Laguna XS 2.1 vs Ling 3.1 Flash

Poolside · US | InclusionAI (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 3.1 Flash for hybrid-reasoning moe — 560b total parameters, ~25b active per token, aimed at agents, tool use, and long-document work or artificial analysis intelligence index score of 41 — above average for its price tier. Choose Laguna XS 2.1 if you need self-hosting or data privacy; Ling 3.1 Flash if you want a managed API.

Laguna XS 2.1 (Poolside, US) and Ling 3.1 Flash (InclusionAI (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 3.1 Flash is inclusionAI's (Ant Group) September 30, 2026 hybrid-reasoning MoE model — 560B parameters aimed at agents and long documents, launched as a free trial with weights still unreleased. They diverge most on price and open vs. closed weights — each quantified below from the models' real specs.

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/$0.9 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: Ling 3.1 Flash is the newer model by about 3 months (released September 30, 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 3.1 Flash
Provider	Poolside (US)	InclusionAI (Ant Group) (China)
Released	July 2, 2026	September 30, 2026
Context window	256K (~393 pages)	262K tokens (~393 pages)
Price (in/out)	$0.1/$0.2 per 1M tokens	$0.3/$0.9 per 1M tokens
Open weight?	Yes — self-hostable	No — API only
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

Open weights make this possible at all — Ling 3.1 Flash is API-only, so it cannot leave the vendor's servers.

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 3.1 Flash ($0.3/$0.9 per 1M tokens), and that gap compounds at volume.

Hybrid-reasoning MoE — 560B total parameters, ~25B active per token, aimed at agents, tool use, and long-document work

Ling 3.1 Flash

Artificial Analysis Intelligence Index score of 41 — above average for its price tier

Ling 3.1 Flash

Laguna XS 2.1 is comparatively weak here — weeks old with no independent replication; every published score traces back to Poolside's own harness

First-party pricing of $0.30/$0.90 per million tokens, with an 80% cache-read discount ($0.06/MTok)

Ling 3.1 Flash

Ling 3.1 Flash lists first-party pricing of $0.30/$0.90 per million tokens, with an 80% cache-read discount ($0.06/MTok) 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 3.1 Flash, and on millions of tokens that margin decides the monthly bill.

A team with data-privacy or self-hosting needs

→ Laguna XS 2.1

Open weights let you run it on your own hardware; Ling 3.1 Flash is API-only.

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 hybrid-reasoning moe — 560b total parameters, ~25b active per token, aimed at agents, tool use, and long-document work

→ Ling 3.1 Flash

That is its strongest area.

An enterprise with regional data-residency rules

→ Laguna XS 2.1 or Ling 3.1 Flash

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 3.1 Flash: where it fits

InclusionAI's (Ant Group) September 30, 2026 hybrid-reasoning MoE model — 560B parameters aimed at agents and long documents, launched as a free trial with weights still unreleased. Released September 30, 2026 by InclusionAI (Ant Group), it is built for hybrid-reasoning MoE — 560B total parameters, ~25B active per token, aimed at agents, tool use, and long-document work, artificial Analysis Intelligence Index score of 41 — above average for its price tier, first-party pricing of $0.30/$0.90 per million tokens, with an 80% cache-read discount ($0.06/MTok), and predecessor Ling 3.0 Flash shipped MIT-licensed open weights, suggesting a similar path once 3.1 clears review.

Its trade-offs: shipped as an announcement plus a two-week free trial rather than a full release — no weights, no model card, and limited independent verification at launch, context window currently served at 262K tokens even though InclusionAI's stated target is 1M — the larger window isn't live yet, not yet open-weight despite InclusionAI saying it plans to open-source the model 'soon', and announced via WeChat rather than a formal English-language technical report, so some specs are vendor-reported only. At $0.3 in / $0.9 out per million tokens, it sits in the budget price band.

The bottom line for this matchup

The defining split here is open vs. closed. Laguna XS 2.1 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Ling 3.1 Flash 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 Laguna XS 2.1 and Ling 3.1 Flash 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 pricing

Frequently asked questions

Is Laguna XS 2.1 or Ling 3.1 Flash better for coding?

Public SWE-Bench figures are not available for Ling 3.1 Flash, 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 3.1 Flash leans toward hybrid-reasoning moe — 560b total parameters, ~25b active per token, aimed at agents, tool use, and long-document work, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Laguna XS 2.1 or Ling 3.1 Flash?

Laguna XS 2.1 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Ling 3.1 Flash is API-metered at $0.3/$0.9 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?

Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.

Can I use both Laguna XS 2.1 and Ling 3.1 Flash together?

Yes — a multi-model platform like LumiChats gives you Laguna XS 2.1, Ling 3.1 Flash 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 3.1 Flash?

Ling 3.1 Flash — released September 30, 2026, about 3 months after Laguna XS 2.1.

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Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.