GPT-6 Luna vs Phi 4
GPT-6 Luna leads the LLM Stats Score 44.5 to 5.4. Phi 4 is 2.3x cheaper per token.
OpenAI · Microsoft · Updated for 2026
Which is better?
GPT-6 Luna leads the overall LLM Stats Score 44.5 to 5.4, ranking #41 overall.
On price, Phi 4 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-6 Luna
- overall performance matters — it scores 44.5 and ranks #41 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you process long inputs — it offers a 1,050,000 token context window
- you want the most recent training data — it shipped Sep 2026
Choose Phi 4
- cost matters — it's about 2.3x cheaper per token
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Individual benchmarks
5 reported for GPT-6 Luna · 13 for Phi 4
GPT-6 Luna and Phi 4don'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
For input processing, GPT-6 Luna ($0.10/1M tokens) is 1.4x more expensive than Phi 4 ($0.07/1M tokens).
For output processing, GPT-6 Luna ($0.50/1M tokens) is 3.6x more expensive than Phi 4 ($0.14/1M tokens).
In conclusion, GPT-6 Luna is more expensive than Phi 4.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GPT-6 Luna accepts 1,050,000 input tokens compared to Phi 4's 16,384 tokens. GPT-6 Luna can generate longer responses up to 128,000 tokens, while Phi 4 is limited to 16,384 tokens.
Input capabilities
Documented input modalities across available providers
GPT-6 Luna supports multimodal inputs, whereas Phi 4 does not.
GPT-6 Luna can handle both text and other forms of data like images, making it suitable for multimodal applications.
GPT-6 Luna
Phi 4
License
Usage and distribution terms
GPT-6 Luna is licensed under a proprietary license, while Phi 4 uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
MIT
Open weights
Release Timeline
When each model was launched
GPT-6 Luna was released on 2026-09-22, while Phi 4 was released on 2024-12-12.
GPT-6 Luna is 22 months newer than Phi 4.
Sep 22, 2026
0 days ago
1.8yr newerDec 12, 2024
1.8 years ago
Knowledge Cutoff
When training data ends
GPT-6 Luna has a knowledge cutoff of 2026-05-18, while Phi 4 has a cutoff of 2024-06-01.
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 Phi 4 (2024-06-01).
May 2026
1.9 yr newerJun 2024
Provider Availability
GPT-6 Luna is available from OpenAI. Phi 4 is available from DeepInfra.
GPT-6 Luna
Phi 4
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-6 Luna and Phi 4 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-6 Luna vs Phi 4.