Testing Three DeepSeek Alternatives Overnight After Its Price Hike Reveals Surprising Results

First Alternative: Tongyi Qianwen

The author transferred a log‑analysis task (parsing a 300‑line Nginx access log to extract top‑10 IPs, 4xx/5xx paths, and suspicious User‑Agent patterns) from DeepSeek to Tongyi Qianwen. Tongyi returned the JSON result in 14 seconds, compared with DeepSeek’s roughly 20‑plus seconds, and the output was completely correct.

Prompt: "Extract from the Nginx access.log snippet: 1. Top‑10 IPs by request count 2. Paths with 4xx/5xx status codes 3. User‑Agent features that look like crawlers. Output in JSON."

Tongyi also added a new field called risk_score (0‑100) for each IP, which DeepSeek never produced. The scores seemed reasonable, e.g., high‑frequency IPs with many 404 responses received scores above 85, though the scoring logic is undocumented.

Second Alternative: Zhipu GLM‑4‑Flash

The author evaluated Zhipu’s GLM‑4‑Flash on a customer‑service test set of 60 messages covering returns, logistics queries, and product inquiries, each with a reference answer. GLM answered 53 correctly, yielding an 88 % accuracy, whereas DeepSeek achieved 56 correct answers (93 %). The 5 % gap translates to one incorrect reply out of ten, which can be unacceptable in a support scenario.

Beyond raw accuracy, GLM tended to “add drama” to responses. For a logistics query, instead of returning the tracking status, GLM replied with a friendly but irrelevant sentence: “Your package is flying fast, please be patient,” without actually checking the order.

Third Alternative: Local Deployment

The author set up an open‑source 14‑billion‑parameter model on an idle RTX 3090 using Ollama. Downloading the model took about 40 minutes, with another half hour spent configuring the environment.

Running the same log‑analysis task locally took nearly two minutes, compared with the sub‑20‑second runtimes of DeepSeek and Tongyi. However, the local approach incurs essentially zero monetary cost (ignoring electricity).

Overall Reflections

Each solution has distinct trade‑offs: Tongyi is fastest and adds useful risk scores but lacks documentation on the scoring algorithm; Zhipu is cheaper but injects unnecessary conversational fluff, making it unsuitable for precise support replies; the local model is cost‑free but slower and requires dedicated hardware.

The price hike of DeepSeek prompted the author to avoid a single‑vendor lock‑in. The final strategy is to use Tongyi for log‑analysis (speed), keep DeepSeek for customer‑service (higher accuracy), and retain the local model as a fallback when network issues arise.

Cost estimates suggest DeepSeek’s upcoming increase could be 30‑50 %, which may be tolerable for low‑volume users but significant for heavy users, reinforcing the need for diversified API usage.