gpt-oss-120b vs Laguna XS 2.1
OpenAI · US | Poolside · US · Updated June 2026
Quick verdict
Pick gpt-oss-120b for self-hostable on a single 80gb h100 gpu via mxfp4 or configurable reasoning depth (low/medium/high). 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, gpt-oss-120b is the value pick.
gpt-oss-120b (OpenAI) and Laguna XS 2.1 (Poolside) are two of the models people most often weigh against each other in 2026. gpt-oss-120b is openAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use. 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, context window and coding benchmarks — each quantified below from the models' real specs.
Key differences at a glance
- ▸Context window: Laguna XS 2.1 holds 2× more — 256K (~393 pages) vs 131K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Coding: Laguna XS 2.1 leads SWE-Bench Verified by 8.5 points (62.4% vs 70.9%) — a real edge on hard, real-world software tasks.
- ▸Recency: Laguna XS 2.1 is the newer model by about 11 months (released July 2, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | gpt-oss-120b | Laguna XS 2.1 |
|---|---|---|
| Provider | OpenAI (US) | Poolside (US) |
| Released | August 5, 2025 | July 2, 2026 |
| Context window | 131K (~197 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 |
| Modalities | text, code | text, code |
| SWE-Bench Verified | 62.4% | 70.9% |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Self-hostable on a single 80GB H100 GPU via MXFP4
gpt-oss-120b
gpt-oss-120b lists self-hostable on a single 80GB H100 GPU via MXFP4 among its strengths; Laguna XS 2.1 does not.
Configurable reasoning depth (low/medium/high)
gpt-oss-120b
gpt-oss-120b lists configurable reasoning depth (low/medium/high) among its strengths; Laguna XS 2.1 does not.
Agentic tool use, function calling, and code execution
gpt-oss-120b
Laguna XS 2.1 is comparatively weak here — weak on harder agentic work (37.5 on Terminal-Bench 2.0), and its gain over XS.2 is barely above noise
Remarkable efficiency — 70.9% on SWE-Bench Verified from only 3B active parameters
Laguna XS 2.1
It scores 70.9% on SWE-Bench Verified against gpt-oss-120b's 62.4% — a 8.5-point edge on real repository work.
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 leads SWE-Bench Verified 70.9% to 62.4%.
Cheap even on the paid tier, at roughly a sixth of GLM 4.7's input price
Laguna XS 2.1
gpt-oss-120b is comparatively weak here — text-only, no image, audio, or video input
Lowest cost at scale
gpt-oss-120b
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
Laguna XS 2.1
Its 256K window is about 2× larger than gpt-oss-120b's 131K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ gpt-oss-120b
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
→ Laguna XS 2.1
Larger 256K window fits more in one prompt.
Anyone whose priority is self-hostable on a single 80gb h100 gpu via mxfp4
→ gpt-oss-120b
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.
gpt-oss-120b: where it fits
OpenAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use. Released August 5, 2025 by OpenAI, it is built for self-hostable on a single 80GB H100 GPU via MXFP4, configurable reasoning depth (low/medium/high), agentic tool use, function calling, and code execution, and full chain-of-thought visibility for debugging.
Its trade-offs are real: text-only, no image, audio, or video input, and 131K context and 5.1B active params trail the largest frontier closed models. 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
gpt-oss-120b and Laguna XS 2.1 overlap enough that the right pick depends on your specific job. gpt-oss-120b costs less per token; Laguna XS 2.1 holds the larger context; and each leads in its own area — gpt-oss-120b for self-hostable on a single 80gb h100 gpu via mxfp4, 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 gpt-oss-120b 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 pricingFrequently asked questions
Is gpt-oss-120b or Laguna XS 2.1 better for coding?
On SWE-Bench Verified, gpt-oss-120b scores 62.4% and Laguna XS 2.1 scores 70.9% — Laguna XS 2.1 has the measurable edge.
Which is cheaper, gpt-oss-120b or Laguna XS 2.1?
gpt-oss-120b is cheaper — Open weight (self-host / free) vs $0.1/$0.2 per 1M tokens.
Which has the bigger context window?
Laguna XS 2.1 — 256K vs 131K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both gpt-oss-120b and Laguna XS 2.1 together?
Yes — a multi-model platform like LumiChats gives you gpt-oss-120b, 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, gpt-oss-120b or Laguna XS 2.1?
Laguna XS 2.1 — released July 2, 2026, about 11 months after gpt-oss-120b.
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.