Kimi K3 vs Bonsai 27B
At a Glance
| Compare |  Kimi K3Moonshot AI |  Bonsai 27BPrismML |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #17 of 4670.3 score · 3/3 sources · complete | UnrankedNot in the 46-model eligible cohort |
| CostLower is better · Published-token output estimate | #30 of 44$0.194 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.5 | #21 of 3852.5 score · 3/3 sources · complete | UnrankedNot in the 38-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $2.85Deepinfra ↗ · Sep 22, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $14.25Deepinfra ↗ · Sep 22, 2026 | Not reported |
| Context windowMaximum documented tokens | 1,049K | 262K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 18, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | Kimi-K3 | Bonsai 27B |
|---|---|---|
| Developer | Moonshot AI | PrismML |
| Family | Kimi K3 | Bonsai 27b |
| Model | Kimi-K3 | Bonsai 27B |
| Version | Kimi-K3 | Bonsai 27B |
| Lifecycle | active | active |
| Released | 2026-07-16 | 2026-07-04 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,049K | 262K |
| Total parameters | 2.8T | 27B |
| Active parameters | 104B | Unknown |
| License | other | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | No |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Unknown |
| Capabilities | chat, generation, reasoning | chat, generation, reasoning, tools, vision |
| Base model | Unknown | Qwen3.6 27B |
| Effective bit width | Unknown | 1 bit per weight |
| Language model size | Unknown | 3.53 GiB |
| Weight format | Unknown | Binary Q1_0 |
Kimi K3 Capabilities
Bonsai 27B Capabilities
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Primary Evidence
Sources and Freshness
Questions
Kimi K3 vs Bonsai 27B FAQs
Is Kimi K3 or Bonsai 27B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Kimi K3 and Bonsai 27B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Kimi K3 or Bonsai 27B?+
Only Kimi K3 has a directly sourced input price: $2.85 per million tokens. Only Kimi K3 has a directly sourced output price: $14.25 per million tokens.
Which has a larger context window, Kimi K3 or Bonsai 27B?+
Kimi K3 has the larger sourced context window. Kimi K3 supports 1,049K and Bonsai 27B supports 262K.
Which performs better in benchmarks, Kimi K3 or Bonsai 27B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Kimi K3 or Bonsai 27B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Kimi K3 is open weight; Bonsai 27B is open weight.
Can Kimi K3 and Bonsai 27B understand images?+
Kimi K3 is documented with image input; Bonsai 27B is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Kimi K3 or Bonsai 27B?+
Neither has a larger sourced maximum output. Kimi K3 is — and Bonsai 27B is —.
Do Kimi K3 and Bonsai 27B support reasoning and tool use?+
Kimi K3: reasoning and image input. Bonsai 27B: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Kimi K3 or Bonsai 27B?+
Kimi K3 has 5 sourced provider routes; Bonsai 27B has 0, so Kimi K3 has broader tracked availability.
Which offers better value, Kimi K3 or Bonsai 27B?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.