Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. Pick Laguna XS 2.1 for remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters or open weights under openmdw-1.1, shipped day one in bf16, fp8, nvfp4 and int4 across every major runtime. On a tight budget at scale, Laguna XS 2.1 is the value pick.
GLM 5 (Z.ai, China) and Laguna XS 2.1 (Poolside, 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. Laguna XS 2.1 is a 33B open-weight coding MoE running on 3B active parameters — 70.9% SWE-Bench Verified and very cheap, but unproven. They diverge most on price, context window and coding benchmarks — each quantified below from the models' real specs.
Key differences
Price: Laguna XS 2.1 is about 10× cheaper on input ($0.1/$0.2 per 1M tokens vs $1/$3.2 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
Context window: Laguna XS 2.1 holds 1.3× more — 256K (~393 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.
Coding: GLM 5 leads SWE-Bench Verified by 6.9 points (77.8% vs 70.9%) — a real edge on hard, real-world software tasks.
Recency: Laguna XS 2.1 is the newer model by about 5 months (released July 2, 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
Laguna XS 2.1
Provider
Z.ai (China)
Poolside (US)
Released
February 11, 2026
July 2, 2026
Context window
200K (~300 pages)
256K (~393 pages)
Price (in/out)
$1/$3.2 per 1M tokens
$0.1/$0.2 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
77.8%
70.9%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Agentic planning and long-horizon coding workflows: GLM 5 — It scores 77.8% on SWE-Bench Verified against Laguna XS 2.1's 70.9% — a 6.9-point edge on real repository work.
Complex systems design and backend reasoning: GLM 5 — Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding — and it leads SWE-Bench Verified 77.8% to 70.9%.
Iterative self-correction on autonomous tasks: GLM 5 — GLM 5 lists iterative self-correction on autonomous tasks among its strengths; Laguna XS 2.1 does not.
Remarkable efficiency — 70.9% on SWE-Bench Verified from only 3B active parameters: Laguna XS 2.1 — A 33B open-weight coding MoE running on 3B active parameters — 70.9% SWE-Bench Verified and very cheap, but unproven — and it runs cheaper at $0.1/$0.2 per 1M tokens.
Open weights under OpenMDW-1.1, shipped day one in BF16, FP8, NVFP4 and INT4 across every major runtime: Laguna XS 2.1 — A 33B open-weight coding MoE running on 3B active parameters — 70.9% SWE-Bench Verified and very cheap, but unproven — and it carries the larger 256K context.
Cheap even on the paid tier, at roughly a sixth of GLM 4.7's input price: Laguna XS 2.1 — At $0.1/$0.2 per 1M tokens it undercuts GLM 5 ($1/$3.2 per 1M tokens), and that gap compounds at volume.
Lowest cost at scale: Laguna XS 2.1 — At $0.1/$0.2 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Laguna XS 2.1 — Its 256K window is about 1.3× larger than GLM 5's 200K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Laguna XS 2.1 — At $0.1/$0.2 per 1M tokens it undercuts GLM 5, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Laguna XS 2.1 — Larger 256K 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 remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters: Laguna XS 2.1 — That is its strongest area.
An enterprise with regional data-residency rules: Laguna XS 2.1 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.
Laguna XS 2.1: where it fits
A 33B open-weight coding MoE running on 3B active parameters — 70.9% SWE-Bench Verified and very cheap, but unproven. Released July 2, 2026 by Poolside, it is built for remarkable efficiency — 70.9% on SWE-Bench Verified from only 3B active parameters, open weights under OpenMDW-1.1, shipped day one in BF16, FP8, NVFP4 and INT4 across every major runtime, cheap even on the paid tier, at roughly a sixth of GLM 4.7's input price, and unusually transparent evaluation — it publishes its harness, step limits, and sandbox specs.
Its trade-offs: weeks old with no independent replication; every published score traces back to Poolside's own harness, the free endpoint trains on your inputs and outputs — disqualifying for proprietary code, which is its main use case, and weak on harder agentic work (37.5 on Terminal-Bench 2.0), and its gain over XS.2 is barely above noise. At $0.1 in / $0.2 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." GLM 5 (China) and Laguna XS 2.1 (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Laguna XS 2.1 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 Laguna XS 2.1 better for coding?
On SWE-Bench Verified, GLM 5 scores 77.8% and Laguna XS 2.1 scores 70.9% — GLM 5 has the measurable edge.
Which is cheaper, GLM 5 or Laguna XS 2.1?
Laguna XS 2.1 is cheaper — $1/$3.2 per 1M tokens vs $0.1/$0.2 per 1M tokens, roughly 10× apart on input.
Which has the bigger context window?
Laguna XS 2.1 — 256K vs 200K, about 1.3× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5 and Laguna XS 2.1 together?
Yes — a multi-model platform like LumiChats gives you GLM 5, Laguna XS 2.1 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 Laguna XS 2.1?
Laguna XS 2.1 — released July 2, 2026, about 5 months after GLM 5.
GLM 5 vs Laguna XS 2.1
Z.ai · China | Poolside · 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 Laguna XS 2.1 for remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters or open weights under openmdw-1.1, shipped day one in bf16, fp8, nvfp4 and int4 across every major runtime. On a tight budget at scale, Laguna XS 2.1 is the value pick.
GLM 5 (Z.ai, China) and Laguna XS 2.1 (Poolside, 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. Laguna XS 2.1 is a 33B open-weight coding MoE running on 3B active parameters — 70.9% SWE-Bench Verified and very cheap, but unproven. They diverge most on price, context window and coding benchmarks — each quantified below from the models' real specs.
Key differences at a glance
▸Price: Laguna XS 2.1 is about 10× cheaper on input ($0.1/$0.2 per 1M tokens vs $1/$3.2 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: Laguna XS 2.1 holds 1.3× more — 256K (~393 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.
▸Coding: GLM 5 leads SWE-Bench Verified by 6.9 points (77.8% vs 70.9%) — a real edge on hard, real-world software tasks.
▸Recency: Laguna XS 2.1 is the newer model by about 5 months (released July 2, 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
Laguna XS 2.1
Provider
Z.ai (China)
Poolside (US)
Released
February 11, 2026
July 2, 2026
Context window
200K (~300 pages)
256K (~393 pages)
Price (in/out)
$1/$3.2 per 1M tokens
$0.1/$0.2 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
77.8%
70.9%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Agentic planning and long-horizon coding workflows
GLM 5
It scores 77.8% on SWE-Bench Verified against Laguna XS 2.1's 70.9% — a 6.9-point edge on real repository work.
Complex systems design and backend reasoning
GLM 5
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding — and it leads SWE-Bench Verified 77.8% to 70.9%.
Iterative self-correction on autonomous tasks
GLM 5
GLM 5 lists iterative self-correction on autonomous tasks among its strengths; Laguna XS 2.1 does not.
Remarkable efficiency — 70.9% on SWE-Bench Verified from only 3B active parameters
Laguna XS 2.1
A 33B open-weight coding MoE running on 3B active parameters — 70.9% SWE-Bench Verified and very cheap, but unproven — and it runs cheaper at $0.1/$0.2 per 1M tokens.
Open weights under OpenMDW-1.1, shipped day one in BF16, FP8, NVFP4 and INT4 across every major runtime
Laguna XS 2.1
A 33B open-weight coding MoE running on 3B active parameters — 70.9% SWE-Bench Verified and very cheap, but unproven — and it carries the larger 256K context.
Cheap even on the paid tier, at roughly a sixth of GLM 4.7's input price
Laguna XS 2.1
At $0.1/$0.2 per 1M tokens it undercuts GLM 5 ($1/$3.2 per 1M tokens), and that gap compounds at volume.
Lowest cost at scale
Laguna XS 2.1
At $0.1/$0.2 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Laguna XS 2.1
Its 256K window is about 1.3× larger than GLM 5's 200K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Laguna XS 2.1
At $0.1/$0.2 per 1M tokens it undercuts GLM 5, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Laguna XS 2.1
Larger 256K 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 remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters
→ Laguna XS 2.1
That is its strongest area.
An enterprise with regional data-residency rules
→ Laguna XS 2.1 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.
Laguna XS 2.1: where it fits
A 33B open-weight coding MoE running on 3B active parameters — 70.9% SWE-Bench Verified and very cheap, but unproven. Released July 2, 2026 by Poolside, it is built for remarkable efficiency — 70.9% on SWE-Bench Verified from only 3B active parameters, open weights under OpenMDW-1.1, shipped day one in BF16, FP8, NVFP4 and INT4 across every major runtime, cheap even on the paid tier, at roughly a sixth of GLM 4.7's input price, and unusually transparent evaluation — it publishes its harness, step limits, and sandbox specs.
Its trade-offs: weeks old with no independent replication; every published score traces back to Poolside's own harness, the free endpoint trains on your inputs and outputs — disqualifying for proprietary code, which is its main use case, and weak on harder agentic work (37.5 on Terminal-Bench 2.0), and its gain over XS.2 is barely above noise. At $0.1 in / $0.2 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." GLM 5 (China) and Laguna XS 2.1 (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Laguna XS 2.1 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 Laguna XS 2.1 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.
On SWE-Bench Verified, GLM 5 scores 77.8% and Laguna XS 2.1 scores 70.9% — GLM 5 has the measurable edge.
Which is cheaper, GLM 5 or Laguna XS 2.1?
Laguna XS 2.1 is cheaper — $1/$3.2 per 1M tokens vs $0.1/$0.2 per 1M tokens, roughly 10× apart on input.
Which has the bigger context window?
Laguna XS 2.1 — 256K vs 200K, about 1.3× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5 and Laguna XS 2.1 together?
Yes — a multi-model platform like LumiChats gives you GLM 5, Laguna XS 2.1 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 Laguna XS 2.1?
Laguna XS 2.1 — released July 2, 2026, about 5 months after GLM 5.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.