Model Comparison
GPT-5.6 Luna vs Phi-4-multimodal-instructWhich is better in 2026?
GPT-5.6 Luna significantly outperforms across most benchmarks. Phi-4-multimodal-instruct is 36.0x cheaper per token.
Verdict: GPT-5.6 Luna vs Phi-4-multimodal-instruct — which is better?
GPT-5.6 Luna (by OpenAI) and Phi-4-multimodal-instruct (by Microsoft) 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-5.6 Luna outperforms in 1 benchmarks (MMMU-Pro), while Phi-4-multimodal-instruct is better at 0 benchmarks. GPT-5.6 Luna significantly outperforms across most benchmarks.
On price, Phi-4-multimodal-instruct is roughly 36.0x 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-5.6 Luna if…
- you want the strongest raw capability — it leads on 1 of 1 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
Choose Phi-4-multimodal-instruct if…
- cost matters — it's about 36.0x cheaper per token
- you need open weights you can self-host or fine-tune
Performance Benchmarks
Comparative analysis across standard metrics
GPT-5.6 Luna outperforms in 1 benchmarks (MMMU-Pro), while Phi-4-multimodal-instruct is better at 0 benchmarks.
GPT-5.6 Luna significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, GPT-5.6 Luna ($1.00/1M tokens) is 20.0x more expensive than Phi-4-multimodal-instruct ($0.05/1M tokens).
For output processing, GPT-5.6 Luna ($6.00/1M tokens) is 60.0x more expensive than Phi-4-multimodal-instruct ($0.10/1M tokens).
In conclusion, GPT-5.6 Luna is more expensive than Phi-4-multimodal-instruct.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GPT-5.6 Luna accepts 1,050,000 input tokens compared to Phi-4-multimodal-instruct's 128,000 tokens. Both models can generate responses up to 128,000 tokens.
Input Capabilities
Supported data types and modalities
Both GPT-5.6 Luna and Phi-4-multimodal-instruct support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GPT-5.6 Luna
Phi-4-multimodal-instruct
License
Usage and distribution terms
GPT-5.6 Luna is licensed under a proprietary license, while Phi-4-multimodal-instruct 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-5.6 Luna was released on 2026-07-09, while Phi-4-multimodal-instruct was released on 2025-02-01.
GPT-5.6 Luna is 17 months newer than Phi-4-multimodal-instruct.
Jul 9, 2026
1 weeks ago
1.4yr newerFeb 1, 2025
1.5 years ago
Knowledge Cutoff
When training data ends
GPT-5.6 Luna has a knowledge cutoff of 2026-02-16, while Phi-4-multimodal-instruct has a cutoff of 2024-06-01.
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 Phi-4-multimodal-instruct (2024-06-01).
Feb 2026
1.7 yr newerJun 2024
Provider Availability
GPT-5.6 Luna is available from OpenAI. Phi-4-multimodal-instruct is available from DeepInfra.
GPT-5.6 Luna
Phi-4-multimodal-instruct
Outputs Comparison
Key Takeaways
GPT-5.6 Luna
View detailsOpenAI
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against GPT-5.6 Luna and Phi-4-multimodal-instruct side-by-side, then vote on the output you prefer.
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FAQ
Common questions about GPT-5.6 Luna vs Phi-4-multimodal-instruct.