DeepSeek-V3 vs GPT-5.6 Sol
Wins 1 of 6 areas
Facts
Wins 5 of 6 areas
Coding · Agents · Reasoning · Long documents · Following instructions
GPT-5.6 Sol is the stronger all-rounder.DeepSeek-V3 is cheaper and better at facts.
Scores updated · 22 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 thinking04GPT-5.6 Sol4 of 4 tests
- CodingWriting and fixing software03GPT-5.6 Sol3 of 3 tests
- AgentsCarrying out multi-step tasks on its own03GPT-5.6 Sol3 of 3 tests
- Long documentsFinding answers in very long texts01GPT-5.6 Sol1 of 1 test
- Following instructionsDoing exactly what it is asked01GPT-5.6 Sol1 of 1 test
- FactsGetting facts right instead of making them up21DeepSeek-V32 of 3 tests
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 95% 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 GPT-5.6 Sol pulls ahead
- Command-line tasks in a real terminalTerminal-Bench 2.1+71.1points ahead
- Hard command-line tasks in a real terminalTerminal-Bench Hard+50.7points ahead
- Common-sense trick questionsSimpleBench+45.9points ahead
Where DeepSeek-V3 pulls ahead
- Avoids making up answers it doesn't knowAA-Omniscience · Non-hallucination+6.3points ahead
Every test, side by side
All 22 tests both models report. The winning score is in its model's colour; marks a score checked independently.
CodingGPT-5.6 Sol
- Terminal-Bench 2.1GPT-5.6 Sol by 71.116.988+71.1
- Terminal-Bench HardGPT-5.6 Sol by 50.715.265.9+50.7
- SciCodeGPT-5.6 Sol by 18.13957.1+18.1
AgentsGPT-5.6 Sol
- GDPValGPT-5.6 Sol by 55.6055.6+55.6
- Terminal-Bench 4.0GPT-5.6 Sol by 39.9039.9+39.9
- τ-Bench V3 · BankingGPT-5.6 Sol by 39.64.744.3+39.6
ReasoningGPT-5.6 Sol
- SimpleBenchGPT-5.6 Sol by 45.918.964.8+45.9
- Humanity's Last ExamGPT-5.6 Sol by 44.84.749.5+44.8
- CritPtGPT-5.6 Sol by 32.3032.3+32.3
- GPQA DiamondGPT-5.6 Sol by 28.665.594.1+28.6
FactsDeepSeek-V3
- AA-Omniscience · AccuracyGPT-5.6 Sol by 33.925.459.4+33.9
- AA-Omniscience · Non-hallucinationDeepSeek-V3 by 6.314.17.8+6.3
- Vectara HHEM hallucination ratelower is betterDeepSeek-V3 by 6.36.112.4+6.3
Following instructionsGPT-5.6 Sol
- IFBenchGPT-5.6 Sol by 31.74172.7+31.7
Other results7 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.
- AA-OmniscienceGPT-5.6 Sol by 62.7-40.722+62.7
- Artificial Analysis Coding IndexGPT-5.6 Sol by 54.42377.4+54.4
- τ²-Bench Telecom (AA run)GPT-5.6 Sol by 3847.185.1+38
- AA IntelligenceGPT-5.6 Sol by 37.39.747+37.3
- vectara_factual_consistencyDeepSeek-V3 by 6.393.987.6+6.3
- vectara_avg_summary_lengthGPT-5.6 Sol by 49.4 rating points81.7131.1+49.4 rating
- vectara_answer_rateGPT-5.6 Sol by 1.697.599.1+1.6
Questions people ask
Which is better, DeepSeek-V3 or GPT-5.6 Sol?
GPT-5.6 Sol wins five of the six areas where both have results: coding, agents, reasoning, long documents and following instructions. DeepSeek-V3 wins facts, and costs 95% less.
Which is better for coding?
GPT-5.6 Sol. It wins 3 of the 3 coding tests both models report; DeepSeek-V3 wins none.
Which is cheaper?
DeepSeek-V3 costs $0.24 per million input tokens and $0.90 per million output tokens; GPT-5.6 Sol costs $4.00 and $20.00. That makes DeepSeek-V3 about 95% cheaper for the same work.
How do you compare the two?
We use the 22 benchmark tests both models have published scores on. The verdict counts the 15 tests in the eight capability areas, and a gap under one point (ten on rating-style scales) counts as a tie. The other 7 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.