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Gemma 4 26b A4b

gemma-4-26b-a4b
  • Open weights
  • Apache 2.0 licence
  • 26.5B params
  • Released 2026-03-12(HF repo created)
8/27axes covered
Overall · LMArena
1,438#90 of 396
Best percentile
86.7IFBench (Artificial Analysis)
Cohort standing
63.4median 57.4

What the data says

Benchmarked on 8 of 27 tracked axes. Every figure below is the model's own published result on that board.

Strongest axes

  • 86.7 pct — IFBench (Artificial Analysis) (72.4%)

Uneven: 59.4 percentile points between its best and worst axis — 86.7 pct on IFBench (Artificial Analysis) versus 27.3 pct on WebDev Arena.

  • IFBench (Artificial Analysis)86.7 pct72.4%Verify →
  • Instruction following81.6 pct1,437Verify →
  • Hard prompts78.5 pct1,461Verify →
  • Overall77.5 pct1,438Verify →
  • Coding75.7 pct1,483Verify →
  • Vision Arena63.3 pct1,242Verify →
  • Factuality Arena57 pct1,444Verify →
  • WebDev Arena27.3 pct1,359Verify →
  • 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
  • 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-4-26B-A4B
Parameters26.5B
ArchitectureGemma4ForConditionalGeneration
LicenceApache 2.0

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "google/gemma-4-26B-A4B"
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-4-26b-a4b stands at 63.4 (raw mean 68.5) — measured on 8 of 27 axes.

    standingtemplateVerify →

  • gemma-4-26b-a4b stands at 63.4 versus a cohort median of 57.4 across 100 models released within 180 days — above 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 →