Kimi K3 vs Llama 4 Scout

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

Quick verdict

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). Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. On a tight budget at scale, Llama 4 Scout is the value pick.

Kimi K3 (Moonshot AI, China) and Llama 4 Scout (Meta, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Llama 4 Scout is the 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. They diverge most on price and context window — each quantified below from the models' real specs.

Key differences at a glance

▸Context window: Llama 4 Scout holds 9.5× more — 10M (~15,000 pages) vs 1M (~1,573 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Kimi K3 is the newer model by about 16 months (released July 27, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.

Side-by-side specs

Spec	Kimi K3	Llama 4 Scout
Provider	Moonshot AI (China)	Meta (US)
Released	July 27, 2026	April 2025
Context window	1M (~1,573 pages)	10M (~15,000 pages)
Price (in/out)	$3/$15 per 1M tokens	Open weight (self-host / free)
Open weight?	Yes — self-hostable	Yes — self-hostable
Modalities	text, image, video, code	text, image, code
SWE-Bench Verified	Not published	Not published
MRCR v2 @ 1M	Not published	15%

Who wins what

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 is the newer of the two.

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

Kimi K3

Llama 4 Scout is comparatively weak here — ~15% on long-context multi-needle reasoning

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

Kimi K3

Kimi K3 lists vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness among its strengths; Llama 4 Scout does not.

Largest advertised context (10M)

Llama 4 Scout

Its 10M window holds about 9.5× more than Kimi K3's 1M in a single prompt.

Open weights, single-GPU friendly

Llama 4 Scout

Kimi K3 is comparatively weak here — 2.8T params need serious hardware to self-host — weights are free, running is not

Self-hosted, data-private deployment

Llama 4 Scout

The 10M-token open-weight giant — enormous on paper, but usable recall is far smaller — and it carries the larger 10M context.

Lowest cost at scale

Llama 4 Scout

Its weights are open, so at volume you pay for your own hardware instead of Kimi K3's $3/$15 per 1M tokens.

Largest single-prompt input

Llama 4 Scout

Its 10M window is about 9.5× larger than Kimi K3's 1M, fitting roughly 15,000 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

→ Llama 4 Scout

At Open weight (self-host / free) it undercuts Kimi K3, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ Llama 4 Scout

Larger 10M window fits more in one prompt.

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

→ Kimi K3

It is specifically built for that.

Anyone whose priority is largest advertised context (10m)

→ Llama 4 Scout

That is its strongest area.

An enterprise with regional data-residency rules

→ Llama 4 Scout or Kimi K3

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

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 are real: 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.

Llama 4 Scout: where it fits

The 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. Released April 2025 by Meta, it is built for largest advertised context (10M), open weights, single-GPU friendly, self-hosted, data-private deployment, and retrieval over very long inputs.

Its trade-offs: effective recall degrades far below 10M, and ~15% on long-context multi-needle reasoning. As an open-weight model, its running cost is your own hardware rather than a per-token fee.

The bottom line for this matchup

This is less "which is smarter" and more "which ecosystem fits." Kimi K3 (China) and Llama 4 Scout (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Llama 4 Scout 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 Kimi K3 and Llama 4 Scout 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 Kimi K3 or Llama 4 Scout 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, Kimi K3 leans toward largest open-weight model at release — 2.8t sparse moe, self-hostable while Llama 4 Scout leans toward largest advertised context (10m), and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Kimi K3 or Llama 4 Scout?

Llama 4 Scout is cheaper — $3/$15 per 1M tokens vs Open weight (self-host / free).

Which has the bigger context window?

Llama 4 Scout — 10M vs 1M, about 9.5× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Kimi K3 and Llama 4 Scout together?

Yes — a multi-model platform like LumiChats gives you Kimi K3, Llama 4 Scout 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, Kimi K3 or Llama 4 Scout?

Kimi K3 — released July 27, 2026, about 16 months after Llama 4 Scout.

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