Conclusion
- Best cost-first coding route: DeepSeek, with strict max_tokens and retry limits.
- Best China-friendly coding route: Qwen through DashScope compatible mode.
- Best low-latency open-model route: Groq or SiliconFlow depending on region.
- Best production pattern: cheap primary model plus a stronger fallback for stuck agent loops.
What to do next
- Create a 10-task coding benchmark from your real work: bug fix, refactor, test generation, docs, and small feature edits.
- Run the same tasks on DeepSeek, Qwen, and one premium fallback; record accepted patches, retries, latency, and total output tokens.
- Keep base_url, model, and api_key configurable so Cursor, OpenClaw, Claude Code-style tools, and custom agents can swap providers.
- Cap max output tokens, stop runaway tool loops, and route easy lint/test explanations to the cheaper model.
- Promote a provider only when cost per accepted patch beats the current stack for a full week.
Recommended paths
| Provider | Free / credits | Best for |
|---|---|---|
| DeepSeek | $5 signup / current console credit | Cost-first coding agents and repo tasks |
| Qwen | 70M signup tokens | China-friendly coding, long context, multilingual repos |
| Zhipu GLM | 5M signup tokens | Low-cost China-direct coding experiments |
| Groq | Free developer limits vary | Fast open-model completions and quick fixes |
| OpenLLMAPI | Signup credit varies | One OpenAI-compatible key with cheap/premium fallback |
Global developer checklist
- Confirm whether signup, billing, and API keys work from your country before writing production code.
- Prefer OpenAI-compatible endpoints when you may need to switch models, regions, or providers later.
- Test free credits with a real smoke prompt and record latency, error shape, streaming behavior, and quota burn.
- Keep at least one fallback route for provider outages, model deprecations, and regional access changes.
Production handoff
Need cheap coding loops with premium fallback?
Use one OpenAI-compatible endpoint to route routine coding tasks to DeepSeek/Qwen and fall back to GPT, Claude, or Gemini when the agent gets stuck.
FAQ
Which metric matters most for coding API cost?
Cost per accepted patch. A model with cheap tokens can lose if it causes retries, broken tests, long explanations, or manual rewrites.
Is Qwen cheaper than DeepSeek for coding?
It depends on current pricing, output length, and task success rate. Qwen is often a strong China/coding route; DeepSeek is usually the first cost benchmark.
Should I self-host a coding model?
Only if you have high sustained GPU utilization or privacy requirements. For sporadic coding agents, hosted APIs are usually cheaper and easier.
How do I avoid agent cost explosions?
Set max iterations, max output tokens, repository diff limits, budget alerts, and fallback rules for repeated failures.