Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5 nano
~36
0/8 categoriesGranite-4.0-H-1B
~43
Winner · 0/8 categoriesGPT-5 nano· Granite-4.0-H-1B
Benchmark data for GPT-5 nano and Granite-4.0-H-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.
GPT-5 nano is priced at $0.05 input / $0.40 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Granite-4.0-H-1B. GPT-5 nano has the larger context window at 400K, compared with 128K for Granite-4.0-H-1B.
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 | GPT-5 nano | Granite-4.0-H-1B |
|---|---|---|
| Agentic | ||
| Coming soon | ||
| Coding | ||
| HumanEval | — | 74% |
| Multimodal & Grounded | ||
| Coming soon | ||
| Reasoning | ||
| BBH | — | 60.4% |
| Knowledge | ||
| MMLU | — | 59.4% |
| GPQA | — | 29.9% |
| MMLU-Pro | — | 34.0% |
| Instruction Following | ||
| IFEval | — | 77.4% |
| Multilingual | ||
| MGSM | — | 37.8% |
| 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.
GPT-5 nano: $0.05 input / $0.40 output per 1M tokens Granite-4.0-H-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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