Model Comparison

GPT-5.6 Luna vs Phi-3.5-mini-instructWhich is better in 2026?

GPT-5.6 Luna significantly outperforms across most benchmarks. Phi-3.5-mini-instruct is 22.5x cheaper per token.

Verdict: GPT-5.6 Luna vs Phi-3.5-mini-instruct — which is better?

GPT-5.6 Luna (by OpenAI) and Phi-3.5-mini-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 (GPQA), while Phi-3.5-mini-instruct is better at 0 benchmarks. GPT-5.6 Luna significantly outperforms across most benchmarks.

On price, Phi-3.5-mini-instruct is roughly 22.5x 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-3.5-mini-instruct if…

  • cost matters — it's about 22.5x cheaper per token
  • you need open weights you can self-host or fine-tune

Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

GPT-5.6 Luna outperforms in 1 benchmarks (GPQA), while Phi-3.5-mini-instruct is better at 0 benchmarks.

GPT-5.6 Luna significantly outperforms across most benchmarks.

Sat Jul 18 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Phi-3.5-mini-instruct costs less

For input processing, GPT-5.6 Luna ($1.00/1M tokens) is 10.0x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).

For output processing, GPT-5.6 Luna ($6.00/1M tokens) is 60.0x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).

In conclusion, GPT-5.6 Luna is more expensive than Phi-3.5-mini-instruct.*

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

Lowest available price from all providers
Sat Jul 18 2026 • llm-stats.com
OpenAI
GPT-5.6 Luna
Input tokens$1.00
Output tokens$6.00
Best providerOpenAI
Microsoft
Phi-3.5-mini-instruct
Input tokens$0.10
Output tokens$0.10
Best providerAzure
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 Phi-3.5-mini-instruct's 128,000 tokens. Both models can generate responses up to 128,000 tokens.

OpenAI
GPT-5.6 Luna
Input1,050,000 tokens
Output128,000 tokens
Microsoft
Phi-3.5-mini-instruct
Input128,000 tokens
Output128,000 tokens
Sat Jul 18 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

GPT-5.6 Luna supports multimodal inputs, whereas Phi-3.5-mini-instruct does not.

GPT-5.6 Luna can handle both text and other forms of data like images, making it suitable for multimodal applications.

GPT-5.6 Luna

Text
Images
Audio
Video

Phi-3.5-mini-instruct

Text
Images
Audio
Video

License

Usage and distribution terms

GPT-5.6 Luna is licensed under a proprietary license, while Phi-3.5-mini-instruct uses MIT.

License differences may affect how you can use these models in commercial or open-source projects.

GPT-5.6 Luna

Proprietary

Closed source

Phi-3.5-mini-instruct

MIT

Open weights

Release Timeline

When each model was launched

GPT-5.6 Luna was released on 2026-07-09, while Phi-3.5-mini-instruct was released on 2024-08-23.

GPT-5.6 Luna is 23 months newer than Phi-3.5-mini-instruct.

GPT-5.6 Luna

Jul 9, 2026

1 weeks ago

1.9yr newer
Phi-3.5-mini-instruct

Aug 23, 2024

1.9 years ago

Knowledge Cutoff

When training data ends

GPT-5.6 Luna has a documented knowledge cutoff of 2026-02-16, while Phi-3.5-mini-instruct's cutoff date is not specified.

We can confirm GPT-5.6 Luna's training data extends to 2026-02-16, but cannot make a direct comparison without Phi-3.5-mini-instruct's cutoff date.

GPT-5.6 Luna

Feb 2026

Phi-3.5-mini-instruct

—

Provider Availability

GPT-5.6 Luna is available from OpenAI. Phi-3.5-mini-instruct is available from Azure.

GPT-5.6 Luna

openai logo
OpenAI
Input Price:Input: $1.00/1MOutput Price:Output: $6.00/1M

Phi-3.5-mini-instruct

azure logo
Azure
Input Price:Input: $0.10/1MOutput Price:Output: $0.10/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

OpenAI
Larger context window (1,050,000 tokens)
Supports multimodal inputs
Higher GPQA score (92.3% vs 30.4%)
Microsoft
Less expensive input tokens
Less expensive output tokens
Has open weights

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against GPT-5.6 Luna and Phi-3.5-mini-instruct side-by-side, then vote on the output you prefer.

GPT-5.6 Luna
✓ Preferred
Phi-3.5-mini-instruct
Open in Playground
AI Model Comparison Table
Feature
OpenAI
GPT-5.6 Luna
Microsoft
Phi-3.5-mini-instruct

FAQ

Common questions about GPT-5.6 Luna vs Phi-3.5-mini-instruct.

Which is better, GPT-5.6 Luna or Phi-3.5-mini-instruct?

GPT-5.6 Luna significantly outperforms across most benchmarks. GPT-5.6 Luna is made by OpenAI and Phi-3.5-mini-instruct is made by Microsoft. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does GPT-5.6 Luna compare to Phi-3.5-mini-instruct in benchmarks?

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%. Phi-3.5-mini-instruct scores GSM8k: 86.2%, ARC-C: 84.6%, RULER: 84.1%, PIQA: 81.0%, OpenBookQA: 79.2%.

Is GPT-5.6 Luna cheaper than Phi-3.5-mini-instruct?

Phi-3.5-mini-instruct is 10.0x cheaper for input tokens. GPT-5.6 Luna costs $1.00/M input and $6.00/M output via openai. Phi-3.5-mini-instruct costs $0.10/M input and $0.10/M output via azure.

What are the context window sizes for GPT-5.6 Luna and Phi-3.5-mini-instruct?

GPT-5.6 Luna supports 1.1M tokens and Phi-3.5-mini-instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GPT-5.6 Luna and Phi-3.5-mini-instruct?

Key differences include context window (1.1M vs 128K), input pricing ($1.00 vs $0.10/M), multimodal support (yes vs no), licensing (Proprietary vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes GPT-5.6 Luna and Phi-3.5-mini-instruct?

GPT-5.6 Luna is developed by OpenAI and Phi-3.5-mini-instruct is developed by Microsoft.

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