Model Comparison
GLM-4.7 vs MiniMax M2Which is better in 2026?
GLM-4.7 significantly outperforms across most benchmarks. MiniMax M2 is 1.9x cheaper per token.
Verdict: GLM-4.7 vs MiniMax M2 — which is better?
GLM-4.7 (by Zhipu AI) and MiniMax M2 (by MiniMax) 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.
GLM-4.7 outperforms in 9 benchmarks (AIME 2025, BrowseComp, BrowseComp-zh, GPQA, Humanity's Last Exam, MMLU-Pro, SWE-bench Multilingual, SWE-Bench Verified, Tau-bench), while MiniMax M2 is better at 1 benchmark (Terminal-Bench). GLM-4.7 significantly outperforms across most benchmarks.
On price, MiniMax M2 is roughly 1.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiniMax M2 also accepts a larger context window (1,000,000 input tokens), making it the stronger choice for long documents and large codebases.
Choose GLM-4.7 if…
- you want the strongest raw capability — it leads on 9 of 10 shared benchmarks
- you want the most recent training data — it shipped Dec 2025
Choose MiniMax M2 if…
- cost matters — it's about 1.9x cheaper per token
- you process long inputs — it offers a 1,000,000 token context window
Performance Benchmarks
Comparative analysis across standard metrics
GLM-4.7 outperforms in 9 benchmarks (AIME 2025, BrowseComp, BrowseComp-zh, GPQA, Humanity's Last Exam, MMLU-Pro, SWE-bench Multilingual, SWE-Bench Verified, Tau-bench), while MiniMax M2 is better at 1 benchmark (Terminal-Bench).
GLM-4.7 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-4.7 ($0.60/1M tokens) is 2.0x more expensive than MiniMax M2 ($0.30/1M tokens).
For output processing, GLM-4.7 ($2.20/1M tokens) is 1.8x more expensive than MiniMax M2 ($1.20/1M tokens).
In conclusion, GLM-4.7 is more expensive than MiniMax M2.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-4.7 has 128.0B more parameters than MiniMax M2, making it 55.7% larger.
Context Window
Maximum input and output token capacity
MiniMax M2 accepts 1,000,000 input tokens compared to GLM-4.7's 202,800 tokens. MiniMax M2 can generate longer responses up to 1,000,000 tokens, while GLM-4.7 is limited to 131,072 tokens.
Input Capabilities
Supported data types and modalities
GLM-4.7 supports multimodal inputs, whereas MiniMax M2 does not.
GLM-4.7 can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-4.7
MiniMax M2
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
GLM-4.7 was released on 2025-12-22, while MiniMax M2 was released on 2025-10-27.
GLM-4.7 is 2 months newer than MiniMax M2.
Dec 22, 2025
7 months ago
1mo newerOct 27, 2025
9 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GLM-4.7 is available from Fireworks, Novita. MiniMax M2 is available from MiniMax, Novita.
GLM-4.7
MiniMax M2
Outputs Comparison
Key Takeaways
GLM-4.7
View detailsZhipu AI
MiniMax M2
View detailsMiniMax
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against GLM-4.7 and MiniMax M2 side-by-side, then vote on the output you prefer.
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FAQ
Common questions about GLM-4.7 vs MiniMax M2.