Choosing a Model

There is a small dropdown in Kiro’s chat panel that most people set once and never think about again. It deserves more attention than that, because it is one of the few settings that changes both the quality of your results and what they cost you.

Every model in Kiro has a credit multiplier: how many credits a unit of work consumes compared to the default. Auto sits at 1.0x. Claude Opus 5 is 2.2x. Qwen3 Coder Next is 0.05x. That is a 44x spread between the top and bottom of the menu for the exact same request. Model choice is not a preference setting; it is a cost lever with a dial on it.

Credit multiplier A number showing how many credits a model consumes relative to Kiro's Auto setting, which is defined as 1.0x. A 2.2x model spends 2.2 credits where Auto would spend 1. and context window The maximum amount of text (code, conversation, files) a model can consider at once, measured in tokens. 1M means roughly a large codebase; 200K means a substantial slice of one. are the two numbers on this page worth memorizing. Everything else follows from them.

The lineup

Verified 2026-08-08 against Kiro’s model documentation. Every model below is available on all paid tiers; multipliers are relative to Auto at 1.0x.

Model	Context	Multiplier	Where it fits
Auto (router)	1M	1.0x	The default, and the baseline every other number is measured against
Claude Opus 5	1M	2.2x	Newest (July 2026); long agentic runs, multi-agent coordination, code review
Claude Opus 4.8 / 4.7 / 4.6	1M	2.2x	Previous Opus generations, same price as the newest
Claude Opus 4.5	200K	2.2x	Oldest Opus still listed; smaller window at the same cost
Claude Sonnet 5	1M	1.3x	”Anthropic’s most agentic Sonnet model yet”; the natural daily driver
Claude Sonnet 4.6	1M	1.3x	The previous Sonnet with a full 1M window
Claude Sonnet 4.5 / 4.0	200K	1.3x	Sonnet 4.5 is the model the Free tier gets
Claude Haiku 4.5	200K	0.4x	Fast and cheap; built for volume, not for subtlety
OpenAI GPT-5.6 Sol	272K	2.4x	Flagship of the GPT-5.6 tier, and the priciest model in Kiro
OpenAI GPT-5.6 Terra	272K	1.2x	The balanced middle tier
OpenAI GPT-5.6 Luna	272K	0.6x	The fastest of the three
GLM-5	200K	0.5x	Open-weight; a capable budget option
DeepSeek 3.2	128K	0.25x	Open-weight; smallest window on the list
MiniMax M2.5	200K	0.25x	Open-weight
MiniMax M2.1	200K	0.15x	Open-weight; older and cheaper than M2.5
Qwen3 Coder Next	256K	0.05x	Open-weight; the cheapest option by a wide margin

Two things stand out. First, the Anthropic families price by family, not by version: every Opus costs 2.2x whether it is 4.5 or 5, and every Sonnet costs 1.3x. So within a family, there is rarely a reason to pick an older version — you pay the same either way. Second, the open-weight tier isn’t slightly cheaper, it is cheaper by an order of magnitude. That gap is what makes it worth thinking about at all.

The GPT-5.6 arrival matters historically too: Sol, Terra, and Luna landed on July 14, 2026 as the first non-Anthropic frontier models offered in Kiro. Roughly, Sol competes with Opus, Terra with Sonnet, and Luna is the speed option.

What Auto actually does

Auto is not a model. It is a router: for each request, it decides which underlying model should answer, combining frontier models with smaller specialized ones, reading intent from how you phrase things, and reusing cached work where it can. You get a 1M context window and, by definition, a 1.0x multiplier.

Auto being the baseline is the part people miss. When Kiro says a model is 2.2x, it means 2.2x Auto — so choosing Opus 5 for everything is a standing decision to spend a bit over double what the default would have spent.

Where the choice actually lives

The picker in the chat panel is the obvious place, but it is not the only one.

• In the IDE and CLI, you choose per session from the model selector. The CLI has remembered your model preference across sessions since v2.6.0 (June 2026), so setting it once sticks.
• In a custom agent, model is a configuration field: model in the CLI’s agent JSON, and model in the YAML front matter of an IDE Markdown agent. This is the underused one. A “quick-fixer” agent pinned to Haiku and a “reviewer” agent pinned to Opus 5 means the right model is selected by the job, not by whatever you last clicked.

That second option is the closest thing to a set-and-forget answer. If you find yourself switching models manually several times a day, that’s a signal to encode the pattern into agents instead. (Custom agents are covered in Core Concepts.)

A practical decision guide

The facts above are Kiro’s. The advice below is recommended practice, not official guidance — a starting point to adjust once you have your own sense of how each model behaves on your codebase.

• Daily driver: Auto, or Sonnet 5. Auto if you want the cheapest sensible default and don’t mind that the underlying model varies. Sonnet 5 (1M, 1.3x) if you want consistent frontier behavior you can build habits around. The 30% premium buys predictability.
• The genuinely hard work: Opus 5. Large refactors, long autonomous runs, debugging that has already defeated one other model, code review where a missed issue is expensive. Accept the 2.2x; you are buying it for a reason.
• Quick, mechanical, high-volume tasks: Haiku 4.5 or an open-weight model. Renaming things, writing boilerplate tests, formatting, drafting commit messages, first-pass summaries. Here the multiplier is the point: at 0.05x, Qwen3 Coder Next lets you do twenty tasks for the price of one on Auto.
• The GPT-5.6 tiers when you want a second opinion. A different model family sometimes sees a problem your usual one keeps walking past. Sol for the hardest questions (at 2.4x, the most expensive choice available), Terra as a Sonnet-class alternative, Luna when speed matters more than depth.

Worked example

Say a mid-sized spec task — requirements, design, tasks, and a first implementation pass — costs about 10 credits on Auto. Same task, different picker settings:

• Opus 5 or any Opus: ~22 credits
• GPT-5.6 Sol: ~24 credits
• Sonnet 5: ~13 credits
• Haiku 4.5: ~4 credits
• Qwen3 Coder Next: ~0.5 credits

Nobody should run a real spec on Qwen3 and expect Opus results. But look at the middle of that list: Sonnet 5 costs three credits more than Auto and a full nine credits less than Opus. Most of the time, that middle row is the right row.

Context windows, in practice

The context window is a ceiling, not a target. A 1M window doesn’t make a model smarter on a small task; it makes it possible to work on a large one.

• 1M models (Opus 5 and 4.8/4.7/4.6, Sonnet 5 and 4.6, Auto) earn their window when you point the agent at a whole codebase, run a long multi-file refactor, or hold a session open across many turns.
• 200K–272K models are plenty for focused work: one module, one bug, one feature. Most of what you do in a day fits comfortably.
• DeepSeek 3.2’s 128K is the tightest on the list. Fine for a file or two; it will run out on a sprawling session.

Worth remembering either way: filling a large window with irrelevant material makes results worse, not better, and costs more doing it. A big window is permission to include what matters, not an invitation to include everything.

A note on tiers

The Free tier gets only Sonnet 4.5 and the open-weight models. Everything else on the table — every Opus, Sonnet 5, the GPT-5.6 tiers, Haiku, and Auto itself — needs a paid plan. If you are following a tutorial that assumes Opus and you are on Free, that is why the option is not in your dropdown. (How plans and credits work is covered in Credits & Pricing; kiro.dev/pricing is the authoritative source for current numbers.)

Opus 5 also arrived through a gradual rollout to paid customers, so a brand-new model may take a little while to appear for you specifically.

This page will go stale

Kiro’s model lineup changes roughly monthly: new models arrive, multipliers get adjusted, older versions retire. The numbers here were checked on August 8, 2026 and are honest as of that date.

The authorities are kiro.dev/docs/models for the current lineup and kiro.dev/changelog/models/ for what changed and when. When this page and those pages disagree, they are right.

In short: Auto is 1.0x and the sane default. Sonnet 5 is the consistent daily driver at 1.3x. Opus 5 at 2.2x is for work that earns it. Haiku and the open-weight models exist so mechanical tasks cost almost nothing. Check the multiplier before you switch, and check the docs before you trust this table.