DeepSeek-V3 vs Nova Pro
Wins 4 of 6 areas
Coding · Reasoning · Long documents · Following instructions
Wins 1 of 6 areas
Facts
DeepSeek-V3 is the stronger all-rounder.Nova Pro is better at facts.
Scores updated · 29 tests both models report · How we compare
Where each one wins
Tests won in each of the six areas 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.
- ReasoningHard problems that need careful thinking20DeepSeek-V32 of 3 tests · 1 tie
- CodingWriting and fixing software20DeepSeek-V32 of 2 tests
- Long documentsFinding answers in very long texts10DeepSeek-V31 of 1 test
- Following instructionsDoing exactly what it is asked10DeepSeek-V31 of 1 test
- FactsGetting facts right instead of making them up12Nova Pro2 of 3 tests
- AgentsCarrying out multi-step tasks on its own00Even0 each · 1 tie
Agents, long documents and following instructions rest on a single test each.
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.
DeepSeek-V3 costs 72% less for the same work.
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
- Reasons across sets of long documentsAA-LCR+19.7points ahead
- Code for real scientific research problemsSciCode+18.2points ahead
- Graduate-level biology, physics and chemistry questionsGPQA Diamond+15.6points ahead
Where Nova Pro pulls ahead
- Avoids making up answers it doesn't knowAA-Omniscience · Non-hallucination+8.2points ahead
Every test, side by side
All 29 tests both models report. The winning score is in its model's colour; marks a score checked independently.
CodingDeepSeek-V3
- SciCodeDeepSeek-V3 by 18.23920.8+18.2
- Terminal-Bench HardDeepSeek-V3 by 9.115.26.1+9.1
ReasoningDeepSeek-V3
- GPQA DiamondDeepSeek-V3 by 15.665.549.9+15.6
- Humanity's Last ExamDeepSeek-V3 by 1.54.73.2+1.5
- CritPttie00tie
FactsNova Pro
- AA-Omniscience · AccuracyDeepSeek-V3 by 8.625.416.9+8.6
- AA-Omniscience · Non-hallucinationNova Pro by 8.214.122.3+8.2
- Vectara HHEM hallucination ratelower is betterNova Pro by 16.15.1+1
Following instructionsDeepSeek-V3
- IFBenchDeepSeek-V3 by 2.94138.1+2.9
Other results18 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.
- τ²-Bench Telecom (AA run)DeepSeek-V3 by 33.147.114+33.1
- HumanEvalNova Pro by 23.865.289+23.8
- vectara_avg_summary_lengthDeepSeek-V3 by 15.581.766.2+15.5
- MMLUDeepSeek-V3 by 12.788.575.8+12.7
- Artificial Analysis Coding IndexDeepSeek-V3 by 122311+12
- MATHDeepSeek-V3 by 8.190.282.1+8.1
- AA-OmniscienceDeepSeek-V3 by 7-40.7-47.7+7
- DROPDeepSeek-V3 by 6.291.685.4+6.2
- naturalquestions_closedbookDeepSeek-V3 by 6.246.740.5+6.2
- IFEvalNova Pro by 686.192.1+6
- AA IntelligenceDeepSeek-V3 by 2.79.77+2.7
- vectara_answer_rateNova Pro by 1.897.599.3+1.8
- vectara_factual_consistencyNova Pro by 193.994.9+1
- GSM8Ktie9494.8tie
- BBHtie87.586.9tie
- OpenBookQAtie95.496tie
- ARC-Challengetie95.394.8tie
- NarrativeQAtie79.679.1tie
Questions people ask
Which is better, DeepSeek-V3 or Nova Pro?
DeepSeek-V3 wins four of the six areas where both have results: coding, reasoning, long documents and following instructions. Nova Pro wins facts. They are level on agents.
Which is better for coding?
DeepSeek-V3. It wins 2 of the 2 coding tests both models report; Nova Pro wins none.
Which is cheaper?
DeepSeek-V3 costs $0.24 per million input tokens and $0.90 per million output tokens; Nova Pro costs $0.80 and $3.20. That makes DeepSeek-V3 about 72% cheaper for the same work.
How do you compare the two?
We use the 29 benchmark tests both models have published scores on. The verdict counts the 11 tests in the eight capability areas, and a gap under one point (ten on rating-style scales) counts as a tie. The other 18 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.