Llama 4 Scout vs MiMo-V2.6-Flash
Meta · US | Xiaomi · China · Updated June 2026
Quick verdict
Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. Pick MiMo-V2.6-Flash for same natively omnimodal design as pro (text, image, video, audio) at a fraction of the size and price or 309b total parameters, 15b active per token (sparse moe) — a hybrid attention mechanism for efficiency. On a tight budget at scale, Llama 4 Scout is the value pick.
Llama 4 Scout (Meta, US) and MiMo-V2.6-Flash (Xiaomi, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Llama 4 Scout is the 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. MiMo-V2.6-Flash is xiaomi's efficient omnimodal MoE model — 309B parameters, 15B active, MIT-licensed, priced at $0.14/$0.28 per million tokens. 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 tokens (~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: MiMo-V2.6-Flash is the newer model by about 18 months (released September 21, 2026), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | Llama 4 Scout | MiMo-V2.6-Flash |
|---|---|---|
| Provider | Meta (US) | Xiaomi (China) |
| Released | April 2025 | September 21, 2026 |
| Context window | 10M (~15,000 pages) | 1M tokens (~1,573 pages) |
| Price (in/out) | Open weight (self-host / free) | $0.14/$0.28 per 1M tokens |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, image, code | text, image, video, audio |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | 15% | Not published |
Who wins what
Largest advertised context (10M)
Llama 4 Scout
Its 10M window holds about 9.5× more than MiMo-V2.6-Flash's 1M tokens in a single prompt.
Open weights, single-GPU friendly
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.
Self-hosted, data-private deployment
Llama 4 Scout
Llama 4 Scout lists self-hosted, data-private deployment among its strengths; MiMo-V2.6-Flash does not.
Same natively omnimodal design as Pro (text, image, video, audio) at a fraction of the size and price
MiMo-V2.6-Flash
Xiaomi's efficient omnimodal MoE model — 309B parameters, 15B active, MIT-licensed, priced at $0.14/$0.28 per million tokens — and it is the newer of the two.
309B total parameters, 15B active per token (sparse MoE) — a hybrid attention mechanism for efficiency
MiMo-V2.6-Flash
MiMo-V2.6-Flash lists 309B total parameters, 15B active per token (sparse MoE) — a hybrid attention mechanism for efficiency among its strengths; Llama 4 Scout does not.
MIT-licensed, self-hostable, and among the cheapest omnimodal options at $0.14/$0.28 per million tokens
MiMo-V2.6-Flash
MiMo-V2.6-Flash lists mIT-licensed, self-hostable, and among the cheapest omnimodal options at $0.14/$0.28 per million tokens among its strengths; Llama 4 Scout does not.
Lowest cost at scale
Llama 4 Scout
Its weights are open, so at volume you pay for your own hardware instead of MiMo-V2.6-Flash's $0.14/$0.28 per 1M tokens.
Largest single-prompt input
Llama 4 Scout
Its 10M window is about 9.5× larger than MiMo-V2.6-Flash's 1M tokens, 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 MiMo-V2.6-Flash, 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 advertised context (10m)
→ Llama 4 Scout
It is specifically built for that.
Anyone whose priority is same natively omnimodal design as pro (text, image, video, audio) at a fraction of the size and price
→ MiMo-V2.6-Flash
That is its strongest area.
An enterprise with regional data-residency rules
→ Llama 4 Scout or MiMo-V2.6-Flash
Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
MiMo-V2.6-Flash: where it fits
Xiaomi's efficient omnimodal MoE model — 309B parameters, 15B active, MIT-licensed, priced at $0.14/$0.28 per million tokens. Released September 21, 2026 by Xiaomi, it is built for same natively omnimodal design as Pro (text, image, video, audio) at a fraction of the size and price, 309B total parameters, 15B active per token (sparse MoE) — a hybrid attention mechanism for efficiency, and mIT-licensed, self-hostable, and among the cheapest omnimodal options at $0.14/$0.28 per million tokens.
Its trade-offs: lower capacity than Pro — expect a real quality gap on the hardest reasoning and generation tasks, and same caveat as Pro: benchmark claims are largely self-reported by Xiaomi at launch, not yet independently verified at scale. At $0.14 in / $0.28 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." Llama 4 Scout (US) and MiMo-V2.6-Flash (China) 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 Llama 4 Scout and MiMo-V2.6-Flash 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 pricingFrequently asked questions
Is Llama 4 Scout or MiMo-V2.6-Flash 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, Llama 4 Scout leans toward largest advertised context (10m) while MiMo-V2.6-Flash leans toward same natively omnimodal design as pro (text, image, video, audio) at a fraction of the size and price, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Scout or MiMo-V2.6-Flash?
Llama 4 Scout is cheaper — Open weight (self-host / free) vs $0.14/$0.28 per 1M tokens.
Which has the bigger context window?
Llama 4 Scout — 10M vs 1M tokens, about 9.5× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Llama 4 Scout and MiMo-V2.6-Flash together?
Yes — a multi-model platform like LumiChats gives you Llama 4 Scout, MiMo-V2.6-Flash 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, Llama 4 Scout or MiMo-V2.6-Flash?
MiMo-V2.6-Flash — released September 21, 2026, about 18 months after Llama 4 Scout.
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.