Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek R1 Distill Qwen 32B
~3
0/8 categoriesGranite-4.0-1B
~40
Winner · 0/8 categoriesDeepSeek R1 Distill Qwen 32B· Granite-4.0-1B
Benchmark data for DeepSeek R1 Distill Qwen 32B and Granite-4.0-1B is coming soon on BenchLM.
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
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 R1 Distill Qwen 32B | Granite-4.0-1B |
|---|---|---|
| Agentic | ||
| Coming soon | ||
| Coding | ||
| React Native Evals | 31.8% | — |
| HumanEval | — | 73% |
| Multimodal & Grounded | ||
| Coming soon | ||
| Reasoning | ||
| BBH | — | 59.7% |
| Knowledge | ||
| MMLU | — | 59.7% |
| GPQA | — | 29.7% |
| MMLU-Pro | — | 32.9% |
| Instruction Following | ||
| IFEval | — | 78.5% |
| Multilingual | ||
| MGSM | — | 27.5% |
| Mathematics | ||
| Coming soon | ||
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
DeepSeek R1 Distill Qwen 32B: $0.00 input / $0.00 output per 1M tokens Granite-4.0-1B: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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