Llama 4 Scout vs Qwen 3.8-Max
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 Qwen 3.8-Max for near-frontier quality at value pricing — artificial analysis intelligence index 58 or large 1m-token context with multimodal input (text, image, video). Choose Llama 4 Scout if you need self-hosting or data privacy; Qwen 3.8-Max if you want a managed API.
Llama 4 Scout (Meta, US) and Qwen 3.8-Max (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. Qwen 3.8-Max is alibaba's flagship mixture-of-experts model — near-frontier on independent tests (AA Index 58) at a fraction of US-flagship pricing, with open weights promised but not yet shipped. 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 Qwen 3.8-Max is API-metered at $2/$6 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 (~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: Qwen 3.8-Max is the newer model by about 16 months (released August 3, 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 | Qwen 3.8-Max |
|---|---|---|
| Provider | Meta (US) | Alibaba (China) |
| Released | April 2025 | August 3, 2026 |
| Context window | 10M (~15,000 pages) | 1M (~1,573 pages) |
| Price (in/out) | Open weight (self-host / free) | $2/$6 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 Qwen 3.8-Max's 1M in a single prompt.
Open weights, single-GPU friendly
Llama 4 Scout
Open weights make this possible at all — Qwen 3.8-Max 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.
Near-frontier quality at value pricing — Artificial Analysis Intelligence Index 58
Qwen 3.8-Max
Alibaba's flagship mixture-of-experts model — near-frontier on independent tests (AA Index 58) at a fraction of US-flagship pricing, with open weights promised but not yet shipped — and it is the newer of the two.
Large 1M-token context with multimodal input (text, image, video)
Qwen 3.8-Max
Llama 4 Scout is comparatively weak here — ~15% on long-context multi-needle reasoning
Mixture-of-experts design: ~2.4T total parameters (Alibaba-reported), a fraction active per token
Qwen 3.8-Max
Qwen 3.8-Max lists mixture-of-experts design: ~2.4T total parameters (Alibaba-reported), a fraction active per token 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 Qwen 3.8-Max's $2/$6 per 1M tokens.
Largest single-prompt input
Llama 4 Scout
Its 10M window is about 9.5× larger than Qwen 3.8-Max'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 Qwen 3.8-Max, 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; Qwen 3.8-Max is API-only.
Anyone whose priority is largest advertised context (10m)
→ Llama 4 Scout
It is specifically built for that.
Anyone whose priority is near-frontier quality at value pricing — artificial analysis intelligence index 58
→ Qwen 3.8-Max
That is its strongest area.
An enterprise with regional data-residency rules
→ Llama 4 Scout or Qwen 3.8-Max
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.
Qwen 3.8-Max: where it fits
Alibaba's flagship mixture-of-experts model — near-frontier on independent tests (AA Index 58) at a fraction of US-flagship pricing, with open weights promised but not yet shipped. Released August 3, 2026 by Alibaba, it is built for near-frontier quality at value pricing — Artificial Analysis Intelligence Index 58, large 1M-token context with multimodal input (text, image, video), mixture-of-experts design: ~2.4T total parameters (Alibaba-reported), a fraction active per token, and $2/$6 per million tokens — far below US flagships like Claude Opus 5 and GPT-5.6 Sol.
Its trade-offs: open weights were announced for release but had not shipped as of mid-August 2026 — a closed API for now, active-parameter count is not officially disclosed by Alibaba, flashier coding/agentic benchmarks (e.g. Terminal-Bench 86.6) are Alibaba's own, not independently reproduced, and trails the very top models (Opus 5, Fable 5, GPT-5.6 Sol) on independent tests. At $2 in / $6 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. Qwen 3.8-Max 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 Qwen 3.8-Max 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 Qwen 3.8-Max 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 Qwen 3.8-Max leans toward near-frontier quality at value pricing — artificial analysis intelligence index 58, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Scout or Qwen 3.8-Max?
Llama 4 Scout is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Qwen 3.8-Max is API-metered at $2/$6 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, 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 Qwen 3.8-Max together?
Yes — a multi-model platform like LumiChats gives you Llama 4 Scout, Qwen 3.8-Max 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 Qwen 3.8-Max?
Qwen 3.8-Max — released August 3, 2026, about 16 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.