Skip to content
aiqbench

Google · model passport

Google: Gemma 2 27B

gemma-2-27b-it
  • Open weights
  • Gemma license licence
  • 27.2B params
  • 8,192 context
  • Released 2024-06-27(curated)
7/27axes covered
Overall · LMArena
1,290#273 of 396
Best percentile
31.3Overall
Cohort standing
32.0median 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

  • 6 pct — AIME (math) (1.4%)

Uneven: 25.3 percentile points between its best and worst axis — 31.3 pct on Overall versus 6 pct on AIME (math).

  • Overall31.3 pct1,290Verify →
  • Instruction following30.8 pct1,271Verify →
  • LiveBench29.2 pct41.2%Verify →
  • Hard prompts28.8 pct1,282Verify →
  • Coding26.3 pct1,304Verify →
  • GPQA Diamond17.6 pct36.5%Verify →
  • AIME (math)6 pct1.4%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-2-27b-it
Parameters27.2B
ArchitectureGemma2ForCausalLM
LicenceGemma license
AccessGated — you must accept the repo's terms before downloading

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "google/gemma-2-27b-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 →

Use it hosted

API model IDgoogle/gemma-2-27b-it
Context length8,192 tokens
Max output2,048 tokens
Input price$0.65 per 1M tokens
Output price$0.65 per 1M tokens

API parameters this endpoint supports

Frequency penaltyMax tokensPresence penaltyRepetition penaltyResponse formatSeedStop sequencesStructured outputsTemperatureTop-p

Example request

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"google/gemma-2-27b-it","messages":[{"role":"user","content":"Hello"}]}'

View on OpenRouter →

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-2-27b-it stands at 32 (raw mean 24.3) — measured on 7 of 27 axes.

    standingtemplateVerify →

  • gemma-2-27b-it stands at 32 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 →