Model profile
OpenAI
New in 2026

OpenAI: GPT-5.4 Nano

OpenAI: GPT-5.4 Nano is a budget-priced multimodal generalist from OpenAI with balanced runtime profile, large context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalBalanced latencyLarge contextBudget pricing
Intelligence
44.4

Benchmark blend

Coding
43.9

Dev workflow signal

Context
400K Tokens

Large

Input Price
$0.20

Budget tier

Decision snapshot
63

OpenAI: GPT-5.4 Nano currently reads as a budget multimodal option with large context and a balanced runtime profile.

Overall profile
Selective fit
Best for
Long-context research / Multimodal
Latency tier
Balanced
Price tier
Budget
Source coverage
OpenRouterArtificial AnalysisVision signal

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
44.4
44

General reasoning and benchmark headroom.

Situational
Speed
200 tok/s
70

TTFT 2.52s

Competitive
Context
400K Tokens
88

How much prompt and task state can stay in view.

Above average
Price
$0.20
86

$1.25 output / 1M

Efficient

Editorial Profile

OpenAI: GPT-5.4 Nano in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Selective fitCoding score 44Math score N/AVision enabled

GPT-5.4 nano is the most lightweight and cost-efficient variant of the GPT-5.4 family, optimized for speed-critical and high-volume tasks. It supports text and image inputs and is designed for low-latency...

Identity

OpenAI multimodal profile

Positioning

Long-context research / Multimodal with large context and balanced runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • Large context headroom supports repo-wide prompts and long research sessions.

  • Vision-capable routing opens up multimodal review and extraction workflows.

  • Latency and throughput look responsive enough for interactive loops.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Latency is balanced rather than ultra-fast, which is fine for most workflows but not the snappiest tier.

Best fit
  • Image-grounded review, multimodal extraction, and UI audit workflows.

  • Long-context summarization, repo analysis, and policy or document review.

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Context
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Context
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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
44.4
GPQA
81.7%
HLE
26.5%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
43.9
SciCode
46.9%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
75.9%
TAU2
81.0%
TerminalBench Hard
42.4%
LCR
66.0%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
400K Tokens
Vision
Enabled
Modalities
file, image, text
Tokenizer
GPT
Max Completion
128000
Moderation
Yes
Supported Parameters
include_reasoningmax_completion_tokensmax_tokensreasoningresponse_formatseedstructured_outputstool_choicetools
Input Modalities
fileimagetext
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.20
Output
per 1M output tokens
$1.25
Blended
AA 3:1 mix
$0.46

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

OR Web Search Price
$0.0100
OR Cache Read
$0.00

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.