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. Pick Ling-2.6-1T for a trillion-parameter moe (1t total / ~50-63b active) from ant group - the fintech giant behind alipay - entering ai at frontier scale or fully open weights under the permissive mit license, unusual for a model this large. On a tight budget at scale, Laguna XS 2.1 is the value pick.
Laguna XS 2.1 (Poolside, US) and Ling-2.6-1T (Ant Group, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Ling-2.6-1T is ant Group's trillion-parameter, MIT-licensed open model - a fintech giant's frontier-scale entry into the Chinese open-weight AI race. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
Key differences
Price: Laguna XS 2.1 is about 3× cheaper on input ($0.1/$0.2 per 1M tokens vs $0.3/$2.5 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: both advertise 256K (~393 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Recency: Laguna XS 2.1 is the newer model by about 3 months (released July 2, 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
Laguna XS 2.1
Ling-2.6-1T
Provider
Poolside (US)
Ant Group (China)
Released
July 2, 2026
April 2026
Context window
256K (~393 pages)
256K (~393 pages)
Price (in/out)
$0.1/$0.2 per 1M tokens
$0.3/$2.5 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
70.9%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
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 — Ling-2.6-1T is comparatively weak here — exact release date is disputed across sources (reports range from mid to late April 2026)
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 Ling-2.6-1T ($0.3/$2.5 per 1M tokens), and that gap compounds at volume.
A trillion-parameter MoE (1T total / ~50-63B active) from Ant Group - the fintech giant behind Alipay - entering AI at frontier scale: Ling-2.6-1T — Ling-2.6-1T lists a trillion-parameter MoE (1T total / ~50-63B active) from Ant Group - the fintech giant behind Alipay - entering AI at frontier scale among its strengths; Laguna XS 2.1 does not.
Fully open weights under the permissive MIT license, unusual for a model this large: Ling-2.6-1T — Ling-2.6-1T lists fully open weights under the permissive MIT license, unusual for a model this large among its strengths; Laguna XS 2.1 does not.
A companion 'Ring' reasoning variant claims gold-medal-level IMO/CMO math scores using a hybrid linear-attention architecture: Ling-2.6-1T — Ling-2.6-1T lists a companion 'Ring' reasoning variant claims gold-medal-level IMO/CMO math scores using a hybrid linear-attention architecture among its strengths; Laguna XS 2.1 does not.
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.
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 Ling-2.6-1T, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters: Laguna XS 2.1 — It is specifically built for that.
Anyone whose priority is a trillion-parameter moe (1t total / ~50-63b active) from ant group - the fintech giant behind alipay - entering ai at frontier scale: Ling-2.6-1T — That is its strongest area.
An enterprise with regional data-residency rules: Laguna XS 2.1 or Ling-2.6-1T — Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
Ling-2.6-1T: where it fits
Ant Group's trillion-parameter, MIT-licensed open model - a fintech giant's frontier-scale entry into the Chinese open-weight AI race. Released April 2026 by Ant Group, it is built for a trillion-parameter MoE (1T total / ~50-63B active) from Ant Group - the fintech giant behind Alipay - entering AI at frontier scale, fully open weights under the permissive MIT license, unusual for a model this large, a companion 'Ring' reasoning variant claims gold-medal-level IMO/CMO math scores using a hybrid linear-attention architecture, and a major Chinese lab with real financial backing, distinct from DeepSeek, Alibaba, or Moonshot.
Its trade-offs: pricing shown is third-party hosting, not an official Ant Group rate card, newer entrant to LLMs specifically - less track record than dedicated AI labs, and exact release date is disputed across sources (reports range from mid to late April 2026). At $0.3 in / $2.5 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." Laguna XS 2.1 (US) and Ling-2.6-1T (China) 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 Laguna XS 2.1 or Ling-2.6-1T better for coding?
Public SWE-Bench figures are not available for Ling-2.6-1T, so the honest test is your own repository — run an identical real bug through both. By design, Laguna XS 2.1 leans toward remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters while Ling-2.6-1T leans toward a trillion-parameter moe (1t total / ~50-63b active) from ant group - the fintech giant behind alipay - entering ai at frontier scale, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Laguna XS 2.1 or Ling-2.6-1T?
Laguna XS 2.1 is cheaper — $0.1/$0.2 per 1M tokens vs $0.3/$2.5 per 1M tokens, roughly 3× apart on input.
Which has the bigger context window?
Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Laguna XS 2.1 and Ling-2.6-1T together?
Yes — a multi-model platform like LumiChats gives you Laguna XS 2.1, Ling-2.6-1T 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, Laguna XS 2.1 or Ling-2.6-1T?
Laguna XS 2.1 — released July 2, 2026, about 3 months after Ling-2.6-1T.
Laguna XS 2.1 vs Ling-2.6-1T
Poolside · US | Ant Group · China · Updated June 2026
Quick verdict
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. Pick Ling-2.6-1T for a trillion-parameter moe (1t total / ~50-63b active) from ant group - the fintech giant behind alipay - entering ai at frontier scale or fully open weights under the permissive mit license, unusual for a model this large. On a tight budget at scale, Laguna XS 2.1 is the value pick.
Laguna XS 2.1 (Poolside, US) and Ling-2.6-1T (Ant Group, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Ling-2.6-1T is ant Group's trillion-parameter, MIT-licensed open model - a fintech giant's frontier-scale entry into the Chinese open-weight AI race. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
▸Price: Laguna XS 2.1 is about 3× cheaper on input ($0.1/$0.2 per 1M tokens vs $0.3/$2.5 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: both advertise 256K (~393 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
▸Recency: Laguna XS 2.1 is the newer model by about 3 months (released July 2, 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
Laguna XS 2.1
Ling-2.6-1T
Provider
Poolside (US)
Ant Group (China)
Released
July 2, 2026
April 2026
Context window
256K (~393 pages)
256K (~393 pages)
Price (in/out)
$0.1/$0.2 per 1M tokens
$0.3/$2.5 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
70.9%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
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
Ling-2.6-1T is comparatively weak here — exact release date is disputed across sources (reports range from mid to late April 2026)
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 Ling-2.6-1T ($0.3/$2.5 per 1M tokens), and that gap compounds at volume.
A trillion-parameter MoE (1T total / ~50-63B active) from Ant Group - the fintech giant behind Alipay - entering AI at frontier scale
Ling-2.6-1T
Ling-2.6-1T lists a trillion-parameter MoE (1T total / ~50-63B active) from Ant Group - the fintech giant behind Alipay - entering AI at frontier scale among its strengths; Laguna XS 2.1 does not.
Fully open weights under the permissive MIT license, unusual for a model this large
Ling-2.6-1T
Ling-2.6-1T lists fully open weights under the permissive MIT license, unusual for a model this large among its strengths; Laguna XS 2.1 does not.
A companion 'Ring' reasoning variant claims gold-medal-level IMO/CMO math scores using a hybrid linear-attention architecture
Ling-2.6-1T
Ling-2.6-1T lists a companion 'Ring' reasoning variant claims gold-medal-level IMO/CMO math scores using a hybrid linear-attention architecture among its strengths; Laguna XS 2.1 does not.
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.
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 Ling-2.6-1T, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters
→ Laguna XS 2.1
It is specifically built for that.
Anyone whose priority is a trillion-parameter moe (1t total / ~50-63b active) from ant group - the fintech giant behind alipay - entering ai at frontier scale
→ Ling-2.6-1T
That is its strongest area.
An enterprise with regional data-residency rules
→ Laguna XS 2.1 or Ling-2.6-1T
Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
Ling-2.6-1T: where it fits
Ant Group's trillion-parameter, MIT-licensed open model - a fintech giant's frontier-scale entry into the Chinese open-weight AI race. Released April 2026 by Ant Group, it is built for a trillion-parameter MoE (1T total / ~50-63B active) from Ant Group - the fintech giant behind Alipay - entering AI at frontier scale, fully open weights under the permissive MIT license, unusual for a model this large, a companion 'Ring' reasoning variant claims gold-medal-level IMO/CMO math scores using a hybrid linear-attention architecture, and a major Chinese lab with real financial backing, distinct from DeepSeek, Alibaba, or Moonshot.
Its trade-offs: pricing shown is third-party hosting, not an official Ant Group rate card, newer entrant to LLMs specifically - less track record than dedicated AI labs, and exact release date is disputed across sources (reports range from mid to late April 2026). At $0.3 in / $2.5 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." Laguna XS 2.1 (US) and Ling-2.6-1T (China) 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 Laguna XS 2.1 and Ling-2.6-1T 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 Laguna XS 2.1 or Ling-2.6-1T better for coding?
Public SWE-Bench figures are not available for Ling-2.6-1T, so the honest test is your own repository — run an identical real bug through both. By design, Laguna XS 2.1 leans toward remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters while Ling-2.6-1T leans toward a trillion-parameter moe (1t total / ~50-63b active) from ant group - the fintech giant behind alipay - entering ai at frontier scale, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Laguna XS 2.1 or Ling-2.6-1T?
Laguna XS 2.1 is cheaper — $0.1/$0.2 per 1M tokens vs $0.3/$2.5 per 1M tokens, roughly 3× apart on input.
Which has the bigger context window?
Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Laguna XS 2.1 and Ling-2.6-1T together?
Yes — a multi-model platform like LumiChats gives you Laguna XS 2.1, Ling-2.6-1T 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, Laguna XS 2.1 or Ling-2.6-1T?
Laguna XS 2.1 — released July 2, 2026, about 3 months after Ling-2.6-1T.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.