Gemini 4 Argon vs Gemma 4 26B A4B

Google · US  |  Google · US · Updated June 2026

Quick verdict

Both are Google models. Gemini 4 Argon is the newer, generally stronger default; reach for Gemma 4 26B A4B when its lower price or a specific cost or latency profile matters more than the latest capabilities.

Gemini 4 Argon and Gemma 4 26B A4B are both Google models, so the real question is not which lab to trust but which tier fits your workload and budget. Gemini 4 Argon is google's Gemini 4 Argon, broadly available October 13, 2026 - a 1M-context model at $2/$10 introductory pricing (doubling December 1). Gemma 4 26B A4B is an Apache-2.0 open MoE with 25.2B total but only 3.8B active parameters, delivering near-31B-dense quality at a fraction of the inference cost. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.

Key differences at a glance

Side-by-side specs

SpecGemini 4 ArgonGemma 4 26B A4B
ProviderGoogle (US) Google (US)
ReleasedOctober 13, 2026 April 2, 2026
Context window1M tokens (~1,573 pages) 256K (~393 pages)
Price (in/out)$2/$10 per 1M tokens $0.12/$0.37 per 1M tokens
Open weight?No — API only Yes — self-hostable
Modalitiestext, image, audio, video, code text, image, video, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses

Gemini 4 Argon

Its 1M tokens window holds about 4× more than Gemma 4 26B A4B's 256K in a single prompt.

Google-reported DeepSWE v1.1 of 77.9% and LVBench long-video understanding of 91.7%

Gemini 4 Argon

Google's Gemini 4 Argon, broadly available October 13, 2026 - a 1M-context model at $2/$10 introductory pricing (doubling December 1) — and it carries the larger 1M tokens context.

Artificial Analysis Intelligence Index of 53 at high effort (AI Weekly)

Gemini 4 Argon

Google's Gemini 4 Argon, broadly available October 13, 2026 - a 1M-context model at $2/$10 introductory pricing (doubling December 1) — and it is the newer of the two.

Fast, cheap inference from a sparse MoE (3.8B active of 25.2B total)

Gemma 4 26B A4B

At $0.12/$0.37 per 1M tokens it undercuts Gemini 4 Argon ($2/$10 per 1M tokens), and that gap compounds at volume.

Near-31B-dense quality at a fraction of the compute and memory-bandwidth cost

Gemma 4 26B A4B

An Apache-2.0 open MoE with 25.2B total but only 3.8B active parameters, delivering near-31B-dense quality at a fraction of the inference cost — and it runs cheaper at $0.12/$0.37 per 1M tokens.

Strong reasoning and coding (88.3% AIME 2026 no-tools, 77.1% LiveCodeBench v6)

Gemma 4 26B A4B

An Apache-2.0 open MoE with 25.2B total but only 3.8B active parameters, delivering near-31B-dense quality at a fraction of the inference cost — and its weights are open while Gemini 4 Argon is API-only.

Lowest cost at scale

Gemma 4 26B A4B

At $0.12/$0.37 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.

Largest single-prompt input

Gemini 4 Argon

Its 1M tokens window is about 4× larger than Gemma 4 26B A4B's 256K, fitting roughly 1,573 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

→ Gemma 4 26B A4B

At $0.12/$0.37 per 1M tokens it undercuts Gemini 4 Argon, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ Gemini 4 Argon

Larger 1M tokens window fits more in one prompt.

A team with data-privacy or self-hosting needs

→ Gemma 4 26B A4B

Open weights let you run it on your own hardware; Gemini 4 Argon is API-only.

Anyone whose priority is 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses

→ Gemini 4 Argon

It is specifically built for that.

Anyone whose priority is fast, cheap inference from a sparse moe (3.8b active of 25.2b total)

→ Gemma 4 26B A4B

That is its strongest area.

Gemini 4 Argon: where it fits

Google's Gemini 4 Argon, broadly available October 13, 2026 - a 1M-context model at $2/$10 introductory pricing (doubling December 1). Released October 13, 2026 by Google, it is built for 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses, google-reported DeepSWE v1.1 of 77.9% and LVBench long-video understanding of 91.7%, and artificial Analysis Intelligence Index of 53 at high effort (AI Weekly).

Its trade-offs are real: introductory $2/$10 pricing ends November 30; from December 1 it doubles to $4/$20, verbose: about 62K output tokens per Artificial Analysis task, so cost per task is high, and announced September 30 for trusted cyber defenders only; broad paid-tier availability followed October 13 (not on the free tier). At $2 in / $10 out per million tokens, it sits in the mid price band.

Gemma 4 26B A4B: where it fits

An Apache-2.0 open MoE with 25.2B total but only 3.8B active parameters, delivering near-31B-dense quality at a fraction of the inference cost. Released April 2, 2026 by Google, it is built for fast, cheap inference from a sparse MoE (3.8B active of 25.2B total), near-31B-dense quality at a fraction of the compute and memory-bandwidth cost, strong reasoning and coding (88.3% AIME 2026 no-tools, 77.1% LiveCodeBench v6), and multimodal input (text/image, plus video processed as frames up to 60s) with native function calling.

Its trade-offs: all 25.2B parameters must be loaded into memory even though only 3.8B are active per token, and 256K context trails 1M-token frontier rivals, and this variant has no audio input (audio is E2B/E4B/12B only). At $0.12 in / $0.37 out per million tokens, it sits in the budget price band.

The bottom line for this matchup

Because Gemini 4 Argon and Gemma 4 26B A4B come from the same lab (Google), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. Gemini 4 Argon is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to Gemini 4 Argon and drop down only with a concrete reason.

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See pricing

Frequently asked questions

Is Gemini 4 Argon or Gemma 4 26B A4B better for coding?

Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Gemini 4 Argon leans toward 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses while Gemma 4 26B A4B leans toward fast, cheap inference from a sparse moe (3.8b active of 25.2b total), and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Gemini 4 Argon or Gemma 4 26B A4B?

Gemma 4 26B A4B is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 4 Argon is API-metered at $2/$10 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.

Which has the bigger context window?

Gemini 4 Argon — 1M tokens vs 256K, about 4× larger. Useful only if the model actually reasons over the full window, which not all do.

Should I upgrade from Gemma 4 26B A4B to Gemini 4 Argon?

Since both are Google models, the newer one (Gemini 4 Argon) is usually the better default unless you need a specific cost or latency profile from the other.

Which is newer, Gemini 4 Argon or Gemma 4 26B A4B?

Gemini 4 Argon — released October 13, 2026, about 6 months after Gemma 4 26B A4B.

Related comparisons

Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.