The decision this guide can support
This guide is for developers and small teams paying API rates for an agent that works inside an existing repository. It helps choose what to evaluate first. It does not name a universal coding winner, award a model rating, or claim that AIModel.Reviews has completed firsthand model tests.
Keep three objects separate: the model supplies inference, the agent harness selects files and tools, and the product adds permissions, quotas, and an execution environment. A Codex-versus-Claude-Code result is not automatically an isolated model comparison.
A practical shortlist
| Candidate | Exact API ID | Input / output per 1M | Role in an evaluation |
|---|---|---|---|
| GPT-5.6 Luna | gpt-5.6-luna | $0.20 / $1.20 | Low-cost starting configuration. |
| GPT-5.6 Terra | gpt-5.6-terra | $2.00 / $12.00 | Same-generation escalation candidate. |
| Claude Fable 5.1 | claude-fable-5-1 | $10.00 / $50.00 | Premium, long-horizon candidate with integration constraints. |
| GPT-6 Astra | gpt-6-astra | $10.00 / $50.00 | Frontier comparator for the hardest end-to-end work. |
Direct-provider USD rates checked 12 September 2026. OpenAI prices shown are standard short-context rates; cache, long-context, service-tier, regional, tool, and runtime charges can change the bill.
When the cheaper model may be enough
Luna is a sensible first candidate when a task is bounded and bad output is cheap to reject: a regression-backed bug fix, a small interface change with known callers, or an explanation whose file references you can verify. That is a selection rule, not a finding that Luna passes those categories reliably.
- For a bug fix, require a test that fails before the change and passes after it.
- For a feature, verify the interface, failure behavior, and an older caller.
- For repository explanation, require correct paths and a checkable call chain.
- For every patch, inspect unrelated edits, dependencies, configuration, and tests.
What should trigger an upgrade test?
Escalate after a repeatable failure remains when the environment, tools, context, and acceptance checks are held constant. A missing dependency is not evidence that a more expensive model is needed. A model repeatedly losing dispersed requirements or choosing the wrong abstraction may be.
Include a higher-effort Luna configuration in the comparison. Keep the starting commit, task, permissions, tool commands, limits, and acceptance checks fixed. Record every attempt, billed usage, intervention, and review/correction time.
Stop rule
Stay with the cheaper configuration when it repeatedly clears your written bar within the tested scope. Retain a stronger configuration for a category only when it repeatedly produces accepted work the cheaper one cannot, or removes a measured burden that matters to you. If both fail, improve the task or keep the work human-led.
Next: read the Luna versus Terra comparison, inspect the workflow-cost examples, or adapt the proposed protocol.
Reader questions
Frequently asked questions
What AI model should I test first for repository coding?
For a bounded, verifiable API workflow, GPT-5.6 Luna is the cost-conscious first candidate in this research. That is an evaluation starting point, not a reliability verdict.
When is Terra worth testing?
When Luna shows a repeatable model-related failure under controlled tools and acceptance checks, compare that same task with Terra and measure accepted outcomes, total billed usage, and human correction effort.
Does a large context window mean the model understands my whole repository?
No. Capacity is not demonstrated retrieval or correct use. Test whether the harness supplies the relevant constraints and whether the resulting change clears repository-specific checks.
Primary reading
Sources and update conditions
Rates, availability, model identity, and provider behavior can change. Recheck the linked sources before making a consequential purchase or sending repository data.