The cheapest coding model is usually the one with the lowest blended cost for your actual prompt mix, not the one with the smallest headline input price. If your workflow is input-heavy, a cheap prompt model can win. If it’s output-heavy or reasoning-heavy, the bill can flip fast.
Why “cheapest” depends on your token mix
LLM pricing is published per 1M input tokens and per 1M output tokens, so the same model can look cheap in one workflow and expensive in another. Coding agents tend to be messy: they ingest repo context, produce patches, then often iterate. That means input tokens, output tokens, and sometimes cached or reasoning tokens all matter.
For a quick sanity check, use a 3:1 input:output blend, which is the same style used by the AI Cost Index. It is not a law of nature, just a useful baseline for comparing models without pretending every workflow is identical.
Rule of thumb: if you only compare input prices, you will often pick the wrong model for coding. Output cost and total token volume decide the real bill.
Worked example: the same task on four models
Say a coding task uses 900,000 input tokens and 300,000 output tokens. That is a 3:1 mix, and it is a nice round example because the math is easy to audit.
Cost formula: (input_tokens / 1,000,000) * input_price + (output_tokens / 1,000,000) * output_price
| Model | Input cost | Output cost | Total |
|---|---|---|---|
| gpt-4o-mini | 0.9 × $0.15 = $0.135 | 0.3 × $0.6 = $0.18 | $0.315 |
| deepseek-chat | 0.9 × $0.28 = $0.252 | 0.3 × $0.42 = $0.126 | $0.378 |
| gemini-2.5-flash | 0.9 × $0.3 = $0.27 | 0.3 × $2.5 = $0.75 | $1.02 |
| gpt-4o | 0.9 × $2.5 = $2.25 | 0.3 × $10 = $3.00 | $5.25 |
On this workload, gpt-4o-mini is the cheapest of the models listed, and gpt-4o is much more expensive. But that does not mean gpt-4o-mini is always the right answer. If the cheaper model needs more retries, more context, or more manual cleanup, the real cost can climb.
If you want to inspect current model prices instead of relying on memory, the live catalog in the model price directory is the place to check.
What the current price spread actually looks like
Here is a small snapshot of the specific prices that matter for coding comparisons right now. All figures are per 1M tokens.
| Model | Input | Output |
|---|---|---|
| gpt-4o-mini | $0.15 | $0.6 |
| deepseek-chat | $0.28 | $0.42 |
| gemini-2.5-flash | $0.3 | $2.5 |
| gpt-4o | $2.5 | $10 |
| claude-3-5-haiku | $0.8 | $4 |
| claude-sonnet-4-5 | $3 | $15 |
| gemini-2.5-pro | $1.25 | $10 |
| claude-opus-4-1 | $15 | $75 |
The obvious takeaway is that the cheapest input price does not always mean the cheapest task. A model with slightly higher input cost but much lower output cost can win on long generations. That is why blended comparisons are useful, and why the value-for-money view exists in the first place.
When the cheapest model is not the cheapest outcome
For coding, three things tend to distort the bill:
- Long repo context, which pushes input tokens up fast.
- Verbose answers or repeated diffs, which inflate output tokens.
- Retry loops, where a cheap model burns more total tokens because it misses on the first pass.
That means a model like gpt-4o-mini can be the cheapest on paper, but if it takes two extra passes to land a correct patch, the effective cost per successful task may no longer be the lowest. Likewise, a model like claude-sonnet-4-5 may cost more per token, but still be efficient if it reduces iteration.
This is also why comparing only “per token” cost is a trap. The unit is per 1M tokens, and the task-level outcome is what matters. If you are shipping features, the bill you care about is the one attached to a finished change, not a single prompt.
How to pick the cheapest model for your workflow
Use this sequence instead of guessing:
- Measure your real token mix for a week.
- Split it by model, agent, and use-case.
- Check whether your workload is input-heavy, output-heavy, or retry-heavy.
- Compare blended cost, not just input price.
- Watch success rate alongside spend.
If your workflow is mostly short prompts with small outputs, gpt-4o-mini and deepseek-chat are often the first models worth testing because their blended cost can stay low. If you need stronger reasoning or broader capability, the higher-priced models may still be worth it, but the math should be explicit, not vibes.
For macro context on the market, the State of AI page is useful because it shows where pricing sits relative to the broader ecosystem, not just a single vendor.
How to track this without guessing
The easiest way to find your cheapest coding model is to measure what you already use. Run npx mytokentracker and it reads the usage logs your coding agents already keep locally, then prints a 30-day cost table at API prices. Usage data stays on your machine.
If you want a dashboard, create a free account and run npx mytokentracker init. It uploads history, then syncs every 30 minutes. It works with Claude Code, Codex, GitHub Copilot CLI, Antigravity, OpenCode, Amp, Qwen Code and more. Only daily totals per agent and model are uploaded, never prompts, code, or file names.
MyTokenTracker also captures cost, input/output/cache/reasoning tokens, latency, and success, broken down by provider, model, platform, and use-case. That makes it much easier to answer the real question: which model is cheapest for the work you actually do?
FAQ
Is the cheapest model always the best model for coding?
No. The cheapest model on a per-1M-token basis can still be expensive if it needs more retries, longer prompts, or extra cleanup. The best choice is the lowest cost per successful task.
Should I compare input price or output price?
Both. Input price matters for context-heavy workflows, but output price can dominate when the model writes long responses, explanations, or large patches. Blended cost is the safer comparison.
How do I know what my workflow really costs?
Track your own logs. A tool like MyTokenTracker can show 30-day cost tables from local agent logs, then break spend down by model, provider, and use-case so you can compare actual usage instead of estimates.
If you want to see the numbers on your own machine, install it with npx mytokentracker.