OpenAIがGPT-5.4 miniとnanoをリリース、性能向上も最大4倍の価格上昇

OpenAI has released two new compact models—GPT-5.4 mini and nano—built for coding assistants, subagents, and computer control. GPT-5.4 mini nearly matches the full model's performance, but both new models come with a steep price hike over their predecessors.

OpenAI says GPT-5.4 mini delivers major improvements over GPT-5 mini in coding, reasoning, multimodal understanding, and tool usage, while running more than twice as fast. The model gets close to the much larger GPT-5.4 across several benchmarks, hitting 54.4 percent vs. 57.7 percent on the coding benchmark SWE-Bench Pro and 72.1 percent vs. 75.0 percent on OSWorld-Verified, which measures computer usage capabilities.

Benchmark

GPT-5.4

GPT-5.4 mini

GPT-5.4 nano

GPT-5 mini

SWE-Bench Pro

57.7%

54.4%

52.4%

45.7%

Terminal-Bench 2.0

75.1%

60.0%

46.3%

38.2%

Toolathlon

54.6%

42.9%

35.5%

26.9%

GPQA Diamond

93.0%

88.0%

82.8%

81.6%

OSWorld-Verified

75.0%

72.1%

39.0%

42.0%

GPT-5.4 nano is the smallest and cheapest option. OpenAI recommends it for classification, data extraction, ranking, and coding subagents that handle simpler supporting tasks. This model is also a big step up from GPT-5 nano.

Big brain plans, small brain grinds

The subagent setup OpenAI shows off in Codex is worth a closer look: a larger model like GPT-5.4 handles planning, coordination, and final evaluation, while farming out parallel subtasks to GPT-5.4 mini or nano subagents.

Those subtasks include things like searching a codebase, scanning large files, or processing supporting docs. OpenAI says GPT-5.4 mini burns only 30 percent of the GPT-5.4 quota in Codex, which should cut costs for simpler tasks to roughly a third.

The mini model also shows a huge jump in computer control. GPT-5.4 mini scored 72.1 percent on the OSWorld Verified benchmark, just a hair behind the full GPT-5.4 at 75.0 percent. GPT-5 mini only managed 42.0 percent.

Coding

Benchmark

GPT-5.4

GPT-5.4 mini

GPT-5.4 nano

GPT-5 mini

SWE-bench Pro (Public)

57.7%

54.4%

52.4%

45.7%

Terminal-Bench 2.0

75.1%

60.0%

46.3%

38.2%

Tool-calling

Benchmark

GPT-5.4

GPT-5.4 mini

GPT-5.4 nano

GPT-5 mini

MCP Atlas

67.2%

57.7%

56.1%

47.6%

Toolathlon

54.6%

42.9%

35.5%

26.9%

τ2-bench (telecom)

98.9%

93.4%

92.5%

74.1%

Intelligence

Benchmark

GPT-5.4

GPT-5.4 mini

GPT-5.4 nano

GPT-5 mini

GPQA Diamond

93.0%

88.0%

82.8%

81.6%

HLE w/ tool

52.1%

41.5%

37.7%

31.6%

HLE w/o tools

39.8%

28.2%

24.3%

18.3%

MM / Vision / CUA

Benchmark

GPT-5.4

GPT-5.4 mini

GPT-5.4 nano

GPT-5 mini

OSWorld-Verified

75.0%

72.1%

39.0%

42.0%

MMMUPro w/ Python

81.5%

78.0%

69.5%

74.1%

MMMUPro

81.2%

76.6%

66.1%

67.5%

OmniDocBench 1.5 (no tools, lower is better)

0.109

0.1263

0.2419

0.1791

Long context

Benchmark

GPT-5.4

GPT-5.4 mini

GPT-5.4 nano

GPT-5 mini

OpenAI MRCR v2 8-needle 64K-128K

86.0%

47.7%

44.2%

35.1%

OpenAI MRCR v2 8-needle 128K-256K

79.3%

33.6%

33.1%

19.4%

Graphwalks BFS 0K-128K

93.1%

76.3%

73.4%

73.4%

Graphwalks parents 0-128K (accuracy)

89.8%

71.5%

50.8%

64.3%

Better performance comes at up to 4x the price

GPT-5.4 mini is now available through the API, Codex, and ChatGPT at $0.75 per million input tokens and $4.50 per million output tokens. Nano is API-only at $0.20 per million input tokens and $1.25 per million output tokens. Both models support a 400,000-token context window.

Compared to the previous mini and nano models in the GPT-5 lineup, that's a serious price bump. According to OpenAI's pricing page, GPT-5 mini ran $0.25 per million input tokens and $2.00 per million output tokens. GPT-5 nano was $0.05 input and $0.40 output per million tokens.

Model

Input (per 1M tokens)

Output (per 1M tokens)

Input markup

Output markup

GPT-5.4 mini

$0.75

$4.50

3.0x

2.25x

GPT-5.4 nano

$0.20

$1.25

4.0x

3.125x

GPT-5 mini

$0.25

$2.00

-

-

GPT-5 nano

$0.05

$0.40

-

-

OpenAI likely justifies the higher prices by pointing to the performance gains, which bring these compact models much closer to the full-size versions that cost significantly more to run.

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