Kimi K2.5 vs Bonsai Image Binary 4B
At a Glance
| Compare |  Kimi K2.5Moonshot AI |  Bonsai Image Binary 4BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.45Deepinfra ↗ · Aug 29, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $2.25Deepinfra ↗ · Aug 29, 2026 | Not reported |
| Context windowMaximum documented tokens | 262K | Not reported |
| 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-K2.5 | Bonsai Image Binary 4B |
|---|---|---|
| Developer | Moonshot AI | PrismML |
| Family | Kimi K2 5 | Bonsai Image 4b |
| Model | Kimi-K2.5 | Bonsai Image Binary 4B |
| Version | Kimi-K2.5 | Bonsai Image Binary 4B |
| Lifecycle | active | active |
| Released | 2026-01-27 | 2026-05-18 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Image |
| Context window | 262K | Unknown |
| Total parameters | 1T | 4B |
| Active parameters | 32B | Unknown |
| License | other | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | No |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard) | Unknown |
| Capabilities | chat, generation, reasoning, tools | generation |
| Base model | Unknown | FLUX.2 Klein 4B |
| Default resolution | Unknown | 512 × 512 |
| Transformer size | Unknown | 0.93 GB |
| Weight format | Unknown | Binary weights with FP16 group scales |
Kimi K2.5 Capabilities
Bonsai Image Binary 4B Capabilities
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Primary Evidence
Sources and Freshness
Questions
Kimi K2.5 vs Bonsai Image Binary 4B FAQs
Is Kimi K2.5 or Bonsai Image Binary 4B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Kimi K2.5 and Bonsai Image Binary 4B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Kimi K2.5 or Bonsai Image Binary 4B?+
Only Kimi K2.5 has a directly sourced input price: $0.45 per million tokens. Only Kimi K2.5 has a directly sourced output price: $2.25 per million tokens.
Which has a larger context window, Kimi K2.5 or Bonsai Image Binary 4B?+
Neither model has a larger sourced context window in this comparison. Kimi K2.5 is 262K and Bonsai Image Binary 4B is —.
Which performs better in benchmarks, Kimi K2.5 or Bonsai Image Binary 4B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Kimi K2.5 or Bonsai Image Binary 4B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Kimi K2.5 is open weight; Bonsai Image Binary 4B is open weight.
Can Kimi K2.5 and Bonsai Image Binary 4B understand images?+
Kimi K2.5 is documented with image input; Bonsai Image Binary 4B is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Kimi K2.5 or Bonsai Image Binary 4B?+
Neither has a larger sourced maximum output. Kimi K2.5 is — and Bonsai Image Binary 4B is —.
Do Kimi K2.5 and Bonsai Image Binary 4B support reasoning and tool use?+
Kimi K2.5: reasoning, tool calling, and image input. Bonsai Image Binary 4B: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, Kimi K2.5 or Bonsai Image Binary 4B?+
Kimi K2.5 has 3 sourced provider routes; Bonsai Image Binary 4B has 0, so Kimi K2.5 has broader tracked availability.
Which offers better value, Kimi K2.5 or Bonsai Image Binary 4B?+
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.