Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. Pick Qwen3.6 35B A3B for extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost or runs at roughly 120 tokens per second on a single 24gb consumer gpu.
Llama 4 Scout (Meta, US) and Qwen3.6 35B A3B (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. Qwen3.6 35B A3B is a sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. Their biggest split is context window, and the breakdown below shows exactly how that plays out for your workload.
Key differences
Context window: Llama 4 Scout holds 38× more — 10M (~15,000 pages) vs 256K (~393 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Qwen3.6 35B A3B is the newer model by about 13 months (released April 16, 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.
Specifications
Spec
Llama 4 Scout
Qwen3.6 35B A3B
Provider
Meta (US)
Alibaba (China)
Released
April 2025
April 16, 2026
Context window
10M (~15,000 pages)
256K (~393 pages)
Price (in/out)
Open weight (self-host / free)
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, code
text, image, code
SWE-Bench Verified
Not published
73.4%
MRCR v2 @ 1M
15%
Not published
Who wins what
Largest advertised context (10M): Llama 4 Scout — Its 10M window holds about 38× more than Qwen3.6 35B A3B's 256K 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; Qwen3.6 35B A3B does not.
Extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost: Qwen3.6 35B A3B — A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware — and it is the newer of the two.
Runs at roughly 120 tokens per second on a single 24GB consumer GPU: Qwen3.6 35B A3B — Qwen3.6 35B A3B lists runs at roughly 120 tokens per second on a single 24GB consumer GPU among its strengths; Llama 4 Scout does not.
Apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN: Qwen3.6 35B A3B — Llama 4 Scout is comparatively weak here — ~15% on long-context multi-needle reasoning
Largest single-prompt input: Llama 4 Scout — Its 10M window is about 38× larger than Qwen3.6 35B A3B's 256K, fitting roughly 15,000 pages in one prompt.
Which should you pick?
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 extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost: Qwen3.6 35B A3B — That is its strongest area.
An enterprise with regional data-residency rules: Llama 4 Scout or Qwen3.6 35B A3B — 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.
Qwen3.6 35B A3B: where it fits
A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. Released April 16, 2026 by Alibaba, it is built for extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost, runs at roughly 120 tokens per second on a single 24GB consumer GPU, apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN, and preserves its reasoning across turns, which cuts the overhead of agentic loops.
Its trade-offs: loses to its smaller dense sibling Qwen3.6 27B on every coding benchmark, despite more total parameters, its SWE-Bench score comes from Alibaba's internal scaffold rather than the standard public harness, and all 35B parameters must stay resident in VRAM even though only 3B compute per token. 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." Llama 4 Scout (US) and Qwen3.6 35B A3B (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. 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.
Frequently asked questions
Is Llama 4 Scout or Qwen3.6 35B A3B better for coding?
Public SWE-Bench figures are not available for Llama 4 Scout, 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 Qwen3.6 35B A3B leans toward extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Scout or Qwen3.6 35B A3B?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Llama 4 Scout — 10M vs 256K, about 38× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Llama 4 Scout and Qwen3.6 35B A3B together?
Yes — a multi-model platform like LumiChats gives you Llama 4 Scout, Qwen3.6 35B A3B 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 Qwen3.6 35B A3B?
Qwen3.6 35B A3B — released April 16, 2026, about 13 months after Llama 4 Scout.
Llama 4 Scout vs Qwen3.6 35B A3B
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 Qwen3.6 35B A3B for extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost or runs at roughly 120 tokens per second on a single 24gb consumer gpu.
Llama 4 Scout (Meta, US) and Qwen3.6 35B A3B (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. Qwen3.6 35B A3B is a sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. Their biggest split is context window, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
▸Context window: Llama 4 Scout holds 38× more — 10M (~15,000 pages) vs 256K (~393 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Qwen3.6 35B A3B is the newer model by about 13 months (released April 16, 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
Qwen3.6 35B A3B
Provider
Meta (US)
Alibaba (China)
Released
April 2025
April 16, 2026
Context window
10M (~15,000 pages)
256K (~393 pages)
Price (in/out)
Open weight (self-host / free)
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, code
text, image, code
SWE-Bench Verified
Not published
73.4%
MRCR v2 @ 1M
15%
Not published
Who wins what
Largest advertised context (10M)
Llama 4 Scout
Its 10M window holds about 38× more than Qwen3.6 35B A3B's 256K 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; Qwen3.6 35B A3B does not.
Extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost
Qwen3.6 35B A3B
A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware — and it is the newer of the two.
Runs at roughly 120 tokens per second on a single 24GB consumer GPU
Qwen3.6 35B A3B
Qwen3.6 35B A3B lists runs at roughly 120 tokens per second on a single 24GB consumer GPU among its strengths; Llama 4 Scout does not.
Apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN
Qwen3.6 35B A3B
Llama 4 Scout is comparatively weak here — ~15% on long-context multi-needle reasoning
Largest single-prompt input
Llama 4 Scout
Its 10M window is about 38× larger than Qwen3.6 35B A3B's 256K, fitting roughly 15,000 pages in one prompt.
Which should you pick?
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 extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost
→ Qwen3.6 35B A3B
That is its strongest area.
An enterprise with regional data-residency rules
→ Llama 4 Scout or Qwen3.6 35B A3B
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.
Qwen3.6 35B A3B: where it fits
A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. Released April 16, 2026 by Alibaba, it is built for extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost, runs at roughly 120 tokens per second on a single 24GB consumer GPU, apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN, and preserves its reasoning across turns, which cuts the overhead of agentic loops.
Its trade-offs: loses to its smaller dense sibling Qwen3.6 27B on every coding benchmark, despite more total parameters, its SWE-Bench score comes from Alibaba's internal scaffold rather than the standard public harness, and all 35B parameters must stay resident in VRAM even though only 3B compute per token. 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." Llama 4 Scout (US) and Qwen3.6 35B A3B (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. 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 Qwen3.6 35B A3B 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.
Is Llama 4 Scout or Qwen3.6 35B A3B better for coding?
Public SWE-Bench figures are not available for Llama 4 Scout, 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 Qwen3.6 35B A3B leans toward extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Scout or Qwen3.6 35B A3B?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Llama 4 Scout — 10M vs 256K, about 38× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Llama 4 Scout and Qwen3.6 35B A3B together?
Yes — a multi-model platform like LumiChats gives you Llama 4 Scout, Qwen3.6 35B A3B 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 Qwen3.6 35B A3B?
Qwen3.6 35B A3B — released April 16, 2026, about 13 months after 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.