GPT-5.4 Nano
Cheapest GPT-5.4 lane for simple routing, extraction, and bulk automation
- Context
- 400K
- Released
- 7mo ago
- Input / 1M tokens
- $0.20
- Output / 1M tokens
- $1.25
- Cached input / 1M tokens
- $0.02
Sources:models.devOpenRouter
Benchmarks
DeepSWE is the primary coding benchmark where an exact model evaluation exists. Other sourced evaluations appear below; missing scores are not zero.
Not evaluated in DeepSWE 1.1 as of 2026-09-22. Results are only shown for exact model versions in the official dataset. Official leaderboard.
No benchmark results have been matched to this model yet.
Specifications
- Developer
- OpenAI
- Model family
- GPT
- Type
- Language model
- Predecessor
- —
- Successor
- —
- Released
- March 17, 2026
- Model ID
openai/gpt-5.4-nano- Openness
- Closed
- License
- —
- Input modalities
- file, image, text
- Output modalities
- text
- Modality
- text+image+file->text
- Tokenizer
- GPT
- Max output tokens
- 128K
- Knowledge cutoff
- 2025-08-31
- Hugging Face
- —
- Source freshness
- Metadata synced Oct 8, 2026, 10:17 PM UTC
Additional source evidence
- Source model
- openai/gpt-5.4-nano
- Source family
- gpt-nano
- Last updated
- 2026-03-17
- Knowledge cutoff
- 2025-08-31
- Model context
- 400K
- Maximum input
- 272K
- Model output limit
- 128K
- Open weights
- No
- File attachments
- Yes
- Reasoning
- Yes
- Tool calling
- Yes
- Structured output
- Yes
- Temperature control
- Yes
Published benchmarks
| Benchmark | Score | Evaluation details | Evidence |
|---|---|---|---|
| SWE-Bench Pro | 52.4 · resolve rate | reasoning effort xhigh · 2026-03-17 | Source |
| Terminal-Bench | 46.3 · accuracy | reasoning effort xhigh · 2.0 · 2026-03-17 | Source |
| MCP Atlas | 56.1 · score | reasoning effort xhigh · 2026-03-17 | Source |
| Toolathlon | 35.5 · score | reasoning effort xhigh · 2026-03-17 | Source |
| τ²-Bench Telecom | 92.5 · accuracy | reasoning effort xhigh · 2026-03-17 | Source |
| GPQA Diamond | 82.8 · accuracy | reasoning effort xhigh · 2026-03-17 | Source |
| Humanity's Last Exam | 37.7 · accuracy | with tools · 2026-03-17 | Source |
| Humanity's Last Exam | 24.3 · accuracy | without tools · 2026-03-17 | Source |
| OSWorld-Verified | 39 · success rate | reasoning effort xhigh · 2026-03-17 | Source |
| MMMU Pro | 69.5 · accuracy | with Python · 2026-03-17 | Source |
| MMMU Pro | 66.1 · accuracy | without tools · 2026-03-17 | Source |
| OmniDocBench | 0.2419 · overall edit distance | reasoning effort none · 1.5 · 2026-03-17 | Source |
| OpenAI MRCR | 44.2 · accuracy | 8-needle, 64K-128K · v2 · 2026-03-17 | Source |
| OpenAI MRCR | 33.1 · accuracy | 8-needle, 128K-256K · v2 · 2026-03-17 | Source |
| Graphwalks | 73.4 · accuracy | BFS, 0-128K · 2026-03-17 | Source |
| Graphwalks | 50.8 · accuracy | parents, 0-128K · 2026-03-17 | Source |
Complete source record (JSON)
{
"id": "openai/gpt-5.4-nano",
"name": "GPT-5.4 nano",
"description": "Cheapest GPT-5.4 lane for simple routing, extraction, and bulk automation",
"family": "gpt-nano",
"last_updated": "2026-03-17",
"knowledge": "2025-08-31",
"attachment": true,
"reasoning": true,
"tool_call": true,
"structured_output": true,
"temperature": true,
"modalities": {
"input": [
"text",
"image"
],
"output": [
"text"
]
},
"limit": {
"context": 400000,
"input": 272000,
"output": 128000
},
"benchmarks": [
{
"name": "SWE-Bench Pro",
"score": 52.4,
"source": "https://openai.com/index/introducing-gpt-5-4-mini-and-nano/",
"metric": "resolve rate",
"variant": "reasoning effort xhigh",
"date": "2026-03-17"
},
{
"name": "Terminal-Bench",
"score": 46.3,
"source": "https://openai.com/index/introducing-gpt-5-4-mini-and-nano/",
"metric": "accuracy",
"variant": "reasoning effort xhigh",
"version": "2.0",
"date": "2026-03-17"
},
{
"name": "MCP Atlas",
"score": 56.1,
"source": "https://openai.com/index/introducing-gpt-5-4-mini-and-nano/",
"metric": "score",
"variant": "reasoning effort xhigh",
"date": "2026-03-17"
},
{
"name": "Toolathlon",
"score": 35.5,
"source": "https://openai.com/index/introducing-gpt-5-4-mini-and-nano/",
"metric": "score",
"variant": "reasoning effort xhigh",
"date": "2026-03-17"
},
{
"name": "τ²-Bench Telecom",
"score": 92.5,
"source": "https://openai.com/index/introducing-gpt-5-4-mini-and-nano/",
"metric": "accuracy",
"variant": "reasoning effort xhigh",
"date": "2026-03-17"
},
{
"name": "GPQA Diamond",
"score": 82.8,
"source": "https://openai.com/index/introducing-gpt-5-4-mini-and-nano/",
"metric": "accuracy",
"variant": "reasoning effort xhigh",
"date": "2026-03-17"
},
{
"name": "Humanity's Last Exam",
"score": 37.7,
"source": "https://openai.com/index/introducing-gpt-5-4-mini-and-nano/",
"metric": "accuracy",
"variant": "with tools",
"date": "2026-03-17"
},
{
"name": "Humanity's Last Exam",
"score": 24.3,
"source": "https://openai.com/index/introducing-gpt-5-4-mini-and-nano/",
"metric": "accuracy",
"variant": "without tools",
"date": "2026-03-17"
},
{
"name": "OSWorld-Verified",
"score": 39,
"source": "https://openai.com/index/introducing-gpt-5-4-mini-and-nano/",
"metric": "success rate",
"variant": "reasoning effort xhigh",
"date": "2026-03-17"
},
{
"name": "MMMU Pro",
"score": 69.5,
"source": "https://openai.com/index/introducing-gpt-5-4-mini-and-nano/",
"metric": "accuracy",
"variant": "with Python",
"date": "2026-03-17"
},
{
"name": "MMMU Pro",
"score": 66.1,
"source": "https://openai.com/index/introducing-gpt-5-4-mini-and-nano/",
"metric": "accuracy",
"variant": "without tools",
"date": "2026-03-17"
},
{
"name": "OmniDocBench",
"score": 0.2419,
"source": "https://openai.com/index/introducing-gpt-5-4-mini-and-nano/",
"metric": "overall edit distance",
"variant": "reasoning effort none",
"version": "1.5",
"date": "2026-03-17"
},
{
"name": "OpenAI MRCR",
"score": 44.2,
"source": "https://openai.com/index/introducing-gpt-5-4-mini-and-nano/",
"metric": "accuracy",
"variant": "8-needle, 64K-128K",
"version": "v2",
"date": "2026-03-17"
},
{
"name": "OpenAI MRCR",
"score": 33.1,
"source": "https://openai.com/index/introducing-gpt-5-4-mini-and-nano/",
"metric": "accuracy",
"variant": "8-needle, 128K-256K",
"version": "v2",
"date": "2026-03-17"
},
{
"name": "Graphwalks",
"score": 73.4,
"source": "https://openai.com/index/introducing-gpt-5-4-mini-and-nano/",
"metric": "accuracy",
"variant": "BFS, 0-128K",
"date": "2026-03-17"
},
{
"name": "Graphwalks",
"score": 50.8,
"source": "https://openai.com/index/introducing-gpt-5-4-mini-and-nano/",
"metric": "accuracy",
"variant": "parents, 0-128K",
"date": "2026-03-17"
}
],
"release_date": "2026-03-17",
"open_weights": false
}Infrastructure
Provider availability
| Provider | Endpoint | Context | Input /M tokens | Output /M tokens | Cached input /M tokens | Cache write /M tokens | Uptime 1d | Data handling |
|---|---|---|---|---|---|---|---|---|
| openai/flex | 400K | $0.10 | $0.63 | $0.01 | — | 100.00% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| openai/flexBatch pricing | 400K | $0.10 | $0.63 | $0.01 | — | 100.00% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| azure | 400K | $0.20 | $1.25 | $0.02 | — | 99.79% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| azureBatch pricing | 400K | $0.20 | $1.25 | $0.02 | — | 100.00% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| openai | 400K | $0.20 | $1.25 | $0.02 | — | 100.00% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| openaiBatch pricing | 400K | $0.20 | $1.25 | $0.02 | — | 100.00% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| azure/us | 400K | $0.22 | $1.38 | $0.02 | — | 100.00% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| azure/usBatch pricing | 400K | $0.22 | $1.38 | $0.02 | — | — | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 |
Provider data:OpenRouter
Text provenance
EU · product-scopedOpenAI textGrain watermarking
In the EU, ChatGPT-generated text uses textGrain. OpenAI says API customers can opt in globally for select models, so watermarking depends on the product surface and configuration.
Official explanation