Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Granite-4.0-H-1B
~43
0/8 categoriesMiniMax M2.7
~66
Winner · 0/8 categoriesGranite-4.0-H-1B· MiniMax M2.7
Benchmark data for Granite-4.0-H-1B and MiniMax M2.7 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.
MiniMax M2.7 is priced at $0.30 input / $1.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Granite-4.0-H-1B. MiniMax M2.7 has the larger context window at 200K, 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 | Granite-4.0-H-1B | MiniMax M2.7 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 57% |
| Toolathlon | — | 46.3% |
| MLE-Bench Lite | — | 66.6% |
| MM-ClawBench | — | 62.7% |
| Coding | ||
| HumanEval | 74% | — |
| SWE-bench Pro | — | 56.2% |
| SWE Multilingual | — | 76.5% |
| Multi-SWE Bench | — | 52.7% |
| VIBE-Pro | — | 55.6% |
| NL2Repo | — | 39.8% |
| Multimodal & Grounded | ||
| GDPval-AA | — | 1495 |
| 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.
Granite-4.0-H-1B: $0.00 input / $0.00 output per 1M tokens MiniMax M2.7: $0.30 input / $1.20 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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