Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. On a tight budget at scale, Llama 4 Scout is the value pick.
GLM 5 (Z.ai, China) and Llama 4 Scout (Meta, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GLM 5 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. Llama 4 Scout is the 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Context window: Llama 4 Scout holds 50× more — 10M (~15,000 pages) vs 200K (~300 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: GLM 5 is the newer model by about 10 months (released February 11, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
GLM 5
Llama 4 Scout
Provider
Z.ai (China)
Meta (US)
Released
February 11, 2026
April 2025
Context window
200K (~300 pages)
10M (~15,000 pages)
Price (in/out)
$1/$3.2 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, code
SWE-Bench Verified
77.8%
Not published
MRCR v2 @ 1M
Not published
15%
Who wins what
Agentic planning and long-horizon coding workflows: GLM 5 — A core design strength of GLM 5.
Complex systems design and backend reasoning: GLM 5 — A core design strength of GLM 5.
Iterative self-correction on autonomous tasks: GLM 5 — A core design strength of GLM 5.
Largest advertised context (10M): Llama 4 Scout — A core design strength of Llama 4 Scout.
Open weights, single-GPU friendly: Llama 4 Scout — A core design strength of Llama 4 Scout.
Self-hosted, data-private deployment: Llama 4 Scout — A core design strength of Llama 4 Scout.
Lowest cost at scale: Llama 4 Scout — At Open weight (self-host / free), it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Llama 4 Scout — Its 10M window is about 50× larger, 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 GLM 5, 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 agentic planning and long-horizon coding workflows: GLM 5 — It is specifically built for that.
Anyone whose priority is largest advertised context (10m): Llama 4 Scout — That is its strongest area.
An enterprise with regional data-residency rules: Llama 4 Scout or GLM 5 — Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GLM 5: where it fits
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. Released February 11, 2026 by Z.ai, it is built for agentic planning and long-horizon coding workflows, complex systems design and backend reasoning, iterative self-correction on autonomous tasks, and open weights under the permissive MIT license.
Its trade-offs are real: 200K context trails 1M-context rivals, and quickly superseded by GLM-5.1 and GLM-5.2. At $1 in / $3.2 out per million tokens, it sits in the budget price band.
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: 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.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." GLM 5 (China) and Llama 4 Scout (US) 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.
Frequently asked questions
Is GLM 5 or Llama 4 Scout 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, GLM 5 leans toward agentic planning and long-horizon coding workflows while Llama 4 Scout leans toward largest advertised context (10m), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5 or Llama 4 Scout?
Llama 4 Scout is cheaper — $1/$3.2 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Llama 4 Scout — 10M vs 200K, about 50× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5 and Llama 4 Scout together?
Yes — a multi-model platform like LumiChats gives you GLM 5, Llama 4 Scout 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, GLM 5 or Llama 4 Scout?
GLM 5 — released February 11, 2026, about 10 months after Llama 4 Scout.
GLM 5 vs Llama 4 Scout
Z.ai · China | Meta · US · Updated June 2026
Quick verdict
Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. On a tight budget at scale, Llama 4 Scout is the value pick.
GLM 5 (Z.ai, China) and Llama 4 Scout (Meta, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GLM 5 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. Llama 4 Scout is the 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. 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 50× more — 10M (~15,000 pages) vs 200K (~300 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: GLM 5 is the newer model by about 10 months (released February 11, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
GLM 5
Llama 4 Scout
Provider
Z.ai (China)
Meta (US)
Released
February 11, 2026
April 2025
Context window
200K (~300 pages)
10M (~15,000 pages)
Price (in/out)
$1/$3.2 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, code
SWE-Bench Verified
77.8%
Not published
MRCR v2 @ 1M
Not published
15%
Who wins what
Agentic planning and long-horizon coding workflows
GLM 5
A core design strength of GLM 5.
Complex systems design and backend reasoning
GLM 5
A core design strength of GLM 5.
Iterative self-correction on autonomous tasks
GLM 5
A core design strength of GLM 5.
Largest advertised context (10M)
Llama 4 Scout
A core design strength of Llama 4 Scout.
Open weights, single-GPU friendly
Llama 4 Scout
A core design strength of Llama 4 Scout.
Self-hosted, data-private deployment
Llama 4 Scout
A core design strength of Llama 4 Scout.
Lowest cost at scale
Llama 4 Scout
At Open weight (self-host / free), it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Llama 4 Scout
Its 10M window is about 50× larger, 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 GLM 5, 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 agentic planning and long-horizon coding workflows
→ GLM 5
It is specifically built for that.
Anyone whose priority is largest advertised context (10m)
→ Llama 4 Scout
That is its strongest area.
An enterprise with regional data-residency rules
→ Llama 4 Scout or GLM 5
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GLM 5: where it fits
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. Released February 11, 2026 by Z.ai, it is built for agentic planning and long-horizon coding workflows, complex systems design and backend reasoning, iterative self-correction on autonomous tasks, and open weights under the permissive MIT license.
Its trade-offs are real: 200K context trails 1M-context rivals, and quickly superseded by GLM-5.1 and GLM-5.2. At $1 in / $3.2 out per million tokens, it sits in the budget price band.
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: 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.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." GLM 5 (China) and Llama 4 Scout (US) 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 GLM 5 and Llama 4 Scout 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.
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, GLM 5 leans toward agentic planning and long-horizon coding workflows while Llama 4 Scout leans toward largest advertised context (10m), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5 or Llama 4 Scout?
Llama 4 Scout is cheaper — $1/$3.2 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Llama 4 Scout — 10M vs 200K, about 50× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5 and Llama 4 Scout together?
Yes — a multi-model platform like LumiChats gives you GLM 5, Llama 4 Scout 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, GLM 5 or Llama 4 Scout?
GLM 5 — released February 11, 2026, about 10 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.