Cheapest Models for Claude Code and Codex

The cheapest model for Claude Code can do the same coding task for a fraction of the cost of the most expensive one. In our own test, six top models ran the identical prompt and the cheapest finished for about 26 cents while the priciest cost over 3 dollars. That is roughly a 12 times spread for the same result. So the short answer is: you can often pay far less without losing quality, if you pick the cheapest capable model instead of always reaching for the most powerful one.

This guide shows the real numbers, then explains how to actually pick and switch models without turning your setup into a chore.

The cost spread is bigger than most people expect

We took one prompt, an HTML animation, and sent the exact same request to six models with no tweaking and no retries. Then we looked at the real cost of each run. Here is what the identical task cost on each model:

The cheapest run, GLM 5.2, cost about 26 cents. The priciest, Fable 5, cost over 3 dollars for the same prompt. Fable 5 produced an impressive result, though even it had a small glitch in the animation. GLM 5.2, an open-weight model at a fraction of the price, held its own. Sonnet 5 looked strong for what it charged. This is a single informal test, not a benchmark suite, but the pattern is hard to miss: price and quality are not the same axis, and the gap between models is enormous.

That is the whole point of a proper LLM pricing comparison for coding. When you look at GPT vs Claude coding cost, or DeepSeek vs Claude cost, or any open-weight model against a premium one, the cheaper option is frequently good enough for the job in front of you. If you default to a premium model for everything, you are almost certainly overpaying.

The trick is matching the model to the job

Here is the honest nuance. Expensive models are not useless. Fable 5 and Opus 4.8 exist because they are genuinely strong on hard problems, deep reasoning, tricky architecture, gnarly debugging. The mistake is not using a premium model. The mistake is using it for everything, including the routine edits and boilerplate that a cheaper model handles just as well.

So the best cheap AI coding model is not a single winner you pick once and forget. It is whichever capable model costs the least for the specific task at hand. A good rule of thumb:

• Routine work such as small edits, refactors, tests, and boilerplate: a cheaper capable model like GLM 5.2 or Sonnet 5 is usually plenty.
• Hard reasoning such as a subtle bug, a new system design, or a large refactor across many files: a premium model can earn its cost.
• When in doubt, start cheap and only move up if the result is not good enough. Most of the time you will not need to.

The cheapest capable LLM for coding, in other words, is a moving target. The savings come from having the freedom to move. Better still, you can let the gateway make that call for you on every request with smart model routing, so routine work drops to a cheaper model automatically.

Why switching models is usually a pain

Knowing a cheaper model works is one thing. Actually switching to it is another. Every provider has its own API, its own keys, and its own config. Reconfiguring your setup each time you want to try a different model kills the idea before you start, so most people stick with one expensive default and eat the cost. That friction is the real reason teams overpay, not a lack of good cheap models.

How Clawgate routes work to the cheapest capable model

Full disclosure: Clawgate is a tool my team at Virstack built, so I am biased. It is also the reason the comparison above was practical to run at all. Clawgate is a control layer for Claude Code and Codex. You point your tool at the gateway once with a single vsk_… key, and from then on you switch between models from different providers straight from a dashboard. No reconfiguring your local setup, no juggling multiple provider keys.

That matters for cost in a few concrete ways:

• One key, every model. Trying GLM 5.2 instead of Opus 4.8 is a dashboard action, not a code change. Testing a cheaper model takes seconds, so you actually do it.
• Real cost per model. Every run is tracked automatically, broken down model by model. The exact numbers in the table above came straight out of the Clawgate usage dashboard. When you can see that one task cost 12 times more on one model than another, you make better choices. You can track Claude Code usage per person and per project the same way.
• Model policies and forced models. A policy limits which models a key can use, and you can force a specific model for a given project, so cheaper tasks route to cheaper models on purpose instead of everything defaulting to the priciest option.

This is the origin story too. Clawgate started as an internal tool that cut our own AI bill by 70 percent before it became a product, and smarter model choice was a big part of that. You can read the full breakdown in how we cut 70 percent of our AI costs, and a step-by-step for running GLM 5.2 in Claude Code and Codex.

One thing to be clear about: we built Clawgate because no single existing tool did all three things a team actually needs. It does not just meter your spend. It also sets hard-stop budgets per user and per project so a bill can never run away, it compresses tokens with opt-in prompt compression that cuts up to 92 percent of the context agents resend on heavy workloads, and it catches abuse and impersonation by flagging requests whose content does not match the project they claim. Cheaper model routing is one lever. Those are the others, in the same dashboard, behind the same key.

Frequently asked questions

What is the cheapest model for coding tasks?

In our own test, the cheapest capable model was GLM 5.2, an open-weight model, which finished the same coding task for about 26 cents while the priciest model cost over 3 dollars. But the cheapest model on paper is not always the right answer for every job. The better question is which model is the cheapest one that still does your specific task well, and that changes depending on the task. Routing simple work to a cheap model and hard work to a stronger one is how you keep quality and still pay less.

Is a cheaper model good enough for coding?

Often, yes. In our comparison, GLM 5.2 held its own against models that cost several times more, and Sonnet 5 looked strong for its price. Cheaper does not mean weak. It means you should match the model to the difficulty of the job instead of defaulting to the most expensive one for everything. Expensive models are not useless, they are just not always necessary. For a hard architecture problem a premium model can be worth it, but for routine edits a cheaper model is usually fine.

How much can I save by switching models?

In our test the cheapest and priciest runs were about a 12 times price difference for the same prompt, so the savings can be large when you stop sending routine work to a premium model. Clawgate started as an internal tool at Virstack that cut our own AI bill by 70 percent, partly through smarter model choice and partly through prompt compression that can cut up to 92 percent of the tokens agents resend on heavy workloads. Your exact saving depends on your mix of tasks and how much you were overpaying.

Can I mix models from different providers?

Yes. With Clawgate you point Claude Code or Codex at the gateway once with a single key, then switch between models from Anthropic, OpenAI, open-weight models, and others straight from a dashboard. You do not reconfigure your local setup or juggle multiple provider keys, and every run is tracked so you can see what each model actually costs before you commit to it.