Model Comparison
MiniMax M2.7 vs GPT-5.6 LunaWhich is better in 2026?
GPT-5.6 Luna significantly outperforms across most benchmarks. MiniMax M2.7 is 4.3x cheaper per token.
Verdict: MiniMax M2.7 vs GPT-5.6 Luna — which is better?
MiniMax M2.7 (by MiniMax) 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.
MiniMax M2.7 outperforms in 0 benchmarks, while GPT-5.6 Luna is better at 4 benchmarks (Artificial Analysis, GDPval-AA, SWE-Bench Pro, Toolathlon). GPT-5.6 Luna significantly outperforms across most benchmarks.
On price, MiniMax M2.7 is roughly 4.3x 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 MiniMax M2.7 if…
- cost matters — it's about 4.3x cheaper per token
- you need open weights you can self-host or fine-tune
Choose GPT-5.6 Luna if…
- you want the strongest raw capability — it leads on 4 of 4 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
MiniMax M2.7 outperforms in 0 benchmarks, while GPT-5.6 Luna is better at 4 benchmarks (Artificial Analysis, GDPval-AA, SWE-Bench Pro, Toolathlon).
GPT-5.6 Luna significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, MiniMax M2.7 ($0.30/1M tokens) is 3.3x cheaper than GPT-5.6 Luna ($1.00/1M tokens).
For output processing, MiniMax M2.7 ($1.20/1M tokens) is 5.0x cheaper than GPT-5.6 Luna ($6.00/1M tokens).
In conclusion, GPT-5.6 Luna is more expensive than MiniMax M2.7.*
* 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 MiniMax M2.7's 196,608 tokens. MiniMax M2.7 can generate longer responses up to 196,608 tokens, while GPT-5.6 Luna is limited to 128,000 tokens.
Input Capabilities
Supported data types and modalities
GPT-5.6 Luna supports multimodal inputs, whereas MiniMax M2.7 does not.
GPT-5.6 Luna can handle both text and other forms of data like images, making it suitable for multimodal applications.
MiniMax M2.7
GPT-5.6 Luna
License
Usage and distribution terms
MiniMax M2.7 is licensed under MIT, while GPT-5.6 Luna uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
MiniMax M2.7 was released on 2026-03-18, while GPT-5.6 Luna was released on 2026-07-09.
GPT-5.6 Luna is 4 months newer than MiniMax M2.7.
Mar 18, 2026
4 months ago
Jul 9, 2026
1 weeks ago
3mo newerKnowledge Cutoff
When training data ends
GPT-5.6 Luna has a documented knowledge cutoff of 2026-02-16, while MiniMax M2.7'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 MiniMax M2.7's cutoff date.
—
Feb 2026
Provider Availability
MiniMax M2.7 is available from Fireworks, MiniMax, Novita. GPT-5.6 Luna is available from OpenAI.
MiniMax M2.7
GPT-5.6 Luna
Outputs Comparison
Key Takeaways
MiniMax M2.7
View detailsMiniMax
GPT-5.6 Luna
View detailsOpenAI
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against MiniMax M2.7 and GPT-5.6 Luna side-by-side, then vote on the output you prefer.
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FAQ
Common questions about MiniMax M2.7 vs GPT-5.6 Luna.