Model Comparison

GLM-4.7 vs MiniMax M2

GLM-4.7 significantly outperforms across most benchmarks. MiniMax M2 is 1.9x cheaper per token.

Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

10 benchmarks

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.

Thu Jun 04 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

MiniMax M2 costs less

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

Lowest available price from all providers
Thu Jun 04 2026 • llm-stats.com
Zhipu AI
GLM-4.7
Input tokens$0.60
Output tokens$2.20
Best providerFireworks
MiniMax
MiniMax M2
Input tokens$0.30
Output tokens$1.20
Best providerMiniMax
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

128.0B diff

GLM-4.7 has 128.0B more parameters than MiniMax M2, making it 55.7% larger.

Zhipu AI
GLM-4.7
358.0Bparameters
MiniMax
MiniMax M2
230.0Bparameters
358.0B
GLM-4.7
230.0B
MiniMax M2

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.

Zhipu AI
GLM-4.7
Input202,800 tokens
Output131,072 tokens
MiniMax
MiniMax M2
Input1,000,000 tokens
Output1,000,000 tokens
Thu Jun 04 2026 • llm-stats.com

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

Text
Images
Audio
Video

MiniMax M2

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

GLM-4.7

MIT

Open weights

MiniMax M2

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.

GLM-4.7

Dec 22, 2025

5 months ago

1mo newer
MiniMax M2

Oct 27, 2025

7 months ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

GLM-4.7 is available from Fireworks, Novita. MiniMax M2 is available from MiniMax, Novita.

GLM-4.7

fireworks logo
Fireworks
Input Price:Input: $0.60/1MOutput Price:Output: $2.20/1M
novita logo
Novita
Input Price:Input: $0.60/1MOutput Price:Output: $2.20/1M

MiniMax M2

minimax logo
MiniMax
Input Price:Input: $0.30/1MOutput Price:Output: $1.20/1M
novita logo
Novita
Input Price:Input: $0.30/1MOutput Price:Output: $1.20/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Zhipu AI
Supports multimodal inputs
Higher AIME 2025 score (95.7% vs 78.0%)
Higher BrowseComp score (52.0% vs 44.0%)
Higher BrowseComp-zh score (66.6% vs 48.5%)
Higher GPQA score (85.7% vs 78.0%)
Higher Humanity's Last Exam score (42.8% vs 12.5%)
Higher MMLU-Pro score (84.3% vs 82.0%)
Higher SWE-bench Multilingual score (66.7% vs 56.5%)
Higher SWE-Bench Verified score (73.8% vs 69.4%)
Higher Tau-bench score (87.4% vs 77.2%)
MiniMax
Larger context window (1,000,000 tokens)
Less expensive input tokens
Less expensive output tokens
Higher Terminal-Bench score (46.3% vs 33.3%)

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-4.7
MiniMax
MiniMax M2

FAQ

Common questions about GLM-4.7 vs MiniMax M2.

Which is better, GLM-4.7 or MiniMax M2?

GLM-4.7 significantly outperforms across most benchmarks. GLM-4.7 is made by Zhipu AI and MiniMax M2 is made by MiniMax. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does GLM-4.7 compare to MiniMax M2 in benchmarks?

GLM-4.7 scores AIME 2025: 95.7%, Tau-bench: 87.4%, GPQA: 85.7%, LiveCodeBench v6: 84.9%, MMLU-Pro: 84.3%. MiniMax M2 scores Tau2 Telecom: 87.0%, LiveCodeBench: 83.0%, MMLU-Pro: 82.0%, AIME 2025: 78.0%, GPQA: 78.0%.

Is GLM-4.7 cheaper than MiniMax M2?

MiniMax M2 is 2.0x cheaper for input tokens. GLM-4.7 costs $0.60/M input and $2.20/M output via fireworks. MiniMax M2 costs $0.30/M input and $1.20/M output via minimax.

What are the context window sizes for GLM-4.7 and MiniMax M2?

GLM-4.7 supports 203K tokens and MiniMax M2 supports 1.0M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GLM-4.7 and MiniMax M2?

Key differences include context window (203K vs 1.0M), input pricing ($0.60 vs $0.30/M), multimodal support (yes vs no). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-4.7 and MiniMax M2?

GLM-4.7 is developed by Zhipu AI and MiniMax M2 is developed by MiniMax.