OpenCode + Chinese AI Models: Setup Guide for DeepSeek, GLM & Kimi

Q: Why OpenCode for Chinese Models?

OpenCode works with any OpenAI-compatible API . Since AIWave exposes Chinese models through a standard endpoint, the integration is straightforward — set a base URL and API key, then pick your model. Practical advantages of the terminal-first approach: SSH workflows : AI coding on remote servers without forwarding ports or VS Code Remote Speed : No Electron overhead, responses render directly in terminal Scripting : Pipe file contents, redirect output, chain with other CLI tools

OpenCode is a terminal-native AI coding assistant — no IDE required. You interact with it entirely through your shell, making it ideal for SSH sessions, headless servers, and developers who live in the terminal.

A rate comparison only holds for a stated token mix and date. Recalculate it against your workload before changing traffic.

Why OpenCode for Chinese Models?

OpenCode works with any OpenAI-compatible API. Since AIWave exposes Chinese models through a standard endpoint, the integration is straightforward — set a base URL and API key, then pick your model.

Practical advantages of the terminal-first approach:

SSH workflows: AI coding on remote servers without forwarding ports or VS Code Remote

Speed: No Electron overhead, responses render directly in terminal

Scripting: Pipe file contents, redirect output, chain with other CLI tools

Installation


go install github.com/opencode-ai/opencode@latest

Or without Go:


curl -fsSL https://opencode.ai/install | bash

Verify: opencode --version

Configuration

Create ~/.config/opencode/config.json:


{
  "provider": "openai",
  "base_url": "https://aiwave.live/v1",
  "api_key": "***",
  "model": "deepseek-chat"
}

Switch models with flags:


opencode --model deepseek-chat   # Fast, cheap
opencode --model glm-5            # Stronger reasoning
opencode --model kimi-k3           # Good balance

Or define multiple in config:


{
  "provider": "openai",
  "base_url": "https://aiwave.live/v1",
  "api_key": "***",
  "models": {
    "fast": "deepseek-chat",
    "reasoning": "glm-5",
    "balanced": "kimi-k3"
  },
  "default_model": "fast"
}

Then use opencode --model reasoning or opencode --model balanced.

Model Comparison

	DeepSeek V4 Flash	GLM-5	Kimi K3
Input (USD/1M tokens)	$0.14	$0.20	$0.30
Output (USD/1M tokens)	$0.28	$0.60	$0.90
Context window	1M	128K	128K
Coding strength	Excellent	Very good	Good
Terminal latency	Sub-second	1-2s	1-3s

*Prices from AIWave pricing.*

Cost Estimates

Terminal sessions use shorter contexts than IDE workflows. Assumptions: 15 prompts per session, 1K input + 500 output tokens per prompt.

Model	Per Query	Per Session (15 prompts)	Monthly (20 sessions)
DeepSeek V4 Flash	$0.0016	$0.0239	$0.48
GLM-5	$0.00050	$0.0075	$0.15
Kimi K3	$0.00075	$0.0113	$0.23
GPT-4o (reference)	$0.00750	$0.1125	$2.25

*Formula: (1000/1M x input_price) + (500/1M x output_price) per query, x15 prompts, x20 sessions.*

A month of daily terminal coding with DeepSeek V4 Flash costs under 10 cents. GPT-4o costs 27x more per session. A capped test budget covers about 238 DeepSeek sessions — roughly a year of daily use.

Practical Usage


# Code generation
opencode "Add input validation to parse_config in config.py"

# Analyze logs
cat error.log | opencode "What caused this error and how to fix it?"

# Code review
git diff main | opencode "Review this diff for bugs"

Troubleshooting

Connection refused: Verify base URL ends with /v1 (no trailing slash). Test with curl first:


curl https://aiwave.live/v1/chat/completions \
  -H "Authorization: Bearer ***" \
  -H "Content-Type: application/json" \
  -d '{"model":"deepseek-chat","messages":[{"role":"user","content":"hello"}]}'

Model not found: Names must match exactly — deepseek-chat (not deepseek-v4-flash), glm-5 (not glm-5.1). Check AIWave models page.

Slow on large files: Truncate to relevant sections before piping. The 1M context window handles a lot, but sending 500K tokens of irrelevant log data wastes time and money.

Top-ups start at $5. Estimate the test budget from the live rate card.