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Head-to-head comparison

GPT-5.2 Chat vs o4 Mini

GPT-5.2 Chat (OpenAI) and o4 Mini (OpenAI) compared on the ModelCap Index, API price, context window, provider availability, weight access and every public benchmark board they share. Figures are the same ones shown on the live rankings; nothing here is a hidden score.

Snapshot as of 17 August 2026

As of 17 August 2026, GPT-5.2 Chat holds the stronger ModelCap Index position (#9 vs #57); o4 Mini is 3.2× cheaper per output token ($4.40/1M vs $14.00/1M); and o4 Mini offers the longer context window (200K vs 128K tokens).

Which should you choose?

Choose GPT-5.2 Chat if…

  • you want the stronger overall ModelCap Index position — #9 against #57 (74.9 vs 43.5 points).
  • Arena coding is your yardstick — 1515 against 1433.
  • you want the more recently listed model — GPT-5.2 Chat was listed 10 December 2025, o4 Mini 16 April 2025.

Choose o4 Mini if…

  • API cost matters — $4.40/1M output tokens against $14.00/1M, about 3.2× cheaper.
  • your workload is prompt-heavy — input tokens cost $1.10/1M against $1.75/1M.
  • you need the longer context window — 200K tokens against 128K.

GPT-5.2 Chat vs o4 Mini: specs, pricing and context

Specification comparison of GPT-5.2 Chat and o4 Mini
FieldGPT-5.2 ChatOpenAIo4 MiniOpenAI
ModelCap Index position#9#57
Index score74.943.5
EvidenceMeasuredMeasured
Input price / 1M tokens$1.75$1.10
Output price / 1M tokens$14.00$4.40
Context window128K tokens200K tokens
Max output tokens32K100K
API providers listed11
Weight accessAPI onlyAPI only
Input modalitiesFile, Image, TextImage, Text, File
First listed10 December 202516 April 2025
PublisherOpenAIOpenAI

Evidence: 2 public benchmark observations across 2 boards · 4 public benchmark observations across 4 boards. Prices are the lowest listed API offer per million tokens observed on the OpenRouter catalogue.

Benchmark scores: GPT-5.2 Chat vs o4 Mini

Public benchmark boards where GPT-5.2 Chat or o4 Mini has a published result
BoardGPT-5.2 Chato4 MiniLeads
Arena codingArena (LMArena)15151508–152214331426–1440GPT-5.2 Chat
ARC-AGI-2ARC Prize Foundation6.1%HighOnly one result
Artificial Analysis Intelligence IndexArtificial Analysis26.1intelligence-indexOnly one result

Scores are the sources' own published figures for each model's best evaluated configuration; ModelCap never re-runs a benchmark.

Want a different pairing? Open the interactive comparison tool to swap either model for any current ranked language model.

GPT-5.2 Chat vs o4 Mini: common questions

Is GPT-5.2 Chat better than o4 Mini?

GPT-5.2 Chat ranks higher on the ModelCap Index as of 17 August 2026: #9 against #57. That is a capability ranking built from public benchmark evidence with published uncertainty; whether it is "better" for you also depends on price, context and where you can run it.

Is GPT-5.2 Chat cheaper than o4 Mini?

o4 Mini is cheaper on output tokens: $4.40/1M against $14.00/1M. Input tokens are $1.75/1M for GPT-5.2 Chat and $1.10/1M for o4 Mini. Prices are the lowest listed API offer ModelCap observed, in USD per million tokens.

Which has the bigger context window, GPT-5.2 Chat or o4 Mini?

o4 Mini has the larger context window: 200K tokens against 128K.

Which is better for coding, GPT-5.2 Chat or o4 Mini?

Arena coding: GPT-5.2 Chat 1515, o4 Mini 1433 — GPT-5.2 Chat leads.

Are GPT-5.2 Chat and o4 Mini open-weight models?

GPT-5.2 Chat: API only. o4 Mini: API only. Open weights mean the checkpoint can be downloaded and self-hosted under its licence; API-only models are available solely through hosted endpoints.

Where can I run GPT-5.2 Chat and o4 Mini?

ModelCap currently lists 1 API provider for GPT-5.2 Chat and 1 for o4 Mini, from the OpenRouter catalogue snapshot the site serves; each model page lists the providers and their prices.

How current is this GPT-5.2 Chat vs o4 Mini comparison?

Every figure comes from the sealed ModelCap dataset published 17 August 2026; the page re-renders within a minute of each data refresh, and the ModelCap Index positions are the same ones shown on the live rankings.

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