Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3.2 (Thinking)
68
Winner · 3/8 categoriesGranite-4.0-H-350M
~24
0/8 categoriesDeepSeek V3.2 (Thinking)· Granite-4.0-H-350M
Pick DeepSeek V3.2 (Thinking) if you want the stronger benchmark profile. Granite-4.0-H-350M only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
DeepSeek V3.2 (Thinking) is clearly ahead on the aggregate, 68 to 24. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V3.2 (Thinking)'s sharpest advantage is in multilingual, where it averages 80.8 against 14.7. The single biggest benchmark swing on the page is MGSM, 84% to 14.7%.
DeepSeek V3.2 (Thinking) is the reasoning model in the pair, while Granite-4.0-H-350M is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. DeepSeek V3.2 (Thinking) gives you the larger context window at 128K, compared with 32K for Granite-4.0-H-350M.
BenchLM keeps the benchmark table and the operator tradeoffs on the same page so a better score does not hide a materially slower, pricier, or smaller-context model.
Runtime metrics show N/A when BenchLM does not have a sourced snapshot for that exact model. The scoring rules and freshness policy are documented on the methodology page.
| Benchmark | DeepSeek V3.2 (Thinking) | Granite-4.0-H-350M |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 71% | — |
| BrowseComp | 70% | — |
| OSWorld-Verified | 67% | — |
| Coding | ||
| HumanEval | 79% | 39% |
| SWE-bench Verified | 48% | — |
| LiveCodeBench | 45% | — |
| SWE-bench Pro | 58% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 66% | — |
| OfficeQA Pro | 77% | — |
| Reasoning | ||
| MuSR | 81% | — |
| BBH | 86% | 33.1% |
| LongBench v2 | 78% | — |
| MRCRv2 | 78% | — |
| ARC-AGI-2 | 4% | — |
| KnowledgeDeepSeek V3.2 (Thinking) wins | ||
| MMLU | 87% | 35.0% |
| GPQA | 85% | 24.1% |
| SuperGPQA | 83% | — |
| MMLU-Pro | 73% | 12.1% |
| HLE | 22% | — |
| FrontierScience | 77% | — |
| SimpleQA | 83% | — |
| Instruction FollowingDeepSeek V3.2 (Thinking) wins | ||
| IFEval | 85% | 55.4% |
| MultilingualDeepSeek V3.2 (Thinking) wins | ||
| MGSM | 84% | 14.7% |
| MMLU-ProX | 79% | — |
| Mathematics | ||
| AIME 2023 | 87% | — |
| AIME 2024 | 89% | — |
| AIME 2025 | 88% | — |
| HMMT Feb 2023 | 83% | — |
| HMMT Feb 2024 | 85% | — |
| HMMT Feb 2025 | 84% | — |
| BRUMO 2025 | 86% | — |
| MATH-500 | 84% | — |
DeepSeek V3.2 (Thinking) is ahead overall, 68 to 24. The biggest single separator in this matchup is MGSM, where the scores are 84% and 14.7%.
DeepSeek V3.2 (Thinking) has the edge for knowledge tasks in this comparison, averaging 65.9 versus 16.4. Inside this category, GPQA is the benchmark that creates the most daylight between them.
DeepSeek V3.2 (Thinking) has the edge for instruction following in this comparison, averaging 85 versus 55.4. Inside this category, IFEval is the benchmark that creates the most daylight between them.
DeepSeek V3.2 (Thinking) has the edge for multilingual tasks in this comparison, averaging 80.8 versus 14.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.