Llama 4 Scout vs Qwen3.8 Max Prime

Meta · US | Alibaba · China · Updated June 2026

Quick verdict

Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. Pick Qwen3.8 Max Prime for same 2.4t-parameter moe architecture and full capability set as qwen3.8 max, repackaged for higher-throughput serving or alibaba advertises 1.5-2x higher output throughput than standard qwen3.8 max for latency-sensitive workloads. Choose Llama 4 Scout if you need self-hosting or data privacy; Qwen3.8 Max Prime if you want a managed API.

Llama 4 Scout (Meta, US) and Qwen3.8 Max Prime (Alibaba, 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. Qwen3.8 Max Prime is alibaba's September 23, 2026 high-throughput variant of Qwen3.8 Max — identical 2.4T-parameter architecture at double the price, with throughput gains public testing hasn't yet confirmed. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.

Key differences at a glance

▸Cost model: Llama 4 Scout ships open weights you can self-host (hardware cost only, no per-token fee), while Qwen3.8 Max Prime is API-metered at $4/$12 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸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: Qwen3.8 Max Prime is the newer model by about 18 months (released September 23, 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	Qwen3.8 Max Prime
Provider	Meta (US)	Alibaba (China)
Released	April 2025	September 23, 2026
Context window	10M (~15,000 pages)	1M tokens (~1,573 pages)
Price (in/out)	Open weight (self-host / free)	$4/$12 per 1M tokens
Open weight?	Yes — self-hostable	No — API only
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 9.5× more than Qwen3.8 Max Prime's 1M tokens in a single prompt.

Open weights, single-GPU friendly

Llama 4 Scout

Open weights make this possible at all — Qwen3.8 Max Prime is API-only, so it cannot leave the vendor's servers.

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.

Same 2.4T-parameter MoE architecture and full capability set as Qwen3.8 Max, repackaged for higher-throughput serving

Qwen3.8 Max Prime

Alibaba advertises 1.5-2x higher output throughput than standard Qwen3.8 Max for latency-sensitive workloads

Qwen3.8 Max Prime

Qwen3.8 Max Prime lists alibaba advertises 1.5-2x higher output throughput than standard Qwen3.8 Max for latency-sensitive workloads among its strengths; Llama 4 Scout does not.

1M-token context window with up to 131,072 output tokens, reasoning enabled by default

Qwen3.8 Max Prime

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

Lowest cost at scale

Llama 4 Scout

Its weights are open, so at volume you pay for your own hardware instead of Qwen3.8 Max Prime's $4/$12 per 1M tokens.

Largest single-prompt input

Llama 4 Scout

Its 10M window is about 9.5× larger than Qwen3.8 Max Prime'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 Qwen3.8 Max Prime, 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.

A team with data-privacy or self-hosting needs

→ Llama 4 Scout

Open weights let you run it on your own hardware; Qwen3.8 Max Prime is API-only.

Anyone whose priority is largest advertised context (10m)

→ Llama 4 Scout

It is specifically built for that.

Anyone whose priority is same 2.4t-parameter moe architecture and full capability set as qwen3.8 max, repackaged for higher-throughput serving

→ Qwen3.8 Max Prime

That is its strongest area.

An enterprise with regional data-residency rules

→ Llama 4 Scout or Qwen3.8 Max Prime

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.

Qwen3.8 Max Prime: where it fits

Alibaba's September 23, 2026 high-throughput variant of Qwen3.8 Max — identical 2.4T-parameter architecture at double the price, with throughput gains public testing hasn't yet confirmed. Released September 23, 2026 by Alibaba, it is built for same 2.4T-parameter MoE architecture and full capability set as Qwen3.8 Max, repackaged for higher-throughput serving, alibaba advertises 1.5-2x higher output throughput than standard Qwen3.8 Max for latency-sensitive workloads, 1M-token context window with up to 131,072 output tokens, reasoning enabled by default, and accepts text, image, and video input alongside text output, inherited unchanged from the base model.

Its trade-offs: priced exactly double standard Qwen3.8 Max ($4/$12 vs $2/$6) for a throughput claim the only public measurements available don't show as measurably faster, no official Alibaba announcement — Alibaba Cloud's Model Studio documentation still lists only qwen3.8-max and its September 2 snapshot, suggesting minimal official backing, like the base model, open weights have been promised but not shipped — closed API only, and purely a speed tier, not a capability upgrade — same benchmark ceiling as the cheaper base model. At $4 in / $12 out per million tokens, it sits in the mid price band.

The bottom line for this matchup

The defining split here is open vs. closed. Llama 4 Scout gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Qwen3.8 Max Prime gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.

Want both Llama 4 Scout and Qwen3.8 Max Prime 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 Llama 4 Scout or Qwen3.8 Max Prime 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 Qwen3.8 Max Prime leans toward same 2.4t-parameter moe architecture and full capability set as qwen3.8 max, repackaged for higher-throughput serving, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Llama 4 Scout or Qwen3.8 Max Prime?

Llama 4 Scout is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Qwen3.8 Max Prime is API-metered at $4/$12 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.

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 Qwen3.8 Max Prime together?

Yes — a multi-model platform like LumiChats gives you Llama 4 Scout, Qwen3.8 Max Prime 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 Qwen3.8 Max Prime?

Qwen3.8 Max Prime — released September 23, 2026, about 18 months after Llama 4 Scout.

Related comparisons

Claude Sonnet 5.5 vs Qwen3.8 Max Prime Claude Sonnet 5.5 vs Llama 4 Scout GPT-6.1 Sol vs Qwen3.8 Max Prime GPT-6.1 Sol vs Llama 4 Scout Claude Haiku 5.5 vs Qwen3.8 Max Prime Claude Haiku 5.5 vs Llama 4 Scout Mistral Large 4 vs Qwen3.8 Max Prime Mistral Large 4 vs Llama 4 Scout

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