GPT-5.4 Nano vs Particle-1

OpenAI · US  |  Particle AI · US · Updated June 2026

Quick verdict

Pick GPT-5.4 Nano for cheapest gpt-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work or classification, extraction, ranking and sub-agent execution at scale. 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-5.4 Nano is the value pick.

GPT-5.4 Nano (OpenAI) and Particle-1 (Particle AI) are two of the models people most often weigh against each other in 2026. GPT-5.4 Nano is openAI's cheapest GPT-5.4 variant at $0.20/$1.25 with a 400K window — a speed-and-cost tier for high-volume tasks, not deep reasoning. 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-5.4 NanoParticle-1
ProviderOpenAI (US) Particle AI (US)
ReleasedMarch 17, 2026 October 28, 2026
Context window400K (~600 pages) 1M tokens (~1,573 pages)
Price (in/out)$0.2/$1.25 per 1M tokens $0.25/$1 per 1M tokens
Open weight?No — API only No — API only
Modalitiestext, image, code text, image, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Cheapest GPT-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work

GPT-5.4 Nano

At $0.2/$1.25 per 1M tokens it undercuts Particle-1 ($0.25/$1 per 1M tokens), and that gap compounds at volume.

Classification, extraction, ranking and sub-agent execution at scale

GPT-5.4 Nano

OpenAI's cheapest GPT-5.4 variant at $0.20/$1.25 with a 400K window — a speed-and-cost tier for high-volume tasks, not deep reasoning — and it runs cheaper at $0.2/$1.25 per 1M tokens.

A 400K context in the smallest, fastest GPT-5.4 variant

GPT-5.4 Nano

GPT-5.4 Nano lists a 400K context in the smallest, fastest GPT-5.4 variant among its strengths; Particle-1 does not.

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

Particle-1

Its 1M tokens window holds about 2.6× more than GPT-5.4 Nano's 400K in a single prompt.

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 carries the larger 1M tokens context.

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

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.

Lowest cost at scale

GPT-5.4 Nano

At $0.2/$1.25 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.

Largest single-prompt input

Particle-1

Its 1M tokens window is about 2.6× larger than GPT-5.4 Nano's 400K, fitting roughly 1,573 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

→ GPT-5.4 Nano

At $0.2/$1.25 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

→ Particle-1

Larger 1M tokens window fits more in one prompt.

Anyone whose priority is cheapest gpt-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work

→ GPT-5.4 Nano

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-5.4 Nano: where it fits

OpenAI's cheapest GPT-5.4 variant at $0.20/$1.25 with a 400K window — a speed-and-cost tier for high-volume tasks, not deep reasoning. Released March 17, 2026 by OpenAI, it is built for cheapest GPT-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work, classification, extraction, ranking and sub-agent execution at scale, a 400K context in the smallest, fastest GPT-5.4 variant, and text and image input for cheap multimodal pipelines.

Its trade-offs are real: a nano tier — not built for hard reasoning or frontier coding, no published SWE-Bench Verified score (OpenAI reported SWE-Bench Pro instead), outclassed by GPT-5.4 and GPT-5.4 Mini whenever a task needs real depth, and image input only — no audio or video. At $0.2 in / $1.25 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-5.4 Nano and Particle-1 overlap enough that the right pick depends on your specific job. GPT-5.4 Nano costs less per token; Particle-1 holds the larger context; and each leads in its own area — GPT-5.4 Nano for cheapest gpt-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work, 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-5.4 Nano 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-5.4 Nano 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-5.4 Nano leans toward cheapest gpt-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work 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-5.4 Nano or Particle-1?

GPT-5.4 Nano is cheaper — $0.2/$1.25 per 1M tokens vs $0.25/$1 per 1M tokens, roughly 1.3× apart on input.

Which has the bigger context window?

Particle-1 — 1M tokens vs 400K, about 2.6× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both GPT-5.4 Nano and Particle-1 together?

Yes — a multi-model platform like LumiChats gives you GPT-5.4 Nano, 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-5.4 Nano or Particle-1?

Particle-1 — released October 28, 2026, about 8 months after GPT-5.4 Nano.

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.