Qwen 3.7 Plus
Qwen route with 90 overall score. Best for Structured output, Data extraction, Tool calling.
Chinese LLM benchmark
DeepSeek, Qwen, Kimi, GLM, MiniMax and MiMo compared
This English summary turns GPTokens BenchLocal results into a practical shortlist for API buyers. Use it as a screening signal before running your own prompts.
First-tier routes
Top benchmark signals
GLM 5.2
GLM route with 90 overall score. Best for Structured output, Data extraction, Tool calling.
MiMo v2.5 Pro
MiMo route with 90 overall score. Best for Tool calling, Structured output, Reasoning.
DeepSeek v4 Pro
DeepSeek route with 89 overall score. Best for Tool calling, Instruction following, Structured output.
Qwen 3.6 Plus
Qwen route with 89 overall score. Best for Instruction following, Structured output, Data extraction.
BenchLocal weighted score
Full benchmark table
| Model | Provider | Score | Best for |
|---|---|---|---|
Qwen 3.7 Plusqwen3.7-plus | Qwen | 90 Excellent | Structured output, Data extraction, Tool calling |
GLM 5.2glm-5.2 | GLM | 90 Excellent | Structured output, Data extraction, Tool calling |
MiMo v2.5 Promimo-v2.5-pro | MiMo | 90 Excellent | Tool calling, Structured output, Reasoning |
DeepSeek v4 Prodeepseek-v4-pro | DeepSeek | 89 Strong | Tool calling, Instruction following, Structured output |
Qwen 3.6 Plusqwen3.6-plus | Qwen | 89 Strong | Instruction following, Structured output, Data extraction |
Kimi K2.6kimi-k2.6 | Kimi | 88 Strong | Coding support, Instruction following, Structured output |
DeepSeek v4 Flashdeepseek-v4-flash | DeepSeek | 87 Strong | Fallback routing, Cost testing, Instruction following |
GLM 5.1glm-5.1 | GLM | 87 Strong | Tool calling, Structured output, Instruction following |
Kimi K2.5kimi-k2.5 | Kimi | 87 Strong | Fallback routing, Instruction following, Reasoning checks |
GLM 5glm-5 | GLM | 86 Strong | GLM comparison, Structured output testing, Fallback routing |
MiMo v2.5mimo-v2.5 | MiMo | 86 Strong | MiMo comparison, Cost testing, Structured output |
Qwen 3.7 Maxqwen3.7-max | Qwen | 84 Strong | Structured output, Qwen variant testing, Data extraction |
MiniMax M2.7minimax-m2.7 | MiniMax | 83 Strong | MiniMax comparison, Structured output, Fallback routing |
MiniMax M2.5minimax-m2.5 | MiniMax | 81 Strong | Fallback routing, MiniMax comparison, Structured output tests |
MiniMax M3minimax-m3 | MiniMax | 76 Usable | Targeted comparison, Fallback experiments, Non-critical workloads |
Production caveat
How to interpret the ranking
The benchmark uses repeatable local tasks for tool calling, instruction following, structured output, CLI behavior, Hermes-style agent execution, math reasoning, and data extraction. It is designed to reveal engineering-agent risk, not just chat quality.
Public benchmarks should shortlist models, not replace production testing. Before scaling, test latency, fallback behavior, refusal patterns, tool-call reliability, and cost per completed task.
Strong first tests
- Qwen 3.7 Plus for structured output and extraction.
- GLM 5.2 for first-tier structured output and tool calling.
- DeepSeek v4 Pro for tool calling and agent-style workflows.
- MiMo v2.5 Pro for first-tier portfolio comparison.
FAQ
Questions developers ask before buying Chinese LLM tokens
Is GPTokens run by a model provider?
No. GPTokens is an independent OpenAI-compatible gateway. It issues GPTokens API keys and routes requests to supported Chinese LLM providers through one account.
Can I use an OpenAI SDK?
Yes. Most integrations only need the GPTokens base URL, a GPTokens API key, and a supported model name from the live model list.
Who is this for?
GPTokens is built for developers and teams in the US, Europe, and other global markets that want to test Chinese LLM APIs without managing separate provider accounts, payment methods, and dashboards.