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Gemma 2 9b It

gemma-2-9b-it
  • Open weights
  • Gemma license licence
  • 9.2B params
  • Released 2024-06-27(curated)
7/27axes covered
Overall · LMArena
1,267#293 of 396
Best percentile
26.3Overall
Cohort standing
27.4median 36.2

What the data says

Benchmarked on 7 of 27 tracked axes. Every figure below is the model's own published result on that board.

Weakest axes

  • 3 pct — AIME (math) (0.6%)
  • 7.7 pct — GPQA Diamond (27.5%)

Uneven: 23.3 percentile points between its best and worst axis — 26.3 pct on Overall versus 3 pct on AIME (math).

  • Overall26.3 pct1,267Verify →
  • Instruction following25.5 pct1,245Verify →
  • Hard prompts24 pct1,257Verify →
  • Coding20.7 pct1,270Verify →
  • LiveBench16.5 pct31.3%Verify →
  • GPQA Diamond7.7 pct27.5%Verify →
  • AIME (math)3 pct0.6%Verify →
  • WebDev ArenaNot benchmarked
  • Vision ArenaNot benchmarked
  • Factuality ArenaNot benchmarked
  • Search ArenaNot benchmarked
  • Document ArenaNot benchmarked
  • Agent ArenaNot benchmarked
  • FrontierMathNot benchmarked
  • SimpleQA VerifiedNot benchmarked
  • ARC-AGI-2Not benchmarked
  • Humanity's Last ExamNot benchmarked
  • SimpleBenchNot 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

Repositorygoogle/gemma-2-9b-it
Parameters9.2B
ArchitectureGemma2ForCausalLM
LicenceGemma license
AccessGated — you must accept the repo's terms before downloading

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "google/gemma-2-9b-it"
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.

  • gemma-2-9b-it stands at 27.4 (raw mean 17.7) — measured on 7 of 27 axes.

    standingtemplateVerify →

  • gemma-2-9b-it stands at 27.4 versus a cohort median of 36.2 across 127 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 →