Laguna XS 2.1 vs Llama 4 Scout

Poolside · US | Meta · US · 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 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.

Laguna XS 2.1 (Poolside) and Llama 4 Scout (Meta) are two of the models people most often weigh against each other in 2026. 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. 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 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: Laguna XS 2.1 is the newer model by about 15 months (released July 2, 2026), usually meaning fresher training data and capabilities.

Side-by-side specs

Spec	Laguna XS 2.1	Llama 4 Scout
Provider	Poolside (US)	Meta (US)
Released	July 2, 2026	April 2025
Context window	256K (~393 pages)	10M (~15,000 pages)
Price (in/out)	$0.1/$0.2 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	70.9%	Not published
MRCR v2 @ 1M	Not published	15%

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 is the newer of the two.

Open weights under OpenMDW-1.1, shipped day one in BF16, FP8, NVFP4 and INT4 across every major runtime

Laguna XS 2.1

Laguna XS 2.1 lists open weights under OpenMDW-1.1, shipped day one in BF16, FP8, NVFP4 and INT4 across every major runtime among its strengths; Llama 4 Scout does not.

Cheap even on the paid tier, at roughly a sixth of GLM 4.7's input price

Laguna XS 2.1

Laguna XS 2.1 lists cheap even on the paid tier, at roughly a sixth of GLM 4.7's input price among its strengths; Llama 4 Scout does not.

Largest advertised context (10M)

Llama 4 Scout

Its 10M window holds about 38× more than Laguna XS 2.1'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; Laguna XS 2.1 does not.

Lowest cost at scale

Llama 4 Scout

Its weights are open, so at volume you pay for your own hardware instead of Laguna XS 2.1's $0.1/$0.2 per 1M tokens.

Largest single-prompt input

Llama 4 Scout

Its 10M window is about 38× larger than Laguna XS 2.1's 256K, 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 Laguna XS 2.1, 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 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 largest advertised context (10m)

→ Llama 4 Scout

That is its strongest area.

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.

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

Laguna XS 2.1 and Llama 4 Scout overlap enough that the right pick depends on your specific job. Llama 4 Scout costs less per token; Llama 4 Scout holds the larger context; and each leads in its own area — Laguna XS 2.1 for remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters, Llama 4 Scout for largest advertised context (10m). Rather than crowning one, run the same hard task through both once and let the results decide.

Want both Laguna XS 2.1 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.

See pricing

Frequently asked questions

Is Laguna XS 2.1 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, Laguna XS 2.1 leans toward remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters while Llama 4 Scout leans toward largest advertised context (10m), and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Laguna XS 2.1 or Llama 4 Scout?

Llama 4 Scout is cheaper — $0.1/$0.2 per 1M tokens vs Open weight (self-host / free).

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 Laguna XS 2.1 and Llama 4 Scout together?

Yes — a multi-model platform like LumiChats gives you Laguna XS 2.1, 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, Laguna XS 2.1 or Llama 4 Scout?

Laguna XS 2.1 — released July 2, 2026, about 15 months after Llama 4 Scout.

Related comparisons

Claude Sonnet 5.5 vs Laguna XS 2.1 Claude Sonnet 5.5 vs Llama 4 Scout GPT-6.1 Sol vs Laguna XS 2.1 GPT-6.1 Sol vs Llama 4 Scout Claude Haiku 5.5 vs Laguna XS 2.1 Claude Haiku 5.5 vs Llama 4 Scout Mistral Large 4 vs Laguna XS 2.1 Mistral Large 4 vs 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.