Gemma 4 26B A4B

▲ Capable

by Google Open Weight Fast Rank #102 of 104

6.0
/ 10

Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — delivering near-31B quality at...

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Specifications

Specifications for Gemma 4 26B A4B
AttributeValue
Lab Google
Tags Open Weight Fast
Overall Score 6.0/10
Release Date 2026-04
Context Window 262,144 tokens
Input Price / 1M $0.06
Output Price / 1M $0.33
Input Modalities Image, Text, Video
Output Modalities Text

Strengths

Weaknesses

Best For

In Depth: Gemma 4 26B A4B

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Benchmark Performance

[Lead paragraph — ~60 words. Anchor Gemma 4 26B A4B's overall score of 6.0/10 against the headline benchmarks it actually competes on (MMLU, HumanEval, MATH, GSM8K, LMSYS Arena). Name the closest peers above and below it on this leaderboard so the reader instantly understands the tier.]

[Detail paragraph — ~80 words. Walk through 2–3 specific benchmark numbers with citations: e.g. "scores X on MMLU vs. Y for the next-best model in its class," "Arena ELO of Z places it between A and B." Mention where the model over- or under-performs its overall rank — a 9.0 model that's a 9.5 on coding but a 8.2 on math is the kind of nuance that wins long-tail queries like "Gemma 4 26B A4B coding benchmark" or "Gemma 4 26B A4B vs [peer]".]

Pricing & Value

[Lead paragraph — ~50 words. State the input/output prices in plain English ("$0.06 in, $0.33 out per million tokens") and convert to something tangible — cost of a 10k-token analysis, cost of a 1M-token agentic run, cost vs. the cheapest model on the leaderboard.]

[Detail paragraph — ~90 words. Compare Gemma 4 26B A4B's price-per-point-of-score against 2–3 named peers. Call out whether you're paying for raw intelligence, long context (262,144 tokens here), low latency, or brand premium. Flag any tier discounts, batch pricing, or caching that materially change the effective cost. If this model is overpriced for its score, say so plainly — that honesty is what ranks.]

Who Should Use This

[Lead paragraph — ~50 words. One sentence per persona: the developer building X, the analyst doing Y, the team that already runs Z. Each should resolve to a concrete decision: "pick Gemma 4 26B A4B if…" and "skip it if…".]

  • [Persona 1 — e.g. "Backend engineers wiring up production agents." One sentence on why this model fits, one on the tradeoff they accept.]
  • [Persona 2 — e.g. "Solo founders prototyping fast." Same structure: why it fits, what they give up.]
  • [Persona 3 — e.g. "Enterprise teams that need a long-context workhorse." Why it fits, the constraint.]
  • [Anti-persona — "Skip Gemma 4 26B A4B if you're optimizing for X or Y." Be specific; this is the most-cited line in a review.]

Release & Version History

[Lead paragraph — ~50 words. Anchor the 2026-04 release in context: what it replaced inside Google's lineup, what the lab claimed it improved, and how those claims held up against independent benchmarks in the weeks after launch.]

[Detail paragraph — ~90 words. Walk the version trail: previous generation, this model, any planned successor or sibling (mini/flash/opus tier). Note pricing or context-window changes vs. the predecessor. Mention deprecation timelines if the lab has announced any — readers searching "Gemma 4 26B A4B deprecated" or "Gemma 4 26B A4B successor" land directly here. Close with the editorial verdict: is this the version to standardize on for the next 6 months, or a stopgap?]

Sources & Further Reading

Related Models

Scores are aggregated from public benchmarks (MMLU, HumanEval, MATH, GSM8K, LMSYS) and normalized to a 1–10 scale. Methodology →