14 AI Models Forecast Bitcoin 30-Day, 90-Day and Year-End Ranges

Fourteen AI models produced 30-day, 90-day and year-end price ranges for bitcoin after the crypto fell 40% over the prior year. The prices used in the test were logged on June 23, 2026, when bitcoin had traded between $62,171 and $65,994; bitcoin then slipped below $60,000 on June 24.

Each model received the same stripped-down prompt that identified only the 40% year-over-year decline and the recent trading range. Models were barred from web searches or external data and were asked to act as neutral bitcoin analysts and provide concise, probability-based forecasts for the three timeframes. The models tested included Claude Sonnet 4.6, Claude Opus 4.8, Microsoft Copilot Think Deeper, Venice AI, ChatGPT 5.5 (High and Medium), Grok Expert, Deepseek Deepthink, Pi AI, Qwen 3.7 Plus, Kimi K2.6, Meta AI, Mistral Lechat Fast, and Gemini 3.1 Pro.

Most models returned wide forecasts and described near-term consolidation. Thirty-day ranges commonly clustered around $60,000 to $68,000. Ninety-day ranges typically widened to about $55,000 to $75,000. Year-end predictions produced the broadest bands, with many models offering ranges that spanned roughly $50,000 to $85,000.

Individual responses followed similar patterns. Grok placed a 30-day window at $61,000–$67,000 and a year-end band near $50,000–$75,000. Deepseek returned a 30-day range of $60,000–$66,000 and a year-end span of $55,000–$75,000. Gemini projected a 30-day $60,000–$68,000 range, a 90-day $55,000–$75,000 band, and a year-end scenario of $70,000–$85,000 if selling pressure eased.

The models referenced common technical points and market drivers. Near-term support was cited around the weekly low at about $62,171 and resistance near recent highs at roughly $66,000–$70,000. Listed bullish factors included potential mean-reversion buying, exchange-traded fund and institutional inflows, and looser macro policy. Listed bearish factors included the persistent 40% annual decline, a decisive break below the weekly low, regulatory setbacks and weaker macro conditions.

Test administrators noted the responses used similar framing and recurring market narratives, producing overlapping, consensus-style outputs rather than sharply divergent forecasts.