Qwen3-Coder+Instruct 模型评测最佳实践#
Qwen3的新模型来啦，分别是代码模型Qwen/Qwen3-Coder-480B-A35B-Instruct和 通用模型 Qwen/Qwen3-235B-A22B-Instruct-2507，下面让我们使用EvalScope模型评测框架，来快速测一下这两个模型的性能吧。
安装依赖#
首先，安装EvalScope模型评估框架：
pip install 'evalscope[app]' -U
pip install bfcl-eval # 安装 bfcl 评测依赖
评测Qwen3-Coder模型工具调用能力#
我们需要通过OpenAI API兼容的推理服务接入模型能力，以进行评测，这里我们使用DashScope提供的API接口。值得注意的是，EvalScope也支持使用transformers进行模型推理评测，详细信息可参考文档。
下面是使用BFCL-v3基准测试，来评测Coder模型的工具调用能力，具体配置如下：
import os
from evalscope import TaskConfig, run_task
task_cfg = TaskConfig(
model='qwen3-coder-plus',
api_url='https://dashscope.aliyuncs.com/compatible-mode/v1',
api_key=os.getenv('DASHSCOPE_API_KEY'),
eval_type='openai_api', # 使用API模型服务
datasets=['bfcl_v3'],
eval_batch_size=10,
dataset_args={
'bfcl_v3': {
'extra_params':{
# 模型在函数名称中拒绝使用点号（`.`）；设置此项，以便在评估期间自动将点号转换为下划线。
'underscore_to_dot': True,
# 模型是否为函数调用模型（Function Calling Model），如果是则会启用函数调用相关的配置；否则会使用prompt绕过函数调用。
'is_fc_model': True,
}
}
},
generation_config={
'temperature': 0.7,
'top_p': 0.8,
'top_k': 20,
'repetition_penalty': 1.05,
'max_tokens': 65536, # 设置最大生成长度
'parallel_tool_calls': True, # 启用并行函数调用
},
# limit=50, # 限制评测数量，便于快速测试，正式评测时建议去掉此项
ignore_errors=True, # 忽略错误，可能会被模型拒绝的测试用例
)
run_task(task_cfg=task_cfg)
输出结果如下：
可以看到模型整体的工具调用能力较强，但在多轮工具调用以及并行工具调用两方面仍有较大提升空间。
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| Model | Dataset | Metric | Subset | Num | Score | Cat.0 |
+==================+===========+=================+=========================+=======+=========+==============+
| qwen3-coder-plus | bfcl_v3 | AverageAccuracy | live_simple | 257 | 0.8171 | AST_LIVE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | AverageAccuracy | live_multiple | 1039 | 0.8085 | AST_LIVE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | AverageAccuracy | live_parallel | 16 | 0.375 | AST_LIVE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | AverageAccuracy | live_parallel_multiple | 24 | 0.4167 | AST_LIVE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | AverageAccuracy | simple | 400 | 0.955 | AST_NON_LIVE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | AverageAccuracy | multiple | 200 | 0.945 | AST_NON_LIVE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | AverageAccuracy | parallel | 200 | 0.55 | AST_NON_LIVE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | AverageAccuracy | parallel_multiple | 200 | 0.56 | AST_NON_LIVE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | AverageAccuracy | java | 100 | 0.64 | AST_NON_LIVE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | AverageAccuracy | javascript | 50 | 0.82 | AST_NON_LIVE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | AverageAccuracy | multi_turn_base | 200 | 0.43 | MULTI_TURN |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | AverageAccuracy | multi_turn_miss_func | 200 | 0.24 | MULTI_TURN |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | AverageAccuracy | multi_turn_miss_param | 200 | 0.305 | MULTI_TURN |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | AverageAccuracy | multi_turn_long_context | 200 | 0.385 | MULTI_TURN |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | AverageAccuracy | irrelevance | 240 | 0.8458 | RELEVANCE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | AverageAccuracy | live_relevance | 17 | 0.6471 | RELEVANCE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | AverageAccuracy | live_irrelevance | 881 | 0.8343 | RELEVANCE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | AverageAccuracy | OVERALL | 4424 | 0.7199 | - |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
评测Qwen3-Instruct模型知识和推理能力#
下面是使用simple_qa和chinese_simpleqa两个基准测试，评测模型的知识水平，同时使用Qwen2.5-72B模型评价答案是否正确；使用AIME25来测试模型的复杂推理能力。具体配置如下：
import os
from evalscope import TaskConfig, run_task
task_cfg = TaskConfig(
model='qwen3-235b-a22b-instruct-2507',
api_url='https://dashscope.aliyuncs.com/compatible-mode/v1',
api_key=os.getenv('DASHSCOPE_API_KEY'),
eval_type='openai_api', # 使用API模型服务
datasets=['simple_qa', 'chinese_simpleqa', 'aime25'],
eval_batch_size=10,
generation_config={
'temperature': 0.7,
'top_p': 0.8,
'top_k': 20,
'max_tokens': 16384, # 设置最大生成长度
},
# limit=20, # 限制评测数量，便于快速测试，正式评测时建议去掉此项
ignore_errors=True, # 忽略错误，可能会被模型拒绝的测试用例
stream=True, # 启用流式输出
judge_model_args={ # 配置Judge模型参数
'model_id': 'qwen2.5-72b-instruct',
'api_url': 'https://dashscope.aliyuncs.com/compatible-mode/v1',
'api_key': os.getenv('DASHSCOPE_API_KEY'),
'generation_config': {
'temperature': 0.0,
'max_tokens': 4096
}
},
)
run_task(task_cfg=task_cfg)
输出结果如下：
可以看出模型展现了良好的推理能力以及较高的知识水平。
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| Model | Dataset | Metric | Subset | Num | Score | Cat.0 |
+===============================+==================+==================+======================+=======+=========+=========+
| qwen3-235b-a22b-instruct-2507 | aime25 | AveragePass@1 | AIME2025-I | 15 | 0.6667 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | aime25 | AveragePass@1 | AIME2025-II | 15 | 0.6667 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | aime25 | AveragePass@1 | OVERALL | 30 | 0.6667 | - |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_correct | 中华文化 | 20 | 0.65 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_correct | 人文与社会科学 | 20 | 1 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_correct | 工程、技术与应用科学 | 20 | 0.8 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_correct | 生活、艺术与文化 | 20 | 0.8 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_correct | 社会 | 20 | 0.9 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_correct | 自然与自然科学 | 20 | 0.8 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_correct | OVERALL | 120 | 0.825 | - |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_incorrect | 中华文化 | 20 | 0.35 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_incorrect | 人文与社会科学 | 20 | 0 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_incorrect | 工程、技术与应用科学 | 20 | 0.2 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_incorrect | 生活、艺术与文化 | 20 | 0.2 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_incorrect | 社会 | 20 | 0.1 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_incorrect | 自然与自然科学 | 20 | 0.2 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_incorrect | OVERALL | 120 | 0.175 | - |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_not_attempted | 中华文化 | 20 | 0 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_not_attempted | 人文与社会科学 | 20 | 0 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_not_attempted | 工程、技术与应用科学 | 20 | 0 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_not_attempted | 生活、艺术与文化 | 20 | 0 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_not_attempted | 社会 | 20 | 0 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_not_attempted | 自然与自然科学 | 20 | 0 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_not_attempted | OVERALL | 120 | 0 | - |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | simple_qa | is_correct | default | 20 | 0.6 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | simple_qa | is_incorrect | default | 20 | 0.35 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | simple_qa | is_not_attempted | default | 20 | 0.05 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
更多支持的Benchmark请查看文档。
结果可视化#
EvalScope支持可视化结果，可以查看模型具体的输出。
运行以下命令，可以启动基于Gradio的可视化界面：
evalscope app
选择评测报告，点击加载，即可看到模型在每个问题上的输出结果，以及整体答题正确率：
总结#
本文介绍了使用EvalScope框架评测Qwen3-Coder和Qwen3-Instruct两个新模型性能的方法与结果。评测内容包括：
Qwen3-Coder模型：使用BFCL-v3基准测试其工具调用能力，结果显示其整体表现较强，但在多轮和并行调用方面仍有提升空间。
Qwen3-Instruct模型：通过simple_qa、chinese_simpleqa和AIME25测试其知识与推理能力，结果表现出色。
完整评测流程与文档详见EvalScope官方文档。