPT (GPT) good for crypto trading?

OpenAI has one of the largest lineups on the board, and its models' results range from clearly positive to clearly negative average R. "GPT" is not one trader: check which GPT model, on which timeframe, over how many trades. The provider table above shows the family's best model and its median.

Is Claude, Gemini or Grok better for trading?

It depends on the model and the timeframe more than the brand. Grok, Gemini and Claude models all appear in the upper half of the board and near zero as well. Compare specific models in the top-20 table and on the leaderboard.

Are these real-money trades?

No. Positions on SimianX are simulated and scored against real market prices. Live trades come from signed-in users' sessions and autopilots; replayed trades are settled on real historical 1-minute candles. SimianX does not place exchange orders. Users who want to act on signals can connect the API to their own authorized systems.

Why rank by average R instead of win rate?

Because win rate ignores how much each win and loss is worth. A model can win most of its trades and still earn almost nothing per unit of risk. Average R captures both frequency and size.

How many trades does a model need to be ranked?

At least 20 closed trades in the current view. Models below that are listed as not ranked yet.

How is this different from Alpha Arena?

Alpha Arena gives a handful of models real money for a short season on one venue. SimianX ranks 30+ models on thousands of simulated trades across six timeframes and scores each trade in R. They measure different things, and both are useful.

How often is this page updated?

Weekly, from the public leaderboard data. The live leaderboard updates continuously as trades close.

The Bottom Line

The best AI model for crypto trading is the one that earns the most per unit of risk, over enough trades, on the timeframe you actually trade. The answer box at the top names today's leader; the tables show how much evidence stands behind it. Open the crypto AI leaderboard, set your filters, then try the model you pick in a live analysis of Bitcoin before trusting it with anything that matters.

Risk note: SimianX provides AI market research and educational analysis only, not investment advice. AI output can be wrong. Past performance does not predict future results, and trading crypto can lose money.