Evaluating Qwen3-Coder+Instruct Model#
The new Qwen3 models have arrived! These include the coding model Qwen/Qwen3-Coder-480B-A35B-Instruct and the general-purpose model Qwen/Qwen3-235B-A22B-Instruct-2507. Let’s quickly assess the performance of these two models using the EvalScope model evaluation framework.
Installing Dependencies#
First, install the EvalScope model evaluation framework:
pip install 'evalscope[service]' -U
pip install bfcl-eval # Install bfcl evaluation dependencies
Evaluating Qwen3-Coder Model’s Tool Calling Abilities#
To evaluate the model, we need to access its capabilities through an OpenAI-compatible inference service. Here, we use the API interface provided by DashScope. Note that EvalScope also supports inference evaluation using transformers; for details, refer to the documentation.
Below is the BFCL-v3 benchmark test to evaluate the Coder model’s tool calling abilities. Configuration details are as follows:
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', # Use API model service
datasets=['bfcl_v3'],
eval_batch_size=10,
dataset_args={
'bfcl_v3': {
'extra_params':{
# The model refuses to use dots ('.') in function names; set this option to automatically convert dots to underscores during evaluation.
'underscore_to_dot': True,
# Whether the model is a function calling model; if true, function-calling-related configs are enabled, otherwise prompt-based bypass is used.
'is_fc_model': True,
}
}
},
generation_config={
'temperature': 0.7,
'top_p': 0.8,
'top_k': 20,
'repetition_penalty': 1.05,
'max_tokens': 65536, # Set max generation length
'parallel_tool_calls': True, # Enable parallel function calls
},
# limit=50, # Limit number of evaluations for quick testing; remove for full evaluation
ignore_errors=True, # Ignore errors; some test cases may be refused by the model
)
run_task(task_cfg=task_cfg)
Output results:
We can see that the model has strong overall tool calling abilities, but there is still significant room for improvement in multi-turn and parallel tool calls.
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| Model | Dataset | Metric | Subset | Num | Score | Cat.0 |
+==================+===========+=================+=========================+=======+=========+==============+
| qwen3-coder-plus | bfcl_v3 | Accuracy ↑ | live_simple | 257 | 81.7% | AST_LIVE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | Accuracy ↑ | live_multiple | 1039 | 80.9% | AST_LIVE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | Accuracy ↑ | live_parallel | 16 | 37.5% | AST_LIVE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | Accuracy ↑ | live_parallel_multiple | 24 | 41.7% | AST_LIVE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | Accuracy ↑ | simple | 400 | 95.5% | AST_NON_LIVE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | Accuracy ↑ | multiple | 200 | 94.5% | AST_NON_LIVE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | Accuracy ↑ | parallel | 200 | 55% | AST_NON_LIVE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | Accuracy ↑ | parallel_multiple | 200 | 56% | AST_NON_LIVE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | Accuracy ↑ | java | 100 | 64% | AST_NON_LIVE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | Accuracy ↑ | javascript | 50 | 82% | AST_NON_LIVE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | Accuracy ↑ | multi_turn_base | 200 | 43% | MULTI_TURN |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | Accuracy ↑ | multi_turn_miss_func | 200 | 24% | MULTI_TURN |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | Accuracy ↑ | multi_turn_miss_param | 200 | 30.5% | MULTI_TURN |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | Accuracy ↑ | multi_turn_long_context | 200 | 38.5% | MULTI_TURN |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | Accuracy ↑ | irrelevance | 240 | 84.6% | RELEVANCE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | Accuracy ↑ | live_relevance | 17 | 64.7% | RELEVANCE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | Accuracy ↑ | live_irrelevance | 881 | 83.4% | RELEVANCE |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
| qwen3-coder-plus | bfcl_v3 | Accuracy ↑ | OVERALL | 4424 | 72% | - |
+------------------+-----------+-----------------+-------------------------+-------+---------+--------------+
Evaluating Qwen3-Instruct Model’s Knowledge and Reasoning Abilities#
Below, we use the simple_qa and chinese_simpleqa benchmarks to evaluate the model’s knowledge base, with Qwen2.5-72B used to judge answer correctness. We also use AIME25 to evaluate complex reasoning abilities. Configuration details:
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', # Use API model service
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, # Set max generation length
},
# limit=20, # Limit number of evaluations for quick tests; remove for full evaluation
ignore_errors=True, # Ignore errors; some test cases may be refused by the model
stream=True, # Enable streaming output
judge={
'strategy': 'auto',
'models': { # Judge model configuration
'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)
Output results:
It can be seen that the model demonstrates good reasoning abilities and a high level of knowledge.
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| 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 | Chinese Culture | 20 | 0.65 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_correct | Humanities & Social | 20 | 1 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_correct | Engineering & Tech | 20 | 0.8 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_correct | Life, Arts & Culture | 20 | 0.8 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_correct | Society | 20 | 0.9 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_correct | Nature & Science | 20 | 0.8 | default |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| qwen3-235b-a22b-instruct-2507 | chinese_simpleqa | is_correct | OVERALL | 120 | 0.825 | - |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
| ... (similar for is_incorrect and is_not_attempted) ...
| 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 |
+-------------------------------+------------------+------------------+----------------------+-------+---------+---------+
For more supported benchmarks, please see the documentation.
Result Visualization#
EvalScope supports visualizing results so you can see the model’s specific outputs.
Run the following command to launch the Web dashboard:
evalscope service
Select the evaluation report and click “Load” to view the model’s output for each question, as well as the overall accuracy:
Summary#
This guide introduced how to use the EvalScope framework to evaluate the performance of the two new models: Qwen3-Coder and Qwen3-Instruct. Evaluation included:
Qwen3-Coder Model: Evaluated tool calling abilities using the BFCL-v3 benchmark. The model demonstrated strong overall performance, but there is room for improvement in multi-turn and parallel calls.
Qwen3-Instruct Model: Assessed knowledge and reasoning abilities using the simple_qa, chinese_simpleqa, and AIME25 benchmarks, with outstanding results.
For the complete evaluation process and documentation, please refer to the official EvalScope documentation.