Model Comparison

GPT-4.1 nano vs GPT-5.6 LunaWhich is better in 2026?

GPT-5.6 Luna significantly outperforms across most benchmarks. GPT-4.1 nano is 12.9x cheaper per token.

Verdict: GPT-4.1 nano vs GPT-5.6 Luna — which is better?

GPT-4.1 nano (by OpenAI) and GPT-5.6 Luna (by OpenAI) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

GPT-4.1 nano outperforms in 0 benchmarks, while GPT-5.6 Luna is better at 2 benchmarks (GPQA, Graphwalks BFS >128k). GPT-5.6 Luna significantly outperforms across most benchmarks.

On price, GPT-4.1 nano is roughly 12.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

GPT-5.6 Luna also accepts a larger context window (1,050,000 input tokens), making it the stronger choice for long documents and large codebases.

Choose GPT-4.1 nano if…

  • cost matters — it's about 12.9x cheaper per token

Choose GPT-5.6 Luna if…

  • you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
  • you process long inputs — it offers a 1,050,000 token context window
  • you want the most recent training data — it shipped Jul 2026

Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

GPT-4.1 nano outperforms in 0 benchmarks, while GPT-5.6 Luna is better at 2 benchmarks (GPQA, Graphwalks BFS >128k).

GPT-5.6 Luna significantly outperforms across most benchmarks.

Mon Jul 20 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

GPT-4.1 nano costs less

For input processing, GPT-4.1 nano ($0.10/1M tokens) is 10.0x cheaper than GPT-5.6 Luna ($1.00/1M tokens).

For output processing, GPT-4.1 nano ($0.40/1M tokens) is 15.0x cheaper than GPT-5.6 Luna ($6.00/1M tokens).

In conclusion, GPT-5.6 Luna is more expensive than GPT-4.1 nano.*

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

Lowest available price from all providers
Mon Jul 20 2026 • llm-stats.com
OpenAI
GPT-4.1 nano
Input tokens$0.10
Output tokens$0.40
Best providerOpenAI
OpenAI
GPT-5.6 Luna
Input tokens$1.00
Output tokens$6.00
Best providerOpenAI
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

GPT-5.6 Luna accepts 1,050,000 input tokens compared to GPT-4.1 nano's 1,047,576 tokens. GPT-5.6 Luna can generate longer responses up to 128,000 tokens, while GPT-4.1 nano is limited to 32,768 tokens.

OpenAI
GPT-4.1 nano
Input1,047,576 tokens
Output32,768 tokens
OpenAI
GPT-5.6 Luna
Input1,050,000 tokens
Output128,000 tokens
Mon Jul 20 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both GPT-4.1 nano and GPT-5.6 Luna support multimodal inputs.

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

GPT-4.1 nano

Text
Images
Audio
Video

GPT-5.6 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-4.1 nano

Proprietary

Closed source

GPT-5.6 Luna

Proprietary

Closed source

Release Timeline

When each model was launched

GPT-4.1 nano was released on 2025-04-14, while GPT-5.6 Luna was released on 2026-07-09.

GPT-5.6 Luna is 15 months newer than GPT-4.1 nano.

GPT-4.1 nano

Apr 14, 2025

1.3 years ago

GPT-5.6 Luna

Jul 9, 2026

1 weeks ago

1.2yr newer

Knowledge Cutoff

When training data ends

GPT-4.1 nano has a knowledge cutoff of 2024-05-31, while GPT-5.6 Luna has a cutoff of 2026-02-16.

GPT-5.6 Luna has more recent training data (up to 2026-02-16), making it potentially better informed about events through that date compared to GPT-4.1 nano (2024-05-31).

GPT-4.1 nano

May 2024

GPT-5.6 Luna

Feb 2026

1.8 yr newer

Provider Availability

GPT-4.1 nano is available from OpenAI. GPT-5.6 Luna is available from OpenAI.

GPT-4.1 nano

openai logo
OpenAI
Input Price:Input: $0.10/1MOutput Price:Output: $0.40/1M

GPT-5.6 Luna

openai logo
OpenAI
Input Price:Input: $1.00/1MOutput Price:Output: $6.00/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

OpenAI
Less expensive input tokens
Less expensive output tokens
OpenAI
Larger context window (1,050,000 tokens)
Higher GPQA score (92.3% vs 50.3%)
Higher Graphwalks BFS >128k score (81.3% vs 2.9%)

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against GPT-4.1 nano and GPT-5.6 Luna side-by-side, then vote on the output you prefer.

GPT-4.1 nano
✓ Preferred
GPT-5.6 Luna
Open in Playground
AI Model Comparison Table
Feature
OpenAI
GPT-4.1 nano
OpenAI
GPT-5.6 Luna

FAQ

Common questions about GPT-4.1 nano vs GPT-5.6 Luna.

Which is better, GPT-4.1 nano or GPT-5.6 Luna?

GPT-5.6 Luna significantly outperforms across most benchmarks. GPT-4.1 nano is made by OpenAI and GPT-5.6 Luna is made by OpenAI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does GPT-4.1 nano compare to GPT-5.6 Luna in benchmarks?

GPT-4.1 nano scores MMLU: 80.1%, IFEval: 74.5%, CharXiv-D: 73.9%, MMMLU: 66.9%, Multi-IF: 57.2%. GPT-5.6 Luna scores Connectors: 99.9%, HealthBench Consensus: 95.1%, GPQA: 92.3%, Search and Function-Calling: 89.7%, Capture-the-Flag Challenges (Internal): 85.2%.

Is GPT-4.1 nano cheaper than GPT-5.6 Luna?

GPT-4.1 nano is 10.0x cheaper for input tokens. GPT-4.1 nano costs $0.10/M input and $0.40/M output via openai. GPT-5.6 Luna costs $1.00/M input and $6.00/M output via openai.

What are the context window sizes for GPT-4.1 nano and GPT-5.6 Luna?

GPT-4.1 nano supports 1.0M tokens and GPT-5.6 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-4.1 nano and GPT-5.6 Luna?

Key differences include context window (1.0M vs 1.1M), input pricing ($0.10 vs $1.00/M). See the full comparison above for benchmark-by-benchmark results.

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