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GPT-5.4 nano vs GPT-6 Luna

GPT-6 Luna leads the LLM Stats Score 44.5 to 26.6. GPT-6 Luna is 2.3x cheaper per token.

OpenAI · OpenAI · Updated for 2026

Which is better?

GPT-6 Luna leads the overall LLM Stats Score 44.5 to 26.6, ranking #40 overall.

On price, GPT-6 Luna is roughly 2.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

GPT-6 Luna also accepts a larger context window (1,050,000 input tokens), making it the stronger choice for long documents and large codebases.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose GPT-5.4 nano

  • you want predictable pricing at $0.20/M input and $1.25/M output

Choose GPT-6 Luna

  • overall performance matters — it scores 44.5 and ranks #40 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • cost matters — it's about 2.3x cheaper per token
  • you process long inputs — it offers a 1,050,000 token context window
  • you want the most recent training data — it shipped Sep 2026

At a glance

The differences that matter most.

GPT-5.4 nano
OpenAI
GPT-6 Luna
OpenAI
Core performance indexes
LLM Stats Score
26.6
#156
44.5
#40
Reasoning
27.4
#142
40.0
#58
Coding
15.7
#134
31.4
#47
Agents
10.8
#118
31.8
#30
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.20 / M
$0.10 / M
Output price
$1.25 / M
$0.50 / M
Context window
400,000
1,050,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
GPT-5.4 nano
GPT-6 Luna
Tool use
10.6#127
22.1#47
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

15 reported for GPT-5.4 nano · 5 for GPT-6 Luna

No common benchmarks found

GPT-5.4 nano and GPT-6 Lunadon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

GPT-6 Luna costs less

For input processing, GPT-5.4 nano ($0.20/1M tokens) is 2.0x more expensive than GPT-6 Luna ($0.10/1M tokens).

For output processing, GPT-5.4 nano ($1.25/1M tokens) is 2.5x more expensive than GPT-6 Luna ($0.50/1M tokens).

In conclusion, GPT-5.4 nano is more expensive than GPT-6 Luna.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Wed Sep 23 2026 • llm-stats.com
OpenAI
GPT-5.4 nano
Input tokens$0.20
Output tokens$1.25
Best providerOpenAI
OpenAI
GPT-6 Luna
Input tokens$0.10
Output tokens$0.50
Best providerOpenAI
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

GPT-6 Luna accepts 1,050,000 input tokens compared to GPT-5.4 nano's 400,000 tokens. Both models can generate responses up to 128,000 tokens.

OpenAI
GPT-5.4 nano
Input400,000 tokens
Output128,000 tokens
OpenAI
GPT-6 Luna
Input1,050,000 tokens
Output128,000 tokens
Wed Sep 23 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both GPT-5.4 nano and GPT-6 Luna support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

GPT-5.4 nano

Text
Images
Audio
Video

GPT-6 Luna

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under proprietary licenses.

Both models have usage restrictions defined by their respective organizations.

GPT-5.4 nano

Proprietary

Closed source

GPT-6 Luna

Proprietary

Closed source

Release Timeline

When each model was launched

GPT-5.4 nano was released on 2026-03-17, while GPT-6 Luna was released on 2026-09-22.

GPT-6 Luna is 6 months newer than GPT-5.4 nano.

GPT-5.4 nano

Mar 17, 2026

6 months ago

GPT-6 Luna

Sep 22, 2026

0 days ago

6mo newer

Knowledge Cutoff

When training data ends

GPT-5.4 nano has a knowledge cutoff of 2025-08-31, while GPT-6 Luna has a cutoff of 2026-05-18.

GPT-6 Luna has more recent training data (up to 2026-05-18), making it potentially better informed about events through that date compared to GPT-5.4 nano (2025-08-31).

GPT-5.4 nano

Aug 2025

GPT-6 Luna

May 2026

9 mo newer

Provider Availability

GPT-5.4 nano is available from OpenAI. GPT-6 Luna is available from OpenAI.

GPT-5.4 nano

openai logo
OpenAI
Input Price:Input: $0.20/1MOutput Price:Output: $1.25/1M

GPT-6 Luna

openai logo
OpenAI
Input Price:Input: $0.10/1MOutput Price:Output: $0.50/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against GPT-5.4 nano and GPT-6 Luna side-by-side, then vote on the output you prefer.

GPT-5.4 nano
✓ Preferred
GPT-6 Luna
Open in Playground

FAQ

Common questions about GPT-5.4 nano vs GPT-6 Luna.

Which is better, GPT-5.4 nano or GPT-6 Luna?

GPT-6 Luna leads the LLM Stats Score 44.5 to 26.6. GPT-5.4 nano is made by OpenAI and GPT-6 Luna is made by OpenAI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does GPT-5.4 nano compare to GPT-6 Luna in benchmarks?

GPT-5.4 nano scores Tau2 Telecom: 92.5%, GPQA: 82.8%, OmniDocBench 1.5: 75.8%, Graphwalks BFS <128k: 73.4%, LiveBench: 70.1%. GPT-6 Luna scores DeepSWE 1.1: 66.6%, OSWorld 2.0: 52.7%, Agents' Last Exam: 50.9%, FrontierCode 1.1: 42.4%, AutomationBench v1.0.6: 20.7%.

Is GPT-5.4 nano cheaper than GPT-6 Luna?

GPT-6 Luna is 2.0x cheaper for input tokens. GPT-5.4 nano costs $0.20/M input and $1.25/M output via openai. GPT-6 Luna costs $0.10/M input and $0.50/M output via openai.

What are the context window sizes for GPT-5.4 nano and GPT-6 Luna?

GPT-5.4 nano supports 400K tokens and GPT-6 Luna supports 1.1M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GPT-5.4 nano and GPT-6 Luna?

Key differences include LLM Stats Score (26.6 vs 44.5), context window (400K vs 1.1M), input pricing ($0.20 vs $0.10/M). See the full comparison above for benchmark-by-benchmark results.

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