GPT-6 Luna vs Particle-1

OpenAI · US  |  Particle AI · 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 Particle-1 for token-efficient coding model, post-trained with reinforcement learning on deepseek v4.1 flash or particle reports 72.57% task pass rate versus 71.15% for deepseek v4.1 flash, at $0.2211 versus $0.3368 per task. On a tight budget at scale, GPT-6 Luna is the value pick.

GPT-6 Luna (OpenAI) and Particle-1 (Particle AI) 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. Particle-1 is particle AI's October 28, 2026 debut - a closed, token-efficient coding model post-trained on DeepSeek V4.1 Flash, with 1M context at $0.25/$1.00 at peak rates. 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

SpecGPT-6 LunaParticle-1
ProviderOpenAI (US) Particle AI (US)
ReleasedSeptember 22, 2026 October 28, 2026
Context window1.05M tokens (~1,575 pages) 1M tokens (~1,573 pages)
Price (in/out)$0.1/$0.5 per 1M tokens $0.25/$1 per 1M tokens
Open weight?No — API only No — API only
Modalitiestext, image text, image, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot 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

At $0.1/$0.5 per 1M tokens it undercuts Particle-1 ($0.25/$1 per 1M tokens), and that gap compounds at volume.

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 runs cheaper at $0.1/$0.5 per 1M tokens.

Cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family

GPT-6 Luna

GPT-6 Luna lists cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family among its strengths; Particle-1 does not.

Token-efficient coding model, post-trained with reinforcement learning on DeepSeek V4.1 Flash

Particle-1

GPT-6 Luna is comparatively weak here — as the entry-level tier, expect it to be the first model swapped out when OpenAI ships the next cost-tier refresh

Particle reports 72.57% task pass rate versus 71.15% for DeepSeek V4.1 Flash, at $0.2211 versus $0.3368 per task

Particle-1

Particle AI's October 28, 2026 debut - a closed, token-efficient coding model post-trained on DeepSeek V4.1 Flash, with 1M context at $0.25/$1.00 at peak rates — and it is the newer of the two.

Particle reports 28.4% to 52.2% fewer output tokens than the base model across four benchmarks

Particle-1

Particle-1 lists particle reports 28.4% to 52.2% fewer output tokens than the base model across four benchmarks among its strengths; GPT-6 Luna does not.

Lowest cost at scale

GPT-6 Luna

At $0.1/$0.5 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.

Which should you pick?

A cost-sensitive startup shipping high volume

→ GPT-6 Luna

At $0.1/$0.5 per 1M tokens it undercuts Particle-1, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ GPT-6 Luna

Larger 1.05M tokens window fits more in one prompt.

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 token-efficient coding model, post-trained with reinforcement learning on deepseek v4.1 flash

→ Particle-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.

Particle-1: where it fits

Particle AI's October 28, 2026 debut - a closed, token-efficient coding model post-trained on DeepSeek V4.1 Flash, with 1M context at $0.25/$1.00 at peak rates. Released October 28, 2026 by Particle AI, it is built for token-efficient coding model, post-trained with reinforcement learning on DeepSeek V4.1 Flash, particle reports 72.57% task pass rate versus 71.15% for DeepSeek V4.1 Flash, at $0.2211 versus $0.3368 per task, particle reports 28.4% to 52.2% fewer output tokens than the base model across four benchmarks, and peak API price of $0.25/$1.00 per million tokens, halved off-peak to $0.125/$0.50 (20:30-04:00 New York time).

Its trade-offs: aPI-only with no published weights, although its DeepSeek base model is open, all benchmarks are vendor-reported on Particle's own harness; none were independently reproduced as of October 31, and pricing is inconsistent across sources: Particle's launch post said $0.30/$1.20 and OpenRouter shows $0.05/$0.50, while its billing docs say $0.25/$1.00. At $0.25 in / $1 out per million tokens, it sits in the budget price band.

The bottom line for this matchup

GPT-6 Luna and Particle-1 overlap enough that the right pick depends on your specific job. GPT-6 Luna costs less per token; GPT-6 Luna holds the larger context; and each leads in its own area — GPT-6 Luna for openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume, Particle-1 for token-efficient coding model, post-trained with reinforcement learning on deepseek v4.1 flash. Rather than crowning one, run the same hard task through both once and let the results decide.

Want both GPT-6 Luna and Particle-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 GPT-6 Luna or Particle-1 better for coding?

Public SWE-Bench figures are not available for either model, 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 Particle-1 leans toward token-efficient coding model, post-trained with reinforcement learning on deepseek v4.1 flash, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, GPT-6 Luna or Particle-1?

GPT-6 Luna is cheaper — $0.1/$0.5 per 1M tokens vs $0.25/$1 per 1M tokens, roughly 2.5× apart on input.

Which has the bigger context window?

Effectively neither — 1.05M tokens vs 1M tokens is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.

Can I use both GPT-6 Luna and Particle-1 together?

Yes — a multi-model platform like LumiChats gives you GPT-6 Luna, Particle-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 Particle-1?

Particle-1 — released October 28, 2026, about 36 days after GPT-6 Luna.

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.