Laguna XS 2.1 vs Step 5 Preview

Poolside · US | StepFun · 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 Step 5 Preview for sparse moe architecture — 600b total parameters, only 27b active per token — built for long-horizon agentic work across large codebases and documents or 1m-token context window (64k max output) at $1/$2.70 per million tokens, among the cheapest frontier-scale context windows available. Choose Laguna XS 2.1 if you need self-hosting or data privacy; Step 5 Preview if you want a managed API.

Laguna XS 2.1 (Poolside, US) and Step 5 Preview (StepFun, 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. Step 5 Preview is stepFun's September 20, 2026 flagship preview — a 600B-parameter sparse MoE model (27B active) with a 1M-token context window at $1/$2.70 per million tokens, open weights promised for mid-October. They diverge most on price, context window 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 10× cheaper on input ($0.1/$0.2 per 1M tokens vs $1/$2.7 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: Step 5 Preview holds 3.8× more — 1M tokens (~1,500 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: Step 5 Preview is the newer model by about 3 months (released September 20, 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	Step 5 Preview
Provider	Poolside (US)	StepFun (China)
Released	July 2, 2026	September 20, 2026
Context window	256K (~393 pages)	1M tokens (~1,500 pages)
Price (in/out)	$0.1/$0.2 per 1M tokens	$1/$2.7 per 1M tokens
Open weight?	Yes — self-hostable	No — API only
Modalities	text, code	text, image, 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

Step 5 Preview is comparatively weak here — stepCodeBench and DeepSWE figures are StepFun's own reported numbers, not yet independently reproduced at scale

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 — Step 5 Preview 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 Step 5 Preview ($1/$2.7 per 1M tokens), and that gap compounds at volume.

Sparse MoE architecture — 600B total parameters, only 27B active per token — built for long-horizon agentic work across large codebases and documents

Step 5 Preview

Its 1M tokens window holds about 3.8× more than Laguna XS 2.1's 256K in a single prompt.

1M-token context window (64K max output) at $1/$2.70 per million tokens, among the cheapest frontier-scale context windows available

Step 5 Preview

Laguna XS 2.1 is comparatively weak here — the free endpoint trains on your inputs and outputs — disqualifying for proprietary code, which is its main use case

67.7% on DeepSWE v1.1, ahead of open-weight rivals Kimi K3 (67.5%) and GLM-5.3 (66.9%)

Step 5 Preview

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

Step 5 Preview

Its 1M tokens window is about 3.8× larger than Laguna XS 2.1's 256K, fitting roughly 1,500 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 Step 5 Preview, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ Step 5 Preview

Larger 1M tokens window fits more in one prompt.

A team with data-privacy or self-hosting needs

→ Laguna XS 2.1

Open weights let you run it on your own hardware; Step 5 Preview 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 sparse moe architecture — 600b total parameters, only 27b active per token — built for long-horizon agentic work across large codebases and documents

→ Step 5 Preview

That is its strongest area.

An enterprise with regional data-residency rules

→ Laguna XS 2.1 or Step 5 Preview

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.

Step 5 Preview: where it fits

StepFun's September 20, 2026 flagship preview — a 600B-parameter sparse MoE model (27B active) with a 1M-token context window at $1/$2.70 per million tokens, open weights promised for mid-October. Released September 20, 2026 by StepFun, it is built for sparse MoE architecture — 600B total parameters, only 27B active per token — built for long-horizon agentic work across large codebases and documents, 1M-token context window (64K max output) at $1/$2.70 per million tokens, among the cheapest frontier-scale context windows available, 67.7% on DeepSWE v1.1, ahead of open-weight rivals Kimi K3 (67.5%) and GLM-5.3 (66.9%), and scores 44 on the Artificial Analysis Intelligence Index, ranking 27th of 653 models tracked.

Its trade-offs: trails closed frontier models on coding — 67.7% on DeepSWE v1.1 vs GPT-6 Astra's 74.1% and Claude Opus 5's 74.0%, open weights were promised for October 15, 2026 but hadn't shipped as of this writing — API-only for now despite the open-weight roadmap, a 'Preview' release, not StepFun's finished flagship — positioning and pricing may change once weights ship, and stepCodeBench and DeepSWE figures are StepFun's own reported numbers, not yet independently reproduced at scale. At $1 in / $2.7 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. Step 5 Preview 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 Step 5 Preview 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 Step 5 Preview better for coding?

Public SWE-Bench figures are not available for Step 5 Preview, 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 Step 5 Preview leans toward sparse moe architecture — 600b total parameters, only 27b active per token — built for long-horizon agentic work across large codebases and documents, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Laguna XS 2.1 or Step 5 Preview?

Laguna XS 2.1 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Step 5 Preview is API-metered at $1/$2.7 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?

Step 5 Preview — 1M tokens vs 256K, about 3.8× 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 Step 5 Preview together?

Yes — a multi-model platform like LumiChats gives you Laguna XS 2.1, Step 5 Preview 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 Step 5 Preview?

Step 5 Preview — released September 20, 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.