DeepSeek-V3 is the stronger all-rounder.
Scores updated · 11 tests both models report · How we compare
Where each one wins
Tests won in each of the one area where both have results. Each piece is one test, so a longer bar means more evidence; grey means the two scored within a point of each other.
Reasoning rests on a single test.
What it costs
Prices per million tokens, roughly 750,000 words. The bars show the cost of a million tokens read plus a million written.
The biggest differences
The tests each model wins by the widest margin, up to three each. Scores are out of 100.
Where DeepSeek-V3 pulls ahead
- Graduate-level biology, physics and chemistry questionsGPQA Diamond+23.1points ahead
Where Hunyuan Large Instruct pulls ahead
No clear win on a test scored out of 100.
Every test, side by side
All 11 tests both models report. The winning score is in its model's colour; marks a score checked independently.
Other results10 tests, not counted
Tests outside the eight areas. They are not counted above: several are summary scores built from other tests, or the same test under another name.
- HumanEvalHunyuan Large Instruct by 24.865.290+24.8
- MATHDeepSeek-V3 by 12.890.277.4+12.8
- Arena HardDeepSeek-V3 by 9.691.481.8+9.6
- C-EvalHunyuan Large Instruct by 2.186.588.6+2.1
- BBHHunyuan Large Instruct by 287.589.5+2
- CMMLUHunyuan Large Instruct by 1.688.890.4+1.6
- MMLUHunyuan Large Instruct by 1.488.589.9+1.4
- IFEvalDeepSeek-V3 by 1.186.185+1.1
- ARC-Challengetie95.394.6tie
- HellaSwagtie88.988.5tie
Questions people ask
Which is better, DeepSeek-V3 or Hunyuan Large Instruct?
DeepSeek-V3 wins the one area where both have results: reasoning. Hunyuan Large Instruct wins none.
How do you compare the two?
We use the 11 benchmark tests both models have published scores on. The verdict counts the 1 tests in the eight capability areas, and a gap under one point (ten on rating-style scales) counts as a tie. The other 10 are listed but not counted, because several are summary scores or repeat a test. Each score is the one shown on the model's own page.