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AgenticCodingBench, open-sourced by SwarmOne, is the first LLM inference benchmark tool specifically designed for agentic programming workloads. It can simulate multi-turn context growth scenarios in real coding sessions and measure key metrics such as TTFT, token throughput, and cache hit rate.
Section 01
AgenticCodingBench, open-sourced by SwarmOne, is the first LLM inference benchmark tool specifically designed for agentic programming workloads. It can simulate multi-turn context growth scenarios in real coding sessions and measure key metrics such as TTFT, token throughput, and cache hit rate.
Section 02
When Claude Code opens a file, reads 2000 lines of code, edits three functions, runs tests, and reads error outputs, this involves more than 5 rounds of LLM interactions, with each round's context window ranging from 40K to 83K tokens and accumulating as the session progresses. This scenario is fundamentally different from ordinary chatbot requests.
Existing benchmarks have obvious limitations:
AgenticCodingBench was created to fill this gap; it can benchmark LLM service stacks against real access patterns generated by tools like Claude Code, Cursor, Windsurf, and Copilot.
Section 03
AgenticCodingBench's requests are filled with realistic coding session content, including:
Section 04
The tool can simulate the context growth process in coding sessions:
| Context Configuration | Token Count | Simulated Scenario |
|---|---|---|
| fresh | ~6K | Just opened the project — system prompt + first question |
| short | ~20K | After a few rounds of conversation — read several files and made one edit |
| medium | ~40K | Mid-session — multiple file reads, tool calls, error traces |
| long | ~70K | Deep session — multiple edits, test runs, debugging loops |
| full | ~83K | Long session near context limit — all accumulated content |
Section 05
Each request includes a unique random salt value to ensure that what is measured is true cold-start inference performance, not cache hits. This is crucial for accurately evaluating inference costs.
Section 06
Using the --cache-mode both parameter, the tool first runs a cold-start test and then a warm-start test to show the precise prefix cache acceleration effect. Taking Anthropic as an example, the cost of cached tokens is 1/10 that of uncached ones ($0.30 vs $3.00 per million tokens).
Section 07
Automatically detects reasoning_content in responses, supports reasoning models like DeepSeek R1, o3, and Claude Extended Thinking, and reports the comparison between thinking overhead and visible output latency.
Section 08
AgenticCodingBench provides three complementary testing modes:
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