Both are OpenAI models. GPT-5.4 Nano is the newer, generally stronger default; reach for GPT-5.2 when a specific cost or latency profile matters more than the latest capabilities.
GPT-5.2 and GPT-5.4 Nano are both OpenAI models, so the real question is not which lab to trust but which tier fits your workload and budget. GPT-5.2 is a capable GPT-5-generation all-rounder, now succeeded by GPT-5.5. 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. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences
Price: GPT-5.4 Nano is about 8.8× cheaper on input ($0.2/$1.25 per 1M tokens vs $1.75/$14 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
Context window: both advertise 400K (~600 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Recency: GPT-5.4 Nano is the newer model by about 3 months (released March 17, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GPT-5.2
GPT-5.4 Nano
Provider
OpenAI (US)
OpenAI (US)
Released
December 11, 2025
March 17, 2026
Context window
400K (~600 pages)
400K (~600 pages)
Price (in/out)
$1.75/$14 per 1M tokens
$0.2/$1.25 per 1M tokens
Open weight?
No — API only
No — API only
Modalities
text, image, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Strong all-round reasoning: GPT-5.2 — GPT-5.4 Nano is comparatively weak here — a nano tier — not built for hard reasoning or frontier coding
Reliable structured output: GPT-5.2 — GPT-5.2 lists reliable structured output among its strengths; GPT-5.4 Nano does not.
Broad ecosystem and tooling: GPT-5.2 — GPT-5.2 lists broad ecosystem and tooling among its strengths; GPT-5.4 Nano does not.
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 GPT-5.2 ($1.75/$14 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-5.2 is comparatively weak here — smaller context than flagships
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.
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 GPT-5.2, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is strong all-round reasoning: GPT-5.2 — It is specifically built for that.
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 — That is its strongest area.
GPT-5.2: where it fits
A capable GPT-5-generation all-rounder, now succeeded by GPT-5.5. Released December 11, 2025 by OpenAI, it is built for strong all-round reasoning, reliable structured output, broad ecosystem and tooling, and professional workflows.
Its trade-offs are real: superseded by GPT-5.5, and smaller context than flagships. At $1.75 in / $14 out per million tokens, it sits in the mid price band.
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: 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.
The bottom line for this matchup
Because GPT-5.2 and GPT-5.4 Nano come from the same lab (OpenAI), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. GPT-5.4 Nano is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to GPT-5.4 Nano and drop down only with a concrete reason.
Frequently asked questions
Is GPT-5.2 or GPT-5.4 Nano 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-5.2 leans toward strong all-round reasoning while GPT-5.4 Nano leans toward cheapest gpt-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GPT-5.2 or GPT-5.4 Nano?
GPT-5.4 Nano is cheaper — $1.75/$14 per 1M tokens vs $0.2/$1.25 per 1M tokens, roughly 8.8× apart on input.
Which has the bigger context window?
Both advertise 400K (~600 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Should I upgrade from GPT-5.2 to GPT-5.4 Nano?
Since both are OpenAI models, the newer one (GPT-5.4 Nano) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, GPT-5.2 or GPT-5.4 Nano?
GPT-5.4 Nano — released March 17, 2026, about 3 months after GPT-5.2.
GPT-5.2 vs GPT-5.4 Nano
OpenAI · US | OpenAI · US · Updated June 2026
Quick verdict
Both are OpenAI models. GPT-5.4 Nano is the newer, generally stronger default; reach for GPT-5.2 when a specific cost or latency profile matters more than the latest capabilities.
GPT-5.2 and GPT-5.4 Nano are both OpenAI models, so the real question is not which lab to trust but which tier fits your workload and budget. GPT-5.2 is a capable GPT-5-generation all-rounder, now succeeded by GPT-5.5. 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. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences at a glance
▸Price: GPT-5.4 Nano is about 8.8× cheaper on input ($0.2/$1.25 per 1M tokens vs $1.75/$14 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: both advertise 400K (~600 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
▸Recency: GPT-5.4 Nano is the newer model by about 3 months (released March 17, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GPT-5.2
GPT-5.4 Nano
Provider
OpenAI (US)
OpenAI (US)
Released
December 11, 2025
March 17, 2026
Context window
400K (~600 pages)
400K (~600 pages)
Price (in/out)
$1.75/$14 per 1M tokens
$0.2/$1.25 per 1M tokens
Open weight?
No — API only
No — API only
Modalities
text, image, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Strong all-round reasoning
GPT-5.2
GPT-5.4 Nano is comparatively weak here — a nano tier — not built for hard reasoning or frontier coding
Reliable structured output
GPT-5.2
GPT-5.2 lists reliable structured output among its strengths; GPT-5.4 Nano does not.
Broad ecosystem and tooling
GPT-5.2
GPT-5.2 lists broad ecosystem and tooling among its strengths; GPT-5.4 Nano does not.
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 GPT-5.2 ($1.75/$14 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-5.2 is comparatively weak here — smaller context than flagships
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.
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 GPT-5.2, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is strong all-round reasoning
→ GPT-5.2
It is specifically built for that.
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
That is its strongest area.
GPT-5.2: where it fits
A capable GPT-5-generation all-rounder, now succeeded by GPT-5.5. Released December 11, 2025 by OpenAI, it is built for strong all-round reasoning, reliable structured output, broad ecosystem and tooling, and professional workflows.
Its trade-offs are real: superseded by GPT-5.5, and smaller context than flagships. At $1.75 in / $14 out per million tokens, it sits in the mid price band.
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: 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.
The bottom line for this matchup
Because GPT-5.2 and GPT-5.4 Nano come from the same lab (OpenAI), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. GPT-5.4 Nano is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to GPT-5.4 Nano and drop down only with a concrete reason.
Want both GPT-5.2 and GPT-5.4 Nano 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 either model, so the honest test is your own repository — run an identical real bug through both. By design, GPT-5.2 leans toward strong all-round reasoning while GPT-5.4 Nano leans toward cheapest gpt-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GPT-5.2 or GPT-5.4 Nano?
GPT-5.4 Nano is cheaper — $1.75/$14 per 1M tokens vs $0.2/$1.25 per 1M tokens, roughly 8.8× apart on input.
Which has the bigger context window?
Both advertise 400K (~600 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Should I upgrade from GPT-5.2 to GPT-5.4 Nano?
Since both are OpenAI models, the newer one (GPT-5.4 Nano) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, GPT-5.2 or GPT-5.4 Nano?
GPT-5.4 Nano — released March 17, 2026, about 3 months after GPT-5.2.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.