Gemma 4 26B A4B vs Kimi K3

Google · US  |  Moonshot AI · China · Updated June 2026

Quick verdict

Pick Gemma 4 26B A4B for fast, cheap inference from a sparse moe (3.8b active of 25.2b total) or near-31b-dense quality at a fraction of the compute and memory-bandwidth cost. Pick Kimi K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable or 1m-token context with native vision (text, image and video). On a tight budget at scale, Gemma 4 26B A4B is the value pick.

Gemma 4 26B A4B (Google, US) and Kimi K3 (Moonshot AI, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Kimi K3 is moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. They diverge most on price and context window — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecGemma 4 26B A4BKimi K3
ProviderGoogle (US) Moonshot AI (China)
ReleasedApril 2, 2026 July 27, 2026
Context window256K (~393 pages) 1M (~1,573 pages)
Price (in/out)$0.12/$0.37 per 1M tokens $3/$15 per 1M tokens
Open weight?Yes — self-hostable Yes — self-hostable
Modalitiestext, image, video, code text, image, video, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

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 Kimi K3 ($3/$15 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

Kimi K3 is comparatively weak here — coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified

Largest open-weight model at release — 2.8T sparse MoE, self-hostable

Kimi K3

Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores — and it carries the larger 1M context.

1M-token context with native vision (text, image and video)

Kimi K3

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

Vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness

Kimi K3

Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores — and it is the newer of the two.

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

Kimi K3

Its 1M 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 Kimi K3, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ Kimi K3

Larger 1M window fits more in one prompt.

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

→ Gemma 4 26B A4B

It is specifically built for that.

Anyone whose priority is largest open-weight model at release — 2.8t sparse moe, self-hostable

→ Kimi K3

That is its strongest area.

An enterprise with regional data-residency rules

→ Gemma 4 26B A4B or Kimi K3

Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.

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 are real: 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.

Kimi K3: where it fits

Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. Released July 27, 2026 by Moonshot AI, it is built for largest open-weight model at release — 2.8T sparse MoE, self-hostable, 1M-token context with native vision (text, image and video), vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness, and fresh-input pricing of $3/M (cached $0.30/M), flat across the full 1M context.

Its trade-offs: coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified, 2.8T params need serious hardware to self-host — weights are free, running is not, no independent benchmark reproduction yet at release, and image input but no audio or video. At $3 in / $15 out per million tokens, it sits in the mid price band.

The bottom line for this matchup

This is less "which is smarter" and more "which ecosystem fits." Gemma 4 26B A4B (US) and Kimi K3 (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Gemma 4 26B A4B is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.

Want both Gemma 4 26B A4B and Kimi K3 without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.

See pricing

Frequently asked questions

Is Gemma 4 26B A4B or Kimi K3 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, Gemma 4 26B A4B leans toward fast, cheap inference from a sparse moe (3.8b active of 25.2b total) while Kimi K3 leans toward largest open-weight model at release — 2.8t sparse moe, self-hostable, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Gemma 4 26B A4B or Kimi K3?

Gemma 4 26B A4B is cheaper — $0.12/$0.37 per 1M tokens vs $3/$15 per 1M tokens, roughly 25× apart on input.

Which has the bigger context window?

Kimi K3 — 1M vs 256K, about 4× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Gemma 4 26B A4B and Kimi K3 together?

Yes — a multi-model platform like LumiChats gives you Gemma 4 26B A4B, Kimi K3 and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.

Which is newer, Gemma 4 26B A4B or Kimi K3?

Kimi K3 — released July 27, 2026, about 4 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.