Llama 4 Scout vs MiniMax M3.1 Flash
Meta · US | MiniMax · China · Updated June 2026
Quick verdict
Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. Pick MiniMax M3.1 Flash for $0.15/$0.60 per million tokens (cached $0.015) with flat pricing regardless of prompt length or 428b-parameter moe (about 23b active) with weights on hugging face from october 23, 2026. On a tight budget at scale, Llama 4 Scout is the value pick.
Llama 4 Scout (Meta, US) and MiniMax M3.1 Flash (MiniMax, 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. MiniMax M3.1 Flash is miniMax's October 19, 2026 always-thinking agentic Flash model - 1M context, text/image/video input, $0.15/$0.60 and open weights under a community license. 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 10× more — 10M (~15,000 pages) vs 1M tokens (~1,500 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: MiniMax M3.1 Flash is the newer model by about 19 months (released October 19, 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 | MiniMax M3.1 Flash |
|---|---|---|
| Provider | Meta (US) | MiniMax (China) |
| Released | April 2025 | October 19, 2026 |
| Context window | 10M (~15,000 pages) | 1M tokens (~1,500 pages) |
| Price (in/out) | Open weight (self-host / free) | $0.15/$0.6 per 1M tokens |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, image, code | text, image, video, code |
| 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 10× more than MiniMax M3.1 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; MiniMax M3.1 Flash does not.
$0.15/$0.60 per million tokens (cached $0.015) with flat pricing regardless of prompt length
MiniMax M3.1 Flash
MiniMax's October 19, 2026 always-thinking agentic Flash model - 1M context, text/image/video input, $0.15/$0.60 and open weights under a community license — and it is the newer of the two.
428B-parameter MoE (about 23B active) with weights on Hugging Face from October 23, 2026
MiniMax M3.1 Flash
MiniMax M3.1 Flash lists 428B-parameter MoE (about 23B active) with weights on Hugging Face from October 23, 2026 among its strengths; Llama 4 Scout does not.
DeepSWE v1.1 of 73.7% and OSWorld V2 of 61.7 (MiniMax-reported)
MiniMax M3.1 Flash
MiniMax M3.1 Flash lists deepSWE v1.1 of 73.7% and OSWorld V2 of 61.7 (MiniMax-reported) 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 MiniMax M3.1 Flash's $0.15/$0.6 per 1M tokens.
Largest single-prompt input
Llama 4 Scout
Its 10M window is about 10× larger than MiniMax M3.1 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 MiniMax M3.1 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 $0.15/$0.60 per million tokens (cached $0.015) with flat pricing regardless of prompt length
→ MiniMax M3.1 Flash
That is its strongest area.
An enterprise with regional data-residency rules
→ Llama 4 Scout or MiniMax M3.1 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.
MiniMax M3.1 Flash: where it fits
MiniMax's October 19, 2026 always-thinking agentic Flash model - 1M context, text/image/video input, $0.15/$0.60 and open weights under a community license. Released October 19, 2026 by MiniMax, it is built for $0.15/$0.60 per million tokens (cached $0.015) with flat pricing regardless of prompt length, 428B-parameter MoE (about 23B active) with weights on Hugging Face from October 23, 2026, deepSWE v1.1 of 73.7% and OSWorld V2 of 61.7 (MiniMax-reported), and artificial Analysis Intelligence Index of 42, 13 points above MiniMax M3.
Its trade-offs: thinking is always on and default effort is max, which inflates output-token cost, behind DeepSeek V4.1 Flash 1017 on Artificial Analysis's independent index, custom MiniMax Community license: attribution required, and products above $20M annual revenue need written authorization, and benchmarks are mostly self-reported; no SWE-bench Verified figure published. At $0.15 in / $0.6 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 MiniMax M3.1 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 MiniMax M3.1 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 MiniMax M3.1 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 MiniMax M3.1 Flash leans toward $0.15/$0.60 per million tokens (cached $0.015) with flat pricing regardless of prompt length, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Scout or MiniMax M3.1 Flash?
Llama 4 Scout is cheaper — Open weight (self-host / free) vs $0.15/$0.6 per 1M tokens.
Which has the bigger context window?
Llama 4 Scout — 10M vs 1M tokens, about 10× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Llama 4 Scout and MiniMax M3.1 Flash together?
Yes — a multi-model platform like LumiChats gives you Llama 4 Scout, MiniMax M3.1 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 MiniMax M3.1 Flash?
MiniMax M3.1 Flash — released October 19, 2026, about 19 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.