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 Microsoft Phi-4 for strong reasoning for a small 14b open-weight model or mit-licensed — fully self-hostable at no per-token cost. On a tight budget at scale, Microsoft Phi-4 is the value pick.
Laguna XS 2.1 (Poolside) and Microsoft Phi-4 (Microsoft) 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. Microsoft Phi-4 is microsoft's MIT-licensed 14B open model — strong reasoning for its size and very cheap, but a tiny 16K context and text-only. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: Microsoft Phi-4 is about 1.4× cheaper on input ($0.07/$0.14 per 1M tokens vs $0.1/$0.2 per 1M tokens) — modest, but it adds up at steady volume.
Context window: Laguna XS 2.1 holds 16× more — 256K (~393 pages) vs 16K (~25 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 18 months (released July 2, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Laguna XS 2.1
Microsoft Phi-4
Provider
Poolside (US)
Microsoft (US)
Released
July 2, 2026
January 10, 2025
Context window
256K (~393 pages)
16K (~25 pages)
Price (in/out)
$0.1/$0.2 per 1M tokens
$0.07/$0.14 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 carries the larger 256K context.
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 — Microsoft Phi-4 is comparatively weak here — text only — no image, audio or video input
Strong reasoning for a small 14B open-weight model: Microsoft Phi-4 — Microsoft's MIT-licensed 14B open model — strong reasoning for its size and very cheap, but a tiny 16K context and text-only — and it runs cheaper at $0.07/$0.14 per 1M tokens.
MIT-licensed — fully self-hostable at no per-token cost: Microsoft Phi-4 — At $0.07/$0.14 per 1M tokens it undercuts Laguna XS 2.1 ($0.1/$0.2 per 1M tokens), and that gap compounds at volume.
Runs on modest or local hardware: Microsoft Phi-4 — Microsoft Phi-4 lists runs on modest or local hardware among its strengths; Laguna XS 2.1 does not.
Lowest cost at scale: Microsoft Phi-4 — At $0.07/$0.14 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Laguna XS 2.1 — Its 256K window is about 16× larger than Microsoft Phi-4's 16K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Microsoft Phi-4 — At $0.07/$0.14 per 1M tokens it undercuts Laguna XS 2.1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Laguna XS 2.1 — Larger 256K 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 strong reasoning for a small 14b open-weight model: Microsoft Phi-4 — 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.
Microsoft Phi-4: where it fits
Microsoft's MIT-licensed 14B open model — strong reasoning for its size and very cheap, but a tiny 16K context and text-only. Released January 10, 2025 by Microsoft, it is built for strong reasoning for a small 14B open-weight model, mIT-licensed — fully self-hostable at no per-token cost, runs on modest or local hardware, and very cheap hosted inference at about $0.07/$0.14.
Its trade-offs: a tiny 16K context — by far the smallest window in this comparison, text only — no image, audio or video input, an early-2025 small model, outclassed on hard tasks by 2026 flagships, and no first-party per-token API; hosted prices are third-party. At $0.07 in / $0.14 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Laguna XS 2.1 and Microsoft Phi-4 overlap enough that the right pick depends on your specific job. Microsoft Phi-4 costs less per token; Laguna XS 2.1 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, Microsoft Phi-4 for strong reasoning for a small 14b open-weight model. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is Laguna XS 2.1 or Microsoft Phi-4 better for coding?
Public SWE-Bench figures are not available for Microsoft Phi-4, 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 Microsoft Phi-4 leans toward strong reasoning for a small 14b open-weight model, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Laguna XS 2.1 or Microsoft Phi-4?
Microsoft Phi-4 is cheaper — $0.1/$0.2 per 1M tokens vs $0.07/$0.14 per 1M tokens, roughly 1.4× apart on input.
Which has the bigger context window?
Laguna XS 2.1 — 256K vs 16K, about 16× 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 Microsoft Phi-4 together?
Yes — a multi-model platform like LumiChats gives you Laguna XS 2.1, Microsoft Phi-4 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 Microsoft Phi-4?
Laguna XS 2.1 — released July 2, 2026, about 18 months after Microsoft Phi-4.
Laguna XS 2.1 vs Microsoft Phi-4
Poolside · US | Microsoft · 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 Microsoft Phi-4 for strong reasoning for a small 14b open-weight model or mit-licensed — fully self-hostable at no per-token cost. On a tight budget at scale, Microsoft Phi-4 is the value pick.
Laguna XS 2.1 (Poolside) and Microsoft Phi-4 (Microsoft) 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. Microsoft Phi-4 is microsoft's MIT-licensed 14B open model — strong reasoning for its size and very cheap, but a tiny 16K context and text-only. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: Microsoft Phi-4 is about 1.4× cheaper on input ($0.07/$0.14 per 1M tokens vs $0.1/$0.2 per 1M tokens) — modest, but it adds up at steady volume.
▸Context window: Laguna XS 2.1 holds 16× more — 256K (~393 pages) vs 16K (~25 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 18 months (released July 2, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Laguna XS 2.1
Microsoft Phi-4
Provider
Poolside (US)
Microsoft (US)
Released
July 2, 2026
January 10, 2025
Context window
256K (~393 pages)
16K (~25 pages)
Price (in/out)
$0.1/$0.2 per 1M tokens
$0.07/$0.14 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 carries the larger 256K context.
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
Microsoft Phi-4 is comparatively weak here — text only — no image, audio or video input
Strong reasoning for a small 14B open-weight model
Microsoft Phi-4
Microsoft's MIT-licensed 14B open model — strong reasoning for its size and very cheap, but a tiny 16K context and text-only — and it runs cheaper at $0.07/$0.14 per 1M tokens.
MIT-licensed — fully self-hostable at no per-token cost
Microsoft Phi-4
At $0.07/$0.14 per 1M tokens it undercuts Laguna XS 2.1 ($0.1/$0.2 per 1M tokens), and that gap compounds at volume.
Runs on modest or local hardware
Microsoft Phi-4
Microsoft Phi-4 lists runs on modest or local hardware among its strengths; Laguna XS 2.1 does not.
Lowest cost at scale
Microsoft Phi-4
At $0.07/$0.14 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Laguna XS 2.1
Its 256K window is about 16× larger than Microsoft Phi-4's 16K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Microsoft Phi-4
At $0.07/$0.14 per 1M tokens it undercuts Laguna XS 2.1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Laguna XS 2.1
Larger 256K 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 strong reasoning for a small 14b open-weight model
→ Microsoft Phi-4
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.
Microsoft Phi-4: where it fits
Microsoft's MIT-licensed 14B open model — strong reasoning for its size and very cheap, but a tiny 16K context and text-only. Released January 10, 2025 by Microsoft, it is built for strong reasoning for a small 14B open-weight model, mIT-licensed — fully self-hostable at no per-token cost, runs on modest or local hardware, and very cheap hosted inference at about $0.07/$0.14.
Its trade-offs: a tiny 16K context — by far the smallest window in this comparison, text only — no image, audio or video input, an early-2025 small model, outclassed on hard tasks by 2026 flagships, and no first-party per-token API; hosted prices are third-party. At $0.07 in / $0.14 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Laguna XS 2.1 and Microsoft Phi-4 overlap enough that the right pick depends on your specific job. Microsoft Phi-4 costs less per token; Laguna XS 2.1 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, Microsoft Phi-4 for strong reasoning for a small 14b open-weight model. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Laguna XS 2.1 and Microsoft Phi-4 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 Microsoft Phi-4 better for coding?
Public SWE-Bench figures are not available for Microsoft Phi-4, 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 Microsoft Phi-4 leans toward strong reasoning for a small 14b open-weight model, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Laguna XS 2.1 or Microsoft Phi-4?
Microsoft Phi-4 is cheaper — $0.1/$0.2 per 1M tokens vs $0.07/$0.14 per 1M tokens, roughly 1.4× apart on input.
Which has the bigger context window?
Laguna XS 2.1 — 256K vs 16K, about 16× 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 Microsoft Phi-4 together?
Yes — a multi-model platform like LumiChats gives you Laguna XS 2.1, Microsoft Phi-4 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 Microsoft Phi-4?
Laguna XS 2.1 — released July 2, 2026, about 18 months after Microsoft Phi-4.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.