DeepSeek-V3.2 vs Granite 4.2 30B
Wins 4 of 6 areas
Coding · Agents · Reasoning · Long documents
Wins 1 of 6 areas
Following instructions
DeepSeek-V3.2 is the stronger all-rounder.Granite 4.2 30B is better at following instructions.
Scores updated · 24 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.
- CodingWriting and fixing software51DeepSeek-V3.25 of 6 tests
- ReasoningHard problems that need careful thinking30DeepSeek-V3.23 of 3 tests
- AgentsCarrying out multi-step tasks on its own20DeepSeek-V3.22 of 2 tests
- Long documentsFinding answers in very long texts10DeepSeek-V3.21 of 1 test
- Following instructionsDoing exactly what it is asked01Granite 4.2 30B1 of 1 test
- FactsGetting facts right instead of making them up11Even1 each
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-V3.2 costs 14% less for the same work.
The biggest differences
The three tests each model wins by the widest margin. Scores are out of 100.
Where DeepSeek-V3.2 pulls ahead
- Fixes real GitHub issues in many programming languagesSWE-bench Multilingual+28.3points ahead
- Reasons across sets of long documentsAA-LCR+24.3points ahead
- Answers hard knowledge questions correctlyAA-Omniscience · Accuracy+22.9points ahead
Where Granite 4.2 30B pulls ahead
- Avoids making up answers it doesn't knowAA-Omniscience · Non-hallucination+57.1points ahead
- Long, multi-file coding tasks in real codebasesSWE-bench Pro+17.7points ahead
- Follows unfamiliar, precisely checkable instructionsIFBench+16.5points ahead
Every test, side by side
All 24 tests both models report. The winning score is in its model's colour; marks a score checked independently.
CodingDeepSeek-V3.2
- SWE-bench MultilingualDeepSeek-V3.2 by 28.370.241.9+28.3
- Terminal-Bench 2.1DeepSeek-V3.2 by 20.246.826.6+20.2
- SWE-bench ProGranite 4.2 30B by 17.715.633.3+17.7
- SWE-bench VerifiedDeepSeek-V3.2 by 16.173.157+16.1
- LiveCodeBench v6DeepSeek-V3.2 by 7.583.375.8+7.5
- SciCodeDeepSeek-V3.2 by 1.138.937.8+1.1
AgentsDeepSeek-V3.2
- GDPValDeepSeek-V3.2 by 5.89.63.8+5.8
- τ-Bench V3 · BankingDeepSeek-V3.2 by 4.418.814.4+4.4
ReasoningDeepSeek-V3.2
- GPQA DiamondDeepSeek-V3.2 by 19.68464.4+19.6
- Humanity's Last ExamDeepSeek-V3.2 by 13.424.611.2+13.4
- CritPtDeepSeek-V3.2 by 2.62.90.3+2.6
FactsEven
- AA-Omniscience · Non-hallucinationGranite 4.2 30B by 57.117.374.4+57.1
- AA-Omniscience · AccuracyDeepSeek-V3.2 by 22.93310.1+22.9
Following instructionsGranite 4.2 30B
- IFBenchGranite 4.2 30B by 16.560.777.2+16.5
Other results9 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.
- Artificial Analysis Coding IndexDeepSeek-V3.2 by 14.344.229.9+14.3
- AA-OmniscienceGranite 4.2 30B by 9.6-22.5-12.9+9.6
- AA IntelligenceDeepSeek-V3.2 by 8.821.512.7+8.8
- MMLU-ProDeepSeek-V3.2 by 7.48577.6+7.4
- τ³-BenchDeepSeek-V3.2 by 7.269.262+7.2
- AA Agentic IndexDeepSeek-V3.2 by 4.418.313.9+4.4
- AIME 2025DeepSeek-V3.2 by 3.993.189.2+3.9
- HMMT 2025DeepSeek-V3.2 by 190.289.2+1
- AIME25 no toolstie89.389.2tie
Questions people ask
Which is better, DeepSeek-V3.2 or Granite 4.2 30B?
DeepSeek-V3.2 wins four of the six areas where both have results: coding, agents, reasoning and long documents. Granite 4.2 30B wins following instructions. They are level on facts.
Which is better for coding?
DeepSeek-V3.2. It wins 5 of the 6 coding tests both models report; Granite 4.2 30B wins 1.
Which is cheaper?
DeepSeek-V3.2 costs $0.28 per million input tokens and $0.42 per million output tokens; Granite 4.2 30B costs $0.16 and $0.65. That makes DeepSeek-V3.2 about 14% cheaper for the same work.
How do you compare the two?
We use the 24 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 9 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.