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MiniMax M2.7 vs Qwen2.5 Coder 32B Instruct

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 MiniMax M2.7 and Qwen2.5 Coder 32B Instruct.

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...

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Alibaba

Qwen2.5 Coder 32B Instruct

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in **code generation**, **code reasoning**...

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Technical Specifications

SpecificationMiniMax M2.7Qwen2.5 Coder 32B Instruct
ProviderMiniMaxAlibaba
Context Window204,800 tokens128,000 tokens
Agent SuitabilityN/AN/A
Time to First Token (TTFT)N/AN/A
Deployment Modelmanaged apiself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-03-182024-11-11

API Pricing Comparison

Input Price per Million Tokens

MiniMax M2.7

$0.18

Qwen2.5 Coder 32B Instruct

$0.66

Output Price per Million Tokens

MiniMax M2.7

$0.72

Qwen2.5 Coder 32B Instruct

$1.00

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
8250.0%vs8120.0%
MiniMax M2.7
Qwen2.5 Coder 32B Instruct
HumanEvalPython coding & logic synthesis
8000.0%vs9150.0%
MiniMax M2.7
Qwen2.5 Coder 32B Instruct
MATHComplex mathematical problem solving
5400.0%vs6800.0%
MiniMax M2.7
Qwen2.5 Coder 32B Instruct
GPQAGraduate-level expert reasoning
3900.0%vs4050.0%
MiniMax M2.7
Qwen2.5 Coder 32B Instruct
HellaSwagCommonsense reasoning and inference
8400.0%vs8400.0%
MiniMax M2.7
Qwen2.5 Coder 32B Instruct
MT-BenchMulti-turn conversation flow quality
870.0%vs885.0%
MiniMax M2.7
Qwen2.5 Coder 32B Instruct

MiniMax M2.7 Quirks & Gotchas

No developer gotchas reported.

Qwen2.5 Coder 32B Instruct Quirks & Gotchas

No developer gotchas reported.