PinchBench runner now defaults to openrouter/xiaomi/mimo-v2.5-pro instead of deepseek/deepseek-chat. Adds --direct-mimo flag for routing through Xiaomi's API directly (bypasses OpenRouter), with tp-/sk- key type detection and endpoint mismatch warnings. Harbor adapter gains --provider CLI flag for MiMo provider routing. Known issues documented in docs/MIMO_BENCHMARK_ISSUES.md: - PinchBench model validation requires OpenRouter prefix - OPENROUTER_API_KEY needed even for some direct-provider paths - Token Plan vs pay-as-you-go key/endpoint mismatch - PinchBench runs through OpenClaw, not CodeWhale
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Benchmarks
CodeWhale integrates with three external benchmarks to measure real-world coding-agent performance. Each benchmark tests a different surface:
| Benchmark | What it tests | Harness | Output format |
|---|---|---|---|
| SWE-bench | Patch generation from GitHub issues | CodeWhale built-in (codewhale swebench) |
all_preds.jsonl |
| Terminal-Bench | End-to-end terminal tasks (compile, deploy, configure) | Harbor framework adapter | Harbor result JSON |
| PinchBench | Real-world agent tasks (calendar, email, coding, research) | Standalone runner via OpenClaw-compatible adapter | PinchBench result JSON |
All three require Docker. SWE-bench and Terminal-Bench also need the official evaluation harness installed separately.
Prerequisites
# Docker (all benchmarks)
docker --version
# Python 3.10+ with uv (Terminal-Bench, PinchBench, SWE-bench eval)
python3 --version
uv --version
# CodeWhale v0.8.53+
codewhale --version
# API key
export DEEPSEEK_API_KEY="sk-..."
SWE-bench
CodeWhale has built-in SWE-bench support via codewhale swebench run and
codewhale swebench export. See docs/SWEBENCH.md for the
single-instance workflow.
Batch run
# Run all instances from a dataset split
./scripts/benchmarks/run-swebench.sh \
--dataset princeton-nlp/SWE-bench_Lite \
--split test \
--predictions-path ./results/swebench_preds.jsonl
# Run a single instance
./scripts/benchmarks/run-swebench.sh \
--instance-id django__django-12345 \
--issue-file ./issue.md \
--predictions-path ./results/swebench_preds.jsonl
Evaluate
python -m swebench.harness.run_evaluation \
--dataset_name princeton-nlp/SWE-bench_Lite \
--predictions_path ./results/swebench_preds.jsonl \
--max_workers 1 \
--run_id codewhale-v0.8.53
Terminal-Bench (via Harbor)
Terminal-Bench tests agents on real terminal tasks — compiling, deploying, configuring servers, training models. The Harbor framework is the official harness.
CodeWhale plugs in via a Harbor adapter (scripts/benchmarks/harbor/codewhale_agent.py).
Setup
pip install harbor
Run
# Via the convenience script
./scripts/benchmarks/run-terminal-bench.sh \
--dataset terminal-bench@2.0 \
--model deepseek/deepseek-chat \
--n-concurrent 4
# Or directly with harbor
harbor run \
--dataset terminal-bench@2.0 \
--agent codewhale \
--model deepseek/deepseek-chat \
--n-concurrent 4
Custom agent path
If the adapter is not installed system-wide, point Harbor at it:
harbor run \
--dataset terminal-bench@2.0 \
--agent scripts.benchmarks.harbor.codewhale_agent:CodeWhaleAgent \
--model deepseek/deepseek-chat
PinchBench
PinchBench measures agent performance on real-world tasks — scheduling, email triage, code generation, research, file management. It uses OpenClaw as the agent runtime.
Setup
./scripts/benchmarks/run-pinchbench.sh --install
Run (MiMo v2.5 Pro — default)
# MiMo v2.5 Pro via OpenRouter (default)
./scripts/benchmarks/run-pinchbench.sh
# MiMo v2.5 Pro via direct Xiaomi API
./scripts/benchmarks/run-pinchbench.sh --direct-mimo
# Specific tasks
./scripts/benchmarks/run-pinchbench.sh --suite task_calendar,task_stock
Run (other models)
./scripts/benchmarks/run-pinchbench.sh --model openrouter/deepseek/deepseek-v4-pro
MiMo v2.5 notes
PinchBench routes through OpenRouter by default. MiMo models are available as
openrouter/xiaomi/mimo-v2.5-pro (Pro) and openrouter/xiaomi/mimo-v2.5
(Omni). For direct Xiaomi API access, use --direct-mimo with
XIAOMI_MIMO_API_KEY set.
See scripts/benchmarks/run-pinchbench.sh --help for full option reference.
Reproducibility checklist
When publishing benchmark results, record:
- CodeWhale version:
codewhale --version - Git commit:
git rev-parse HEAD - Model and provider (e.g.
deepseek/deepseek-chat) - Benchmark dataset and version
- Docker platform (
linux/amd64vslinux/arm64) - Worker concurrency
- Timestamp (UTC)
- Full result file (
all_preds.jsonl, Harbor result dir, or PinchBench results JSON)
References
- SWE-bench: https://github.com/SWE-bench/SWE-bench
- Terminal-Bench: https://github.com/laude-institute/terminal-bench / https://www.tbench.ai
- Harbor: https://github.com/harbor-framework/harbor / https://harborframework.com
- PinchBench: https://github.com/pinchbench/skill / https://pinchbench.com