OpenAI

GPT 4.1 Nano 2025-04-14

Type
language model
Context
1,048K tokens
Max output
33K
API string
azure/gpt-4.1-nano-2025-04-14

The provider has deprecated this model. It still responds, but it is not a good choice for new projects.

Prices by provider

Provider Input, $ per 1M Output, $ per 1M In our data since
Microsoft Foundry azure/gpt-4.1-nano-2025-04-14 $0.1 $0.4 31 Jul 2026
OpenAI $0.1 $0.4 31 Jul 2026
Microsoft Foundry azure/us/gpt-4.1-nano-2025-04-14 $0.11 $0.44 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
Arena Score, programming 1,306.21 1,287–1,325 — —
Arena Score in English 1,302.84 1,293–1,313 — —
Arena Score, hard prompts 1,285.8 1,272–1,299 — —
Arena Score, overall 1,284.87 1,277–1,293 — —
Arena Score, long queries 1,282.76 1,263–1,303 — —
Arena Score, multi-turn dialogue 1,276.52 1,258–1,295 — —
Arena Score, mathematics 1,274.09 1,251–1,297 — —
Arena Score, expert questions 1,271.26 1,241–1,301 — —
Arena Score, instruction following 1,267.09 1,255–1,279 — —
Arena Score in Russian 1,261.13 1,240–1,282 — —
Arena Score, creative writing 1,260.63 1,242–1,279 — —
Arena Score, working with images 1,063.53 1,045–1,082 — —
MATH, difficulty level five 70 % · with a tuned harness Epoch evaluations
GPQA Diamond — graduate-level questions 31.9 % · with a tuned harness Epoch evaluations
Mock AIME 2024–2025 — olympiad problems 28.82 % · with a tuned harness Epoch evaluations
Fiction.LiveBench — holding a long context 25 % — Fiction.live leaderboard
WeirdML — unusual machine learning tasks 18.98 % — WeirdML Leaderboard
Aider Polyglot — code edits in six languages 8.9 % · with a tuned harness Aider LLM Leaderboards
CritPt — physics problems 0 % — —
ARC-AGI — generalising to unseen patterns 0 % · with a tuned harness ARC Prize Leaderboard
ARC-AGI-2 0 % — —