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Ibm · model passport

Granite 4 2 30b

granite-4-2-30b
  • Open weights
  • Apache 2.0 licence
  • 29.3B params
  • Released 2026-08-07(HF repo created)
5/27axes covered
Overall · LMArena
1,340#216 of 396
Best percentile
50.6Coding
Cohort standing
44.5median 60.2

What the data says

Benchmarked on 5 of 27 tracked axes. Every figure below is the model's own published result on that board.

Weakest axes

  • 11 pct — Factuality Arena (1,375)

Uneven: 39.6 percentile points between its best and worst axis — 50.6 pct on Coding versus 11 pct on Factuality Arena.

  • Coding50.6 pct1,401Verify →
  • Instruction following49.7 pct1,339Verify →
  • Hard prompts48.7 pct1,363Verify →
  • Overall45.7 pct1,340Verify →
  • Factuality Arena11 pct1,375Verify →
  • WebDev ArenaNot benchmarked
  • Vision ArenaNot benchmarked
  • Search ArenaNot benchmarked
  • Document ArenaNot benchmarked
  • Agent ArenaNot benchmarked
  • GPQA DiamondNot benchmarked
  • AIME (math)Not benchmarked
  • FrontierMathNot benchmarked
  • SimpleQA VerifiedNot benchmarked
  • ARC-AGI-2Not benchmarked
  • Humanity's Last ExamNot benchmarked
  • SimpleBenchNot benchmarked
  • LiveBenchNot benchmarked
  • BFCL v4 (function calling)Not benchmarked
  • SWE-bench Verified (live board)Not benchmarked
  • Terminal-Bench 4.0Not benchmarked
  • MMLU-Pro (Artificial Analysis)Not benchmarked
  • LiveCodeBench (Artificial Analysis)Not benchmarked
  • IFBench (Artificial Analysis)Not benchmarked
  • HHEM Hallucination (Vectara)Not benchmarked
  • SWE-bench VerifiedNot benchmarked
  • Terminal-BenchNot benchmarked

Percentiles are the model's standing within each board's own population. Axes marked "not benchmarked" have no published result for this model — that is an absence of data, not a low score.

How to run it

Run it yourself

Repositoryibm-granite/granite-4.2-30b
Parameters29.3B
ArchitectureGraniteForCausalLM
LicenceApache 2.0

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ibm-granite/granite-4.2-30b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

Ollama

We don't sync Ollama tags, so we won't print a command we can't verify — a wrong one would pull the wrong model. Search the Ollama library →

Unsloth fine-tuning recipes are model-specific and we don't derive them here. Unsloth docs →

Identifiers, limits and prices are synced from the upstream source shown above. Any field we haven't synced is left out entirely rather than guessed.

  • granite-4.2-30b stands at 44.5 (raw mean 41.2) — measured on 5 of 27 axes.

    standingtemplateVerify →

  • granite-4.2-30b stands at 44.5 versus a cohort median of 60.2 across 191 models released within 180 days — below the peer median.

    cohorttemplateVerify →

Every claim is a computed fact from the current board data, phrased by a low-temperature model call and rejected by a validation gate if any number is not in the source row. How the leaderboards work →