Pick GPT-6 Luna for openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume or available to free and go plan users in the chatgpt desktop app, not gated to paid tiers. 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. Choose Laguna XS 2.1 if you need self-hosting or data privacy; GPT-6 Luna if you want a managed API.
GPT-6 Luna (OpenAI) and Laguna XS 2.1 (Poolside) are two of the models people most often weigh against each other in 2026. GPT-6 Luna is openAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth. 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 context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Cost model: Laguna XS 2.1 ships open weights you can self-host (hardware cost only, no per-token fee), while GPT-6 Luna is API-metered at $0.1/$0.5 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: GPT-6 Luna holds 4× more — 1.05M tokens (~1,575 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: GPT-6 Luna is the newer model by about 3 months (released September 22, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GPT-6 Luna
Laguna XS 2.1
Provider
OpenAI (US)
Poolside (US)
Released
September 22, 2026
July 2, 2026
Context window
1.05M tokens (~1,575 pages)
256K (~393 pages)
Price (in/out)
$0.1/$0.5 per 1M tokens
$0.1/$0.2 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image
text, code
SWE-Bench Verified
Not published
70.9%
MRCR v2 @ 1M
Not published
Not published
Who wins what
OpenAI's cheapest GPT-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume: GPT-6 Luna — Its 1.05M tokens window holds about 4× more than Laguna XS 2.1's 256K in a single prompt.
Available to Free and Go plan users in the ChatGPT desktop app, not gated to paid tiers: GPT-6 Luna — OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth — and it carries the larger 1.05M tokens context.
Cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family: GPT-6 Luna — OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth — and it is the newer of the two.
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 its weights are open while GPT-6 Luna is API-only.
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 — GPT-6 Luna 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 — 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; GPT-6 Luna does not.
Largest single-prompt input: GPT-6 Luna — Its 1.05M tokens window is about 4× larger than Laguna XS 2.1's 256K, fitting roughly 1,575 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases: GPT-6 Luna — Larger 1.05M 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; GPT-6 Luna is API-only.
Anyone whose priority is openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume: GPT-6 Luna — 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-6 Luna: where it fits
OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth. Released September 22, 2026 by OpenAI, it is built for openAI's cheapest GPT-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume, available to Free and Go plan users in the ChatGPT desktop app, not gated to paid tiers, cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family, and 1.05M-token context window carried down from the flagship tier.
Its trade-offs are real: less reasoning capability than sibling GPT-6 Sol — built for speed and volume, not hard problems, and as the entry-level tier, expect it to be the first model swapped out when OpenAI ships the next cost-tier refresh. At $0.1 in / $0.5 out per million tokens, it sits in the budget price band.
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
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. GPT-6 Luna 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.
Frequently asked questions
Is GPT-6 Luna or Laguna XS 2.1 better for coding?
Public SWE-Bench figures are not available for GPT-6 Luna, so the honest test is your own repository — run an identical real bug through both. By design, GPT-6 Luna leans toward openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume 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, GPT-6 Luna or Laguna XS 2.1?
Laguna XS 2.1 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-6 Luna is API-metered at $0.1/$0.5 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?
GPT-6 Luna — 1.05M tokens vs 256K, about 4× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GPT-6 Luna and Laguna XS 2.1 together?
Yes — a multi-model platform like LumiChats gives you GPT-6 Luna, 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-6 Luna or Laguna XS 2.1?
GPT-6 Luna — released September 22, 2026, about 3 months after Laguna XS 2.1.
GPT-6 Luna vs Laguna XS 2.1
OpenAI · US | Poolside · US · Updated June 2026
Quick verdict
Pick GPT-6 Luna for openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume or available to free and go plan users in the chatgpt desktop app, not gated to paid tiers. 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. Choose Laguna XS 2.1 if you need self-hosting or data privacy; GPT-6 Luna if you want a managed API.
GPT-6 Luna (OpenAI) and Laguna XS 2.1 (Poolside) are two of the models people most often weigh against each other in 2026. GPT-6 Luna is openAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth. 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 context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Cost model: Laguna XS 2.1 ships open weights you can self-host (hardware cost only, no per-token fee), while GPT-6 Luna is API-metered at $0.1/$0.5 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: GPT-6 Luna holds 4× more — 1.05M tokens (~1,575 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: GPT-6 Luna is the newer model by about 3 months (released September 22, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GPT-6 Luna
Laguna XS 2.1
Provider
OpenAI (US)
Poolside (US)
Released
September 22, 2026
July 2, 2026
Context window
1.05M tokens (~1,575 pages)
256K (~393 pages)
Price (in/out)
$0.1/$0.5 per 1M tokens
$0.1/$0.2 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image
text, code
SWE-Bench Verified
Not published
70.9%
MRCR v2 @ 1M
Not published
Not published
Who wins what
OpenAI's cheapest GPT-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume
GPT-6 Luna
Its 1.05M tokens window holds about 4× more than Laguna XS 2.1's 256K in a single prompt.
Available to Free and Go plan users in the ChatGPT desktop app, not gated to paid tiers
GPT-6 Luna
OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth — and it carries the larger 1.05M tokens context.
Cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family
GPT-6 Luna
OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth — and it is the newer of the two.
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 its weights are open while GPT-6 Luna is API-only.
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 — GPT-6 Luna 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
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; GPT-6 Luna does not.
Largest single-prompt input
GPT-6 Luna
Its 1.05M tokens window is about 4× larger than Laguna XS 2.1's 256K, fitting roughly 1,575 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases
→ GPT-6 Luna
Larger 1.05M 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; GPT-6 Luna is API-only.
Anyone whose priority is openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume
→ GPT-6 Luna
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-6 Luna: where it fits
OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth. Released September 22, 2026 by OpenAI, it is built for openAI's cheapest GPT-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume, available to Free and Go plan users in the ChatGPT desktop app, not gated to paid tiers, cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family, and 1.05M-token context window carried down from the flagship tier.
Its trade-offs are real: less reasoning capability than sibling GPT-6 Sol — built for speed and volume, not hard problems, and as the entry-level tier, expect it to be the first model swapped out when OpenAI ships the next cost-tier refresh. At $0.1 in / $0.5 out per million tokens, it sits in the budget price band.
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
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. GPT-6 Luna 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 GPT-6 Luna 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.
Public SWE-Bench figures are not available for GPT-6 Luna, so the honest test is your own repository — run an identical real bug through both. By design, GPT-6 Luna leans toward openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume 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, GPT-6 Luna or Laguna XS 2.1?
Laguna XS 2.1 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-6 Luna is API-metered at $0.1/$0.5 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?
GPT-6 Luna — 1.05M tokens vs 256K, about 4× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GPT-6 Luna and Laguna XS 2.1 together?
Yes — a multi-model platform like LumiChats gives you GPT-6 Luna, 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-6 Luna or Laguna XS 2.1?
GPT-6 Luna — released September 22, 2026, about 3 months after Laguna XS 2.1.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.