Gemma 4 vs Kimi K2.7 Code

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

Quick verdict

Pick Gemma 4 for self-hosted, data-private deployment or running locally or on edge devices. Pick Kimi K2.7 Code for long-horizon agentic software engineering or token-efficient reasoning (~30% fewer than k2.6). On a tight budget at scale, Gemma 4 is the value pick.

Gemma 4 (Google, US) and Kimi K2.7 Code (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 is google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting. Kimi K2.7 Code is moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. 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 4Kimi K2.7 Code
ProviderGoogle (US) Moonshot AI (China)
ReleasedApril 2, 2026 June 12, 2026
Context window256K (~384 pages) 256K (~393 pages)
Price (in/out)Open weight (self-host / free) $0.95/$4 per 1M tokens
Open weight?Yes — self-hostable Yes — self-hostable
Modalitiestext, image, code text, image, video, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Self-hosted, data-private deployment

Gemma 4

A core design strength of Gemma 4.

Running locally or on edge devices

Gemma 4

A core design strength of Gemma 4.

Fine-tuning on your own data

Gemma 4

A core design strength of Gemma 4.

Long-horizon agentic software engineering

Kimi K2.7 Code

A core design strength of Kimi K2.7 Code.

Token-efficient reasoning (~30% fewer than K2.6)

Kimi K2.7 Code

A core design strength of Kimi K2.7 Code.

Open-weight 1T MoE, self-hostable

Kimi K2.7 Code

A core design strength of Kimi K2.7 Code.

Lowest cost at scale

Gemma 4

At Open weight (self-host / free), it is the cheaper of the two — the gap dominates the bill on high-volume workloads.

Largest single-prompt input

Kimi K2.7 Code

Its 256K window is about 1× larger, fitting roughly 393 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

Gemma 4

At Open weight (self-host / free) it undercuts Kimi K2.7 Code, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

Kimi K2.7 Code

Larger 256K window fits more in one prompt.

Anyone whose priority is self-hosted, data-private deployment

Gemma 4

It is specifically built for that.

Anyone whose priority is long-horizon agentic software engineering

Kimi K2.7 Code

That is its strongest area.

An enterprise with regional data-residency rules

Gemma 4 or Kimi K2.7 Code

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

Gemma 4: where it fits

Google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting. Released April 2, 2026 by Google, it is built for self-hosted, data-private deployment, running locally or on edge devices, fine-tuning on your own data, and multimodal tasks over a 256K context.

Its trade-offs are real: trails frontier closed models on the hardest tasks, and needs your own hardware to run. As an open-weight model, its running cost is your own hardware rather than a per-token fee.

Kimi K2.7 Code: where it fits

Moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. Released June 12, 2026 by Moonshot AI, it is built for long-horizon agentic software engineering, token-efficient reasoning (~30% fewer than K2.6), open-weight 1T MoE, self-hostable, and multi-turn tool use with preserved reasoning.

Its trade-offs: only self-reported benchmarks; no SWE-Bench Verified, and thinking mode and sampling params can't be disabled. At $0.95 in / $4 out per million tokens, it sits in the budget price band.

The bottom line for this matchup

This is less "which is smarter" and more "which ecosystem fits." Gemma 4 (US) and Kimi K2.7 Code (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Gemma 4 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 and Kimi K2.7 Code 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 or Kimi K2.7 Code 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 leans toward self-hosted, data-private deployment while Kimi K2.7 Code leans toward long-horizon agentic software engineering, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Gemma 4 or Kimi K2.7 Code?

Gemma 4 is cheaper — Open weight (self-host / free) vs $0.95/$4 per 1M tokens.

Which has the bigger context window?

Kimi K2.7 Code — 256K vs 256K, about 1× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Gemma 4 and Kimi K2.7 Code together?

Yes — a multi-model platform like LumiChats gives you Gemma 4, Kimi K2.7 Code 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 or Kimi K2.7 Code?

Kimi K2.7 Code — released June 12, 2026, about 2 months after Gemma 4.

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.