Bonsai Image Binary 4B vs Bonsai Image Ternary 4B
At a Glance
| Compare |  Bonsai Image Binary 4BPrismML | Bonsai Image Ternary 4BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | Not reported | Not reported |
| Model facts checked | Sep 18, 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 | Bonsai Image Binary 4B | Bonsai Image Ternary 4B |
|---|---|---|
| Developer | PrismML | PrismML |
| Family | Bonsai Image 4b | Bonsai Image 4b |
| Model | Bonsai Image Binary 4B | Bonsai Image Ternary 4B |
| Version | Bonsai Image Binary 4B | Bonsai Image Ternary 4B |
| Lifecycle | active | active |
| Released | 2026-05-18 | 2026-05-21 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Image | Image |
| Context window | Unknown | Unknown |
| Total parameters | 4B | 4B |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | apache-2.0 |
| Open weights | Yes | Yes |
| API available | No | No |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Unknown |
| Capabilities | generation | generation |
| Base model | FLUX.2 Klein 4B | FLUX.2 Klein 4B |
| Default resolution | 512 × 512 | 512 × 512 |
| Transformer size | 0.93 GB | 1.21 GB |
| Weight format | Binary weights with FP16 group scales | Ternary weights with FP16 group scales |
Bonsai Image Binary 4B Capabilities
Bonsai Image Ternary 4B Capabilities
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Primary Evidence
Sources and Freshness
Questions
Bonsai Image Binary 4B vs Bonsai Image Ternary 4B FAQs
Is Bonsai Image Binary 4B or Bonsai Image Ternary 4B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Bonsai Image Binary 4B and Bonsai Image Ternary 4B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Bonsai Image Binary 4B or Bonsai Image Ternary 4B?+
Neither model has a directly sourced input price in this comparison. Neither model has a directly sourced output price in this comparison.
Which has a larger context window, Bonsai Image Binary 4B or Bonsai Image Ternary 4B?+
Neither model has a larger sourced context window in this comparison. Bonsai Image Binary 4B is — and Bonsai Image Ternary 4B is —.
Which performs better in benchmarks, Bonsai Image Binary 4B or Bonsai Image Ternary 4B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Bonsai Image Binary 4B or Bonsai Image Ternary 4B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Bonsai Image Binary 4B is open weight; Bonsai Image Ternary 4B is open weight.
Can Bonsai Image Binary 4B and Bonsai Image Ternary 4B understand images?+
Bonsai Image Binary 4B is not documented with image input; Bonsai Image Ternary 4B is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Bonsai Image Binary 4B or Bonsai Image Ternary 4B?+
Neither has a larger sourced maximum output. Bonsai Image Binary 4B is — and Bonsai Image Ternary 4B is —.
Do Bonsai Image Binary 4B and Bonsai Image Ternary 4B support reasoning and tool use?+
Bonsai Image Binary 4B: none of these features are definitively sourced. Bonsai Image Ternary 4B: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, Bonsai Image Binary 4B or Bonsai Image Ternary 4B?+
Bonsai Image Binary 4B has 0 sourced provider routes; Bonsai Image Ternary 4B has 0, a tie.
Which offers better value, Bonsai Image Binary 4B or Bonsai Image Ternary 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.