IBM Granite 4.1 vs Laguna XS 2.1

IBM · US  |  Poolside · US · Updated June 2026

Quick verdict

Pick IBM Granite 4.1 for enterprise-grade open weights - apache 2.0, iso 42001-certified, cryptographically signed or efficient hybrid mamba-2/transformer design - much lower memory and faster inference. 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, IBM Granite 4.1 is the value pick.

IBM Granite 4.1 (IBM) and Laguna XS 2.1 (Poolside) are two of the models people most often weigh against each other in 2026. IBM Granite 4.1 is iBM's most complete open-weight enterprise release - small, Apache-2.0, long-context and governance-friendly, built to deploy efficiently rather than top the leaderboard. 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 and context window — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecIBM Granite 4.1Laguna XS 2.1
ProviderIBM (US) Poolside (US)
ReleasedApril 29, 2026 July 2, 2026
Context window512K (~768 pages) 256K (~393 pages)
Price (in/out)Open weight (self-host / free) $0.1/$0.2 per 1M tokens
Open weight?Yes — self-hostable Yes — self-hostable
Modalitiestext, code text, code
SWE-Bench VerifiedNot published 70.9%
MRCR v2 @ 1MNot published Not published

Who wins what

Enterprise-grade open weights - Apache 2.0, ISO 42001-certified, cryptographically signed

IBM Granite 4.1

IBM's most complete open-weight enterprise release - small, Apache-2.0, long-context and governance-friendly, built to deploy efficiently rather than top the leaderboard — and it carries the larger 512K context.

Efficient hybrid Mamba-2/transformer design - much lower memory and faster inference

IBM Granite 4.1

Its 512K window holds about 2× more than Laguna XS 2.1's 256K in a single prompt.

512K-token context on small, deployable dense models (3B/8B/30B)

IBM Granite 4.1

IBM Granite 4.1 lists 512K-token context on small, deployable dense models (3B/8B/30B) 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

IBM Granite 4.1 is comparatively weak here — not a frontier-intelligence competitor - built for efficient deployment, not top benchmark scores

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 is the newer of the two.

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; IBM Granite 4.1 does not.

Lowest cost at scale

IBM Granite 4.1

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

IBM Granite 4.1

Its 512K window is about 2× larger than Laguna XS 2.1's 256K, fitting roughly 768 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

IBM Granite 4.1

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

IBM Granite 4.1

Larger 512K window fits more in one prompt.

Anyone whose priority is enterprise-grade open weights - apache 2.0, iso 42001-certified, cryptographically signed

IBM Granite 4.1

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.

IBM Granite 4.1: where it fits

IBM's most complete open-weight enterprise release - small, Apache-2.0, long-context and governance-friendly, built to deploy efficiently rather than top the leaderboard. Released April 29, 2026 by IBM, it is built for enterprise-grade open weights - Apache 2.0, ISO 42001-certified, cryptographically signed, efficient hybrid Mamba-2/transformer design - much lower memory and faster inference, 512K-token context on small, deployable dense models (3B/8B/30B), and free to self-host; governance-friendly for on-prem and regulated deployments.

Its trade-offs are real: not a frontier-intelligence competitor - built for efficient deployment, not top benchmark scores, best as a workhorse; reasoning-heavy tasks favor larger models, instruct models are text-focused (vision and speech are separate family members), and efficiency and performance claims are IBM's own. As an open-weight model, its running cost is your own hardware rather than a per-token fee.

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

IBM Granite 4.1 and Laguna XS 2.1 overlap enough that the right pick depends on your specific job. IBM Granite 4.1 costs less per token; IBM Granite 4.1 holds the larger context; and each leads in its own area — IBM Granite 4.1 for enterprise-grade open weights - apache 2.0, iso 42001-certified, cryptographically signed, Laguna XS 2.1 for remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters. Rather than crowning one, run the same hard task through both once and let the results decide.

Want both IBM Granite 4.1 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.

See pricing

Frequently asked questions

Is IBM Granite 4.1 or Laguna XS 2.1 better for coding?

Public SWE-Bench figures are not available for IBM Granite 4.1, so the honest test is your own repository — run an identical real bug through both. By design, IBM Granite 4.1 leans toward enterprise-grade open weights - apache 2.0, iso 42001-certified, cryptographically signed while Laguna XS 2.1 leans toward remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, IBM Granite 4.1 or Laguna XS 2.1?

IBM Granite 4.1 is cheaper — Open weight (self-host / free) vs $0.1/$0.2 per 1M tokens.

Which has the bigger context window?

IBM Granite 4.1 — 512K vs 256K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both IBM Granite 4.1 and Laguna XS 2.1 together?

Yes — a multi-model platform like LumiChats gives you IBM Granite 4.1, 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, IBM Granite 4.1 or Laguna XS 2.1?

Laguna XS 2.1 — released July 2, 2026, about 2 months after IBM Granite 4.1.

Related comparisons

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