The article tests whether DeepSeek Harness maintains high prompt caching rates when using alternative AI models, finding that GLM and Kimi achieve 97-99% cache reuse, while Opus shows no cache activity and GPT test failed.
TL;DR: Yes, at least with GLM and Kimi. In real DeepSeek Harness sessions, GLM reached 97% cache reuse inside a tool loop and 99.6% on the next turn. Kimi reached 99% on both. Opus showed no cache activity during this test, and the GPT test couldn't be completed because the third-party route I used didn't handle the current DSH request correctly. I wanted to test whether DeepSeek Harness can keep its high cache hit rate when DeepSeek is replaced with another model. DSH sends a large repeated prefix on each request: the system prompt, 25 tool definitions, conversation history, and previous tool results. New messages are appended to the end. If the upstream model/provider supports prefix caching, most of that context should be reusable. Before testing DSH, I ran 300+ direct API requests through the same third-party gateway, GMI Cloud. GLM had 16/21 prefix hits. Kimi had 13/21, around 62%. I also checked 80 requests against the billing export and every one matched the reported token usage and published prices exactly, so I used cached_tokens as the main signal for the DSH test. Then I ran normal DSH web sessions through a transparent logging proxy. The agent called tools, read a long file, and continued the same conversation. The proxy only recorded the actual requests and usage. GLM worked extremely well. After the first tool call, 7680 of 7924 input tokens were cached: 97%. On the next conversation turn, 18304 of 18383 tokens were cached: 99.6%. So once the conversation was running, almost the entire existing system prompt, tool definitions, and history were being reused. This also matched the direct API results, where GLM had already been the most reliable model for prefix caching. Kimi was the bigger change. Its direct API prefix test was only 13/21, around 62%, and some requests became much faster without reporting cached tokens. Inside DSH, the tool-loop request reported 7424/7498 cached tokens, 99%. The next turn reported 17152/17325, also 99%. Even the first main request already had 5632 of 7498 tokens cached, despite me not manually warming that DSH system prompt + tools prefix with Kimi beforehand. I don't have enough data to say why that happened, but the actual result is clear: Kimi worked normally in DSH and subsequent requests stayed around 99% cache reuse. That was the biggest difference between the direct API test and the real DSH test: Kimi went from roughly 62% prefix hits to roughly 99% in the actual harness workload. Opus could run in DSH, but the second request had more than 25k tokens of context and still reported zero cached tokens. The upstream Opus route also showed no cache activity in the direct tests that day, so I can't draw a broader conclusion from it. I couldn't complete the GPT test. The third-party GPT route I used didn't handle the current DSH request/configuration correctly, so there's no useful GPT cache result from this test. One other thing I verified: the cache percentage shown in the DSH web UI matched the cached_tokens recorded by the proxy. So when the provider reports cache usage correctly, DSH's own cache display is enough to monitor it. The useful result here is that DSH's append-style request pattern also works with other models. GLM and Kimi both handled real tool calls and multi-turn history, and both reached roughly 97–99.6% prefix cache reuse.
Composio tested DeepSeek v4 flash, GLM 5.2, and Kimi K3 on hard agentic tasks, finding DeepSeek the fastest and cheapest with roughly the same success rate as the others, while frontier models still lead slightly.
A comparison of DeepSeek 0813 'Pro' against GLM 5.2 and Kimi K3, likely covering benchmark performance and capability differences between these AI models.
Maka's Harness project improved the self-check mechanism, enabling DeepSeek Flash V4 to achieve evaluation results close to GLM-5.2 on the terminal-bench sample set, completing 10 programming agent tasks with only 4 RMB and a 97.5% cache hit rate.
This article tests four open-source Chinese AI models — Zhipu GLM 5.1, Moonshot Kimi K2.6, Stepfun MIMO 2.5 Pro, and DeepSeek V4 Pro — on programming tasks. It finds that GLM leads overall in most tasks but not absolutely; each model has its own strengths and weaknesses.
A discussion questioning whether older AI models like GLM 5.2 and Kimi 2.7 remain relevant for coding now that newer models such as Kimi K3, Qwen 3.8 Max, and DeepSeek V4 Pro are arriving.