Cache-hit input
Stable instructions and reusable context may receive the lower published rate when the official service records a cache hit. Measure the actual hit rate; do not assume every repeated token qualifies.
Kimi K3 API pricing for game developers
Plan Kimi K3 API cost for an Unreal game-development workflow
Understand official Kimi K3 API pricing, cache-hit and cache-miss inputs, output cost, Unreal workload assumptions, and a tracked prototype handoff.
Direct answer
At launch, Moonshot AI lists Kimi K3 API pricing at $0.30 per million cache-hit input tokens, $3.00 per million cache-miss input tokens, and $15.00 per million output tokens. Unreal teams should model cost from measured token usage, cache behavior, retries, and review loops—not the lowest headline rate alone.
Model the price of the whole Unreal workflow
The official unit price is only one input. A practical budget separates stable cached context, changing task context, generated output, retries, parallel runs, and the human verification work required before a native Unreal change is accepted.
Cache-miss input
New repository slices, logs, screenshots, design notes, and changed instructions use the higher published input rate when not served from cache.
Output tokens
Plans, patches, explanations, tests, and recovery attempts can dominate cost because the published output rate is higher than either input tier.
Operational overhead
Retries, failed tool calls, parallel evaluations, long thinking traces, human review, CI, and Unreal build time belong in the project budget even when they are not API token charges.
A four-stage Unreal evaluation workflow
Choose one billable task
Define a single repository review, bug investigation, test-plan generation, or implementation proposal with an explicit stopping condition.
Separate stable and changing context
Keep reusable policies and project instructions distinct from fresh files, logs, screenshots, and task-specific evidence so cache behavior can be measured.
Run a measured pilot
Record input, cached input, output, retries, latency, tool calls, and reviewer time for several representative tasks instead of extrapolating from one success.
Set budget and fallback rules
Cap spend per task, stop repeated failure loops, route sensitive work appropriately, and require human approval before any native Unreal change is merged.
Four bounded task prompts
Use these as task contracts, not as capability claims. Each one asks for observable evidence and a stopping condition.
Repository cost pilot
Analyze one isolated gameplay system with only the relevant files, return a change plan and validation checklist, and stop before implementation if required context is missing.
Screenshot triage pilot
Review a small set of labeled before-and-after captures, identify visible regressions, connect each finding to a testable hypothesis, and avoid claiming unseen runtime behavior.
Log-to-test pilot
Turn one bounded crash or packaging log into probable causes, evidence requests, a minimal reproduction plan, rollback criteria, and a costed sequence of next checks.
Prototype budget pilot
Create a small playable slice with a strict feature list and completion state, then record how many revision rounds are needed to satisfy the frozen acceptance checks.
Concrete outputs to retain
Token-cost worksheet
Measured cache-hit input, cache-miss input, output, retries, and per-task cost using the current official published rates.
Representative task set
A small suite spanning repository review, visual debugging, logs, planning, and prototype iteration rather than one unusually easy prompt.
Budget guardrails
Per-task limits, retry ceilings, timeouts, evidence requirements, sensitive-data rules, and escalation conditions.
Prototype handoff
A playable direction plus a separate native Unreal estimate for implementation, testing, packaging, and human review.
Best fit and human-review boundary
Best for
- Estimating K3 API spend from measured game-development tasks
- Comparing cost per accepted outcome instead of cost per token alone
- Designing cache-aware project instructions and bounded evidence packs
Still needs human review
- Pricing and service behavior can change, so the official API page must be checked before a production commitment
- Token billing does not include all engineering, CI, Unreal build, review, and release costs
- Sensitive source code, credentials, assets, and user data need an approved data-handling path before API use
Official evidence and adjacent K3 Unreal routes
Capability, availability, architecture, and pricing claims on this page are bounded to Moonshot AI's July 2026 launch post. Social comparisons are treated as demand signals, not verified results.
Kimi K3 API pricing for game developers FAQ
What is the official Kimi K3 API price at launch?
Moonshot AI's July 2026 launch lists $0.30 per million cache-hit input tokens, $3.00 per million cache-miss input tokens, and $15.00 per million output tokens. These are launch figures, not a permanent guarantee. Confirm the current official API pricing, billing unit, availability, and terms before committing a production Unreal workload.
Why are cache-hit and cache-miss prices different?
A cache hit means the service can reuse eligible previously processed context under its caching rules, while a cache miss requires fresh input processing. The lower price is attractive for stable project instructions, but actual eligibility and hit rate depend on the service. Measure billed usage rather than labeling repeated text as cached yourself.
How should an Unreal team estimate monthly K3 cost?
Sample representative tasks, record billed cache-hit input, cache-miss input, output, retries, and parallel runs, then multiply by expected task frequency. Add human review, CI, build machines, storage, observability, and failed-run overhead. Report a range with assumptions, because repository size alone does not predict prompt or output volume reliably.
Does a one-million-token context window mean I should send the whole Unreal repository?
No. A large context window is capacity, not a recommendation. Sending irrelevant files increases cost, noise, latency, privacy exposure, and the chance of contradictory instructions. Start with a repository map, retrieve only task-relevant files and logs, preserve stable instructions separately, and expand context only when evidence shows that something necessary is missing.
Can SEELE AI reduce Kimi API costs?
SEELE AI does not control Kimi API billing. It can reduce planning ambiguity by turning a bounded concept into a browser-playable prototype that stakeholders review before a large native implementation. That can prevent wasted engineering cycles, but it is a separate product workflow and should not be presented as a discount, cache layer, or official Kimi integration.
What is the best cost metric for AI-assisted game development?
Use cost per accepted, verified outcome. Track token charges alongside completion rate, human corrections, regressions, elapsed time, review effort, build minutes, and unresolved risk. A cheap run that produces an unusable patch or requires extensive cleanup can cost more than a higher-priced run that reaches the frozen acceptance criteria safely.
Does the generate button use Kimi K3?
No model is specified in the generated prompt. The button opens SEELE AI's generation page with a complete browser-playable game brief and attribution parameters. The page discusses K3 API planning, but the generation prompt describes only the desired experience. It does not claim to call Kimi K3 or create a native Unreal project.
Test the playable direction before native Unreal production
The prompt describes the complete game slice and does not select a model. This final route keeps the paid-download reminder and full attribution chain attached.