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Google DeepMind · model passport

Google: Gemma 4 26B A4B

gemma-4-26b-a4b-it
  • Open weights
  • 25.8B params
  • 262,144 context
  • Released 2026-03-11(HF repo created)
3/27axes covered
Best percentile
88.6HHEM Hallucination (Vectara)
Cohort standing
58.6median 57.4

What the data says

Benchmarked on 3 of 27 tracked axes. Every figure below is the model's own published result on that board.

Strongest axes

  • 88.6 pct — HHEM Hallucination (Vectara) (94.8%)

Uneven: 34.3 percentile points between its best and worst axis — 88.6 pct on HHEM Hallucination (Vectara) versus 54.3 pct on GPQA Diamond.

  • HHEM Hallucination (Vectara)88.6 pct94.8%Verify →
  • AIME (math)58.5 pct82.2%Verify →
  • GPQA Diamond54.3 pct73.2%Verify →
  • OverallNot benchmarked
  • 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
  • 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
  • SWE-bench VerifiedNot benchmarked
  • Terminal-BenchNot benchmarked
  • CodingNot benchmarked
  • Instruction followingNot benchmarked
  • Hard promptsNot 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-it
Parameters25.8B
ArchitectureGemma4ForConditionalGeneration

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "google/gemma-4-26B-A4B-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-4-26b-a4b-it
Context length262,144 tokens
Max output235,929 tokens
Acceptsimage, text, video
Input price$0.07 per 1M tokens
Cached input price$0.04 per 1M tokens
Output price$0.23 per 1M tokens

API parameters this endpoint supports

Frequency penaltyInclude reasoningLogit biasLogprobsMax tokensMin-pPresence penaltyReasoningRepetition penaltyResponse formatSeedStop sequencesStructured outputsTemperatureTool choiceTool callingTop-kTop logprobsTop-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-4-26b-a4b-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-4-26b-a4b-it stands at 58.6 (raw mean 67.1) — measured on 3 of 27 axes.

    standingtemplateVerify →

  • gemma-4-26b-a4b-it stands at 58.6 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 →