OpenAI

GPT 5.4 2026-03-05

Type
language model
Context
1,050K tokens
Max output
128K
API string
azure_ai/gpt-5.4-2026-03-05

Prices by provider

Provider Input, $ per 1M Output, $ per 1M In our data since
Microsoft Foundry azure_ai/gpt-5.4-2026-03-05 $2.5 $15 31 Jul 2026
OpenAI $2.5 $15 31 Jul 2026
Microsoft Foundry azure/eu/gpt-5.4-2026-03-05 $2.75 $16.5 31 Jul 2026

Only the base tier and only per-token prices are shown. Batch, discounted and cached rates, as well as prices per image or per second of video, do not go into this table: they cannot stand in the same column as a price per million tokens.

A single provider appears several times if it sells this model under different identifiers and at different prices — for example, at different weight precision. The identifier is shown next to the name. Identical offers that arrived from two sources under different spellings are merged into one row.

The date in the last column is the day this price first entered our collection. The price may well be older: before that day we simply were not recording it. It has not changed since — otherwise a new row with a new date would stand in its place.

Measurement results

Task set Result Run conditions Measured by
Mock AIME 2024–2025 — olympiad problems 97.78 % effort: medium · with a tuned harness Epoch evaluations
ARC-AGI — generalising to unseen patterns 93.67 % effort: xhigh · with a tuned harness
GPQA Diamond — graduate-level questions 91.07 % effort: medium · with a tuned harness Epoch evaluations
Terminal-Bench — working in the command line 81.8 % · with a tuned harness https://www.tbench.ai/leaderboard/terminal-bench/2.0
FrontierMath, levels 1–3 78.6 % effort: xhigh Epoch evaluations
WeirdML — unusual machine learning tasks 77.7 % effort: xhigh WeirdML Leaderboard
SWE-bench Verified — fixing bugs in repositories 76.86 % effort: high · with a tuned harness Epoch evaluations
ARC-AGI-2 73.95 % effort: xhigh
FrontierMath, level 4 — research-grade problems 49 % effort: xhigh Epoch evaluations
SimpleQA Verified — factual accuracy 44.84 % effort: xhigh Epoch evaluations
Chess puzzles 41.08 % effort: medium Epoch evaluations
APEX-Agents 36 % effort: xhigh
DeepResearch Bench — deep research 35.09 % effort: low https://drb.futuresearch.ai/#drb self-reported
Humanity’s Last Exam — expert-level questions 33.03 % effort: xhigh
GSO-Bench — code optimisation 31.37 % effort: xhigh https://gso-bench.github.io/index.html
CritPt — physics problems 23.43 % effort: xhigh