Moonshot AI
Kimi K3
Kimi K3 has near-S capability, but cost and quota limits make it an A-tier operating choice.
API pricing
USD per million tokens · Kimi API- Input
- $3
- Output
- $15
- Cached input
- $0.3
- Context window
- 1.05M
Suggested for this guide
Kimi
Use the same Kimi family tested on this page. Check the current plan and model access before subscribing.
Best for: Claude Code setups and cost-aware model work
Check Kimi plansPartner link. It supports Superbash Learn at no extra cost to you.Try the visual runs
Open the generated scene to test it, or compare that prompt with another model.
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How Kimi K3 compares
Three relevant peers, side by side.
- Kimi K3 This model
- Claude Fable 5
- GPT-5.6 Sol
- Claude Opus 4.8
6 benchmarks · 4 models
Official benchmark profile
Published scores outside our visual tests
GPQA Diamond
93.5%DeepSWE
67.5%Terminal-Bench 2.1
88.3%Full official benchmark table12 scores and peer comparisons
| Benchmark | Area | Score | Comparison |
|---|---|---|---|
| GPQA Diamond | Academic reasoning | 93.5% | |
| DeepSWE | Coding | 67.5% | |
| Terminal-Bench 2.1 | Agentic coding | 88.3% | |
| SWE-Marathon | Long-horizon coding | 42.0% | |
| BrowseComp | Agentic research | 91.2% | |
| DeepSearchQA (F1) | Deep research | 95.0% | |
| MCPMark-Verified | Tool use | 94.5% | |
| AutomationBench | Workflow automation | 30.8% | |
| SpreadsheetBench 2 | Spreadsheet work | 34.8% | |
| OmniDocBench | Document understanding | 91.1% | |
| MMMU-Pro | Multimodal reasoning | 81.6% / 83.4% | |
| CharXiv (RQ) | Chart reasoning | 84.8% / 91.3% |
Sources, pricing details and methodology
Verified model facts
Model specifications
- Status
- Current
- API model ID
kimi-k3- Context
- 1.05M
- Max output
- 131K
- License
- Not publicly verified
- Access route
- Not publicly verified
- Modalities
- Not publicly verified
- Hardware
- Not publicly verified
- Kimi API · USD / 1M tokens
- $3 input · $0.3 cached input · $15 output
Prices exclude applicable taxes and separately billed tools.
Benchmark sources
Moonshot reports Kimi K3 at maximum reasoning effort across coding, research, tool use, document work, and vision. The official table mixes provider harnesses on some agent tasks, so each row keeps its evaluation setting.
Kimi K3 Technical ReportEvaluation settings
- Maximum reasoning effort, temperature 1.0, top-p 0.95.
- Kimi Code harness on DeepSWE v1.1 tasks.
- Kimi Code harness; peers use each provider's cited best harness.
- Claude Code for Kimi and Claude; Codex for GPT-5.6 on the July 9 H20-calibrated task branch.
- Context compaction at 300K tokens; the full 1M-context run without context management scored 90.4%.
- Maximum reasoning effort, temperature 1.0, top-p 1.0.
- 600-task public subset with the official benchmark setup.
- Claude Code for Kimi and Claude; Codex for GPT models.
- Maximum reasoning effort; average over three multimodal runs.
- Without tools / with Python; original input order; average over three runs.
- Without tools / with Python; average over three runs.
- DeepSWE
- The official leaderboard score under mini-SWE-agent is 67.3%.
Editorial verdict
Where this model fits
Kimi K3 handles long coding, tool use, and visual iteration well. The team exhausted short-window and monthly plan allowances during regular use, so this placement accounts for access and cost as well as raw capability.
Best for
Watch out
Why it is ranked here
Evidence
Superbash editorial model ranking
Takeaway: Kimi K3 is currently placed in Tier A.
The September 2026 editorial roster places Kimi K3 at rank 4.
Open source →Kimi K3: Open Frontier Intelligence
Takeaway: Moonshot’s release table reports competitive coding, agentic, and vision results, including 88.3% on Terminal-Bench 2.1 and 81.2% on FrontierSWE.
These are vendor-reported results at maximum reasoning effort and vary by agent harness, so they establish K3’s frontier capability but are not a like-for-like overall score.
Open source →Superbash visual benchmark suite: Kimi K3
Takeaway: K3 completed all nine shared visual benchmark briefs in the Superbash suite.
The outputs provide a practical check of visual implementation quality alongside published benchmark results.
Open source →Related guides