Play vs computer

Play chess against a 1600-rated computer

Sharp Idris plays like a real 1600 player. Free, in your browser, no sign-up.

Last updated

touch_appTry the opening: Sharp Idris answers from what real 1600 players play.

8
7
6
5
4
3
2
1a
b
c
d
e
f
g
h
You have White. Make your first move.

How the 1600 computer plays

A 1600 player calculates a few moves ahead, defends its pieces and plays purposeful plans. You win by outplaying it slowly, or by a well-prepared tactic.

64
average loss per move (centipawns)
4.2
big blunders per 100 moves
4.2s
average think, 10-minute game
6
moves it looks ahead

For comparison, real Chess.com rapid players around 1600 lose about 59 centipawns per move (measured on their own games, the same way). The bot is Sharp Idris (1600): see how the bots are built.

Who this level is for

You are a solid club player and want games where you have to earn the win.

A sample game at 1600

  1. 1.d4
  2. d5
  3. 2.Nc3
  4. e6
  5. 3.Bf4
  6. Bd6
  7. 4.Bxd6
  8. Qxd6
  9. 5.a3
  10. Qd8
  11. 6.Qd2
  12. f5
  13. 7.O-O-O
  14. Nd7
  15. 8.Qf4
  16. e5?
  17. 9.dxe5
  18. Ne7
  19. 10.Qg5
  20. Nxe5
  21. 11.Qxg7
  22. N5g6
  23. 12.Qd4
  24. f4
  25. 13.Rd2?
  26. b6
  27. 14.Nxd5
  28. Nxd5
  29. 15.e4
  30. fxe3
  31. 16.Bb5+
  32. Ke7
  33. 17.fxe3
  34. a5
  35. 18.Ne2
  36. Qd6?
  37. 19.Qg7+
  38. Kd8
  39. 20.Rxd5
  40. Qxd5
  41. 21.Qf6+
  42. Ne7
  43. 22.Qxh8+
  44. Qg8?
  45. 23.Rd1+
  46. Nd5??
  47. 24.Qf6#
  48. 1-0
The 1600 bot against itself, every move scored by Stockfish: 1 blunder (??). Hover a marked move to see what was better. A sample game, chosen because it contains a mistake.

How to beat a 1600 computer

  1. 1Improve your worst piece before launching an attack.
  2. 2Avoid weaknesses around your king: it will find the attacking moves.
  3. 3Practise rook endgames: equal-looking endings are where 1600s go wrong.

How we measured this

Measured on 41 Chess.com players' public games, November 2026.

We picked random public Chess.com players rated 400 to 2000 and took their 5 most recent rapid games. Stockfish scored every move they played from move 5 on, comparing it with the best move, and we averaged the loss per move. We fitted a smooth trend through all the players so no single game sways a rating, then ran our bots through exactly the same measurement.

Correction: our first version used textbook reference values that overstated human mistakes; the bots are now tuned to these measured numbers.