Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/kilo-org/kilocode/model-selectiongit clone --depth 1 https://github.com/Kilo-Org/kilocodeWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00010 | $0.02394 |
| Opus 5 | $0.00005 | $0.01197 |
| Sonnet 5 | $0.00002 | $0.00479 |
| Haiku 4.5 | $0.00001 | $0.00239 |
Grade A, and why
model-selection scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Selection Guide
Here's the honest truth about AI model recommendations: by the time I write them down, they're probably already outdated. New models drop every few weeks, existing ones get updated, prices shift, and yesterday's champion becomes today's budget option.
Instead of maintaining a static list that's perpetually behind, we built something better — a real-time leaderboard showing which models Kilo Code users are actually having success with right now.
Check the Live Models List
👉 See what's working today at kilo.ai/models
This isn't benchmarks from some lab. It's real usage data from developers like you, updated continuously. You'll see which models people are choosing for different tasks, what's delivering results, and how the landscape is shifting in real-time.
General Guidance
While the specifics change constantly, some principles stay consistent:
How to Select and Switch Models
{% tabs %} {% tab label="VSCode" %}
- Use the model selector in the chat prompt area to pick a model for the current session. You can also type
/modelsto open the model picker. - When the selected model supports variants, type
/variantto open the reasoning effort selector. - Press
Shift+Tabin the prompt input to cycle to the next reasoning effort variant, wrapping after the last one. This works in the sidebar chat, the Agent Manager prompt, and the New Worktree dialog, and the variant selector tooltip shows the shortcut on hover. To keepShift+Tabfor keyboard focus navigation instead, disable thekilo-code.new.chat.shiftTabCyclesVariantsetting (also available under Settings → Display). - Set per-agent defaults and a global default in the Settings panel (Models tab), or directly in the
kilo.jsoncconfig file. - Model precedence: Session override → Last picked per agent → Per-agent config → Global config → Auto Free (note: Auto Free may route to providers that log prompts — see the Auto Model page for details).
- The model selector remembers the last model you picked for each agent, so switching agents restores your previous choice. A manual pick always beats config settings.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 192 lines · 10 tokens per session scan A 7efd9b52ce10
model-selection is an agent published in the GitHub repository Kilo-Org/kilocode (27,081 stars, last pushed 2d ago), licensed MIT. It adds 10 tokens to every session and 2,394 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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