The cheapest AI API is not always the one with the lowest input price. Output tokens are usually where the bill hurts, and coding agents produce a lot of output.
So the better question is: which API is cheap enough to use all day without making the agent useless?
Quick ranking
| Model Family | Typical Strength | Watch For |
|---|---|---|
| DeepSeek V3.2 | Very cheap output, good coding value | Reliability can be uneven |
| Gemini Flash | Long context, free-tier path, good speed | Weaker reasoning than Pro |
| MiniMax M2.5 | Cheap daily agent work | Less proven than Claude/OpenAI |
| Grok 4.1 Fast | Huge context for low cost | Not the best hard-coding model |
| GPT mini models | Reliable general API behavior | Output can cost more than bargain models |
| Ollama local models | No token bill | Hardware, speed, and quality limits |
If you are building a coding agent, I would not optimize for the absolute cheapest model on a spreadsheet. I would optimize for the cheapest model that completes boring work without constant supervision.
Why output price matters
A chat app mostly reads short prompts and writes short answers. A coding agent is different. It reads files, writes patches, explains failures, rewrites tests, and sometimes dumps a lot of code.
That means output pricing matters. A model with cheap input and expensive output can look good until the first big refactor.
For high-output jobs, DeepSeek V3.2 and Grok 4.1 Fast are interesting because output is relatively cheap. For long-context jobs, Gemini Flash and Grok 4.1 Fast are usually better than tiny models with cramped context windows.
Best cheap APIs by use case
Cheapest everyday agent default
Start with MiniMax M2.5, DeepSeek V3.2, or Gemini Flash. They are cheap enough for daily use and capable enough for routine coding work.
Cheapest huge context
Grok 4.1 Fast and Gemini Flash are the standouts. If your agent needs to read a lot before answering, cheap context matters more than benchmark flexing.
Cheapest local setup
Ollama is the cheapest if you already own the hardware. Gemma 4 and Qwen3.5 are good first choices. The tradeoff is speed. Local agents can feel great for small edits and painful for long tool loops.
Cheapest serious fallback
Do not use your fallback for everything. Claude Sonnet, Gemini Pro, and GPT-5.4 are better saved for tasks where the cheap default already failed once.
A practical routing setup
Use this pattern:
- Cheap model for all default traffic
- Long-context model for repo-scale reads
- Premium model only when the cheap model gets stuck
- Local model for private or low-risk work
In OpenClaw, that might mean:
- MiniMax M2.5 as default
- Gemini Flash for long context
- Claude Sonnet for hard coding
- Gemma 4 through Ollama for private local tasks
This is less glamorous than chasing the top benchmark model. It also saves real money.
Compare total job cost
When pricing APIs, estimate a whole job:
total cost =
input_tokens / 1,000,000 * input_price
+
output_tokens / 1,000,000 * output_price
Then run that across a normal day, not a single prompt. For agents, the difference between a cheap default and a premium default can be 10x to 50x over a month.
Where Haimaker fits
If you are tired of opening five provider accounts just to compare prices, route through Haimaker. You get one API key and can switch between cheap, long-context, and premium models without rewriting your app.
That matters because the cheapest model changes. Your architecture should make switching boring.