โ† Back to Model Hub/SIDE-BY-SIDE REVIEW
SHARE THIS:

GLM 4.7 vs MiniMax M2.7

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for GLM 4.7 and MiniMax M2.7.

Zhipu AI

GLM 4.7

GLM-4.7 is Z.aiโ€™s latest flagship model, featuring upgrades in two key areas: enhanced programming capabilities and more stable multi-step reasoning/execution. It demonstrates significant improvements in executing complex agent tasks while...

View Full Specs
MiniMax

MiniMax M2.7

MiniMax-M2.7 is a next-generation large language model designed for autonomous, real-world productivity and continuous improvement. Built to actively participate in its own evolution, M2.7 integrates advanced agentic capabilities through multi-agent...

View Full Specs

Technical Specifications

SpecificationGLM 4.7MiniMax M2.7
ProviderZhipu AIMiniMax
Context Window202,752 tokens204,800 tokens
Agent SuitabilityN/AN/A
Time to First Token (TTFT)N/AN/A
Deployment Modelmanaged apimanaged api
Production Stabilitystablestable
API AvailableYesYes
Released Date2025-12-222026-03-18

API Pricing Comparison

Input Price per Million Tokens

GLM 4.7

$0.40

MiniMax M2.7

$0.18

Output Price per Million Tokens

GLM 4.7

$1.75

MiniMax M2.7

$0.72

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
8150.0%vs8250.0%
GLM 4.7
MiniMax M2.7
HumanEvalPython coding & logic synthesis
8200.0%vs8000.0%
GLM 4.7
MiniMax M2.7
MATHComplex mathematical problem solving
5150.0%vs5400.0%
GLM 4.7
MiniMax M2.7
GPQAGraduate-level expert reasoning
3800.0%vs3900.0%
GLM 4.7
MiniMax M2.7
HellaSwagCommonsense reasoning and inference
8350.0%vs8400.0%
GLM 4.7
MiniMax M2.7
MT-BenchMulti-turn conversation flow quality
865.0%vs870.0%
GLM 4.7
MiniMax M2.7

GLM 4.7 Quirks & Gotchas

No developer gotchas reported.

MiniMax M2.7 Quirks & Gotchas

No developer gotchas reported.