USE CASE · VERIFIED 14 JUL 2026

The cheapest AI API for coding assistants

Coding tasks are different from most other API workloads in one important way: the model needs to hold a meaningful chunk of your codebase in context to give useful answers, which makes context window size as important as the per-token price. A cheap model that can only see 128K tokens of your code is often less useful than a slightly pricier one that can see your whole repository.

Why the cheapest tier usually isn't the right answer here

Unlike simple classification tasks, coding genuinely benefits from stronger reasoning — budget-tier models can write plausible-looking code that has subtle bugs a mid-tier or flagship model would catch. Where the budget tier does earn its place is autocomplete-style suggestions and simple boilerplate generation, where speed and cost matter more than deep reasoning about architecture.

Context window is the number that actually matters

Gemini 3.1 Pro's 2M-token context window is the largest we track, meaning it can hold a substantially larger codebase in memory than Claude Opus 4.8 or GPT-5.5 (both effectively capped lower in practice for a single request). If your coding assistant needs to reason across many files simultaneously — refactoring that touches a dozen files, debugging an issue that spans multiple modules — that context headroom often matters more than a modest per-token price difference.

The lever that changes everything: prompt caching

Coding assistants are the textbook use case for prompt caching: the same codebase context gets sent on nearly every request within a session, with only the specific question changing. Caching that repeated codebase context can cut the input-token cost by roughly 90% on cache hits — for a workload this input-heavy, caching often matters more than which model you pick. Model choice affects quality; caching affects your actual bill.

How we'd actually decide

• Autocomplete and simple boilerplate: GPT-4o mini or Gemini 2.5 Flash — tied for cheapest, plenty capable for this.
• General coding assistant, moderate complexity: Claude Sonnet 5 or GPT-4o — the reasoning quality jump from budget tier is usually worth it here.
• Large codebase, cross-file reasoning: Gemini 3.1 Pro — the context window is the deciding factor, not the price.
• Whatever you pick: implement prompt caching before optimizing model choice — it's the bigger lever for this specific workload shape.

Worked example uses standard (non-batch, non-cached) list pricing verified 14 July 2026. Applying prompt caching to the repeated codebase-context portion would meaningfully lower every figure above. Use the calculator with your own volume for an exact estimate.