GPT-5.4 vs MiniMax M2.7
GPT-5.4 and MiniMax M2.7 are closely matched at 42.5 and 35.5 on the LLM Stats Score. MiniMax M2.7 is 10.7x cheaper per token.
OpenAI · MiniMax · Updated for 2026
Which is better?
GPT-5.4 and MiniMax M2.7 are closely matched on the overall LLM Stats Score at 42.5 and 35.5.
In the 3 individual benchmarks reported for both models, GPT-5.4 wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, MiniMax M2.7 is roughly 10.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GPT-5.4 also accepts a larger context window (1,000,000 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-5.4
- your work emphasizes agents — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- you process long inputs — it offers a 1,000,000 token context window
Choose MiniMax M2.7
- cost matters — it's about 10.7x cheaper per token
- you want the most recent training data — it shipped Mar 2026
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
23 reported for GPT-5.4 · 11 for MiniMax M2.7
GPT-5.4 outperforms in 3 benchmarks (SWE-Bench Pro, Terminal-Bench 2.0, Toolathlon), while MiniMax M2.7 is better at 0 benchmarks.
GPT-5.4 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GPT-5.4 ($2.50/1M tokens) is 8.3x more expensive than MiniMax M2.7 ($0.30/1M tokens).
For output processing, GPT-5.4 ($15.00/1M tokens) is 12.5x more expensive than MiniMax M2.7 ($1.20/1M tokens).
In conclusion, GPT-5.4 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-5.4 accepts 1,000,000 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-5.4 is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
GPT-5.4 supports multimodal inputs, whereas MiniMax M2.7 does not.
GPT-5.4 can handle both text and other forms of data like images, making it suitable for multimodal applications.
GPT-5.4
MiniMax M2.7
License
Usage and distribution terms
GPT-5.4 is licensed under a proprietary license, while MiniMax M2.7 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-5.4 was released on 2026-03-05, while MiniMax M2.7 was released on 2026-03-18.
MiniMax M2.7 is 0 month newer than GPT-5.4.
Mar 5, 2026
6 months ago
Mar 18, 2026
5 months ago
1w newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GPT-5.4 is available from OpenAI. MiniMax M2.7 is available from Fireworks, MiniMax, Novita.
GPT-5.4
MiniMax M2.7
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-5.4 and MiniMax M2.7 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-5.4 vs MiniMax M2.7.