Bonsai Image Binary 4B vs Solar Pro 4
At a Glance
| Compare |  Bonsai Image Binary 4BPrismML |  Solar Pro 4Upstage |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.090Upstage ↗ · Sep 18, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.36Upstage ↗ · Sep 18, 2026 |
| Context windowMaximum documented tokens | Not reported | 524K |
| 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 | Solar Pro 4 |
|---|---|---|
| Developer | PrismML | Upstage |
| Family | Bonsai Image 4b | Solar Pro |
| Model | Bonsai Image Binary 4B | Solar Pro 4 |
| Version | Bonsai Image Binary 4B | Solar Pro 4 |
| Lifecycle | active | active |
| Released | 2026-05-18 | 2026-08-14 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Image | Text |
| Context window | Unknown | 524K |
| Total parameters | 4B | Unknown |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | Unknown |
| Open weights | Yes | No |
| API available | No | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Upstage (Standard) |
| Capabilities | generation | agents, chat, generation, reasoning, structured_outputs, tools |
| Base model | FLUX.2 Klein 4B | Unknown |
| Default resolution | 512 × 512 | Unknown |
| Supported languages | Unknown | English, Korean, Japanese |
| Training data cutoff | Unknown | February 2026 |
| Transformer size | 0.93 GB | Unknown |
| Weight format | Binary weights with FP16 group scales | Unknown |
Bonsai Image Binary 4B Capabilities
Solar Pro 4 Capabilities
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Primary Evidence
Sources and Freshness
Questions
Bonsai Image Binary 4B vs Solar Pro 4 FAQs
Is Bonsai Image Binary 4B or Solar Pro 4 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Bonsai Image Binary 4B and Solar Pro 4, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Bonsai Image Binary 4B or Solar Pro 4?+
Only Solar Pro 4 has a directly sourced input price: $0.090 per million tokens. Only Solar Pro 4 has a directly sourced output price: $0.36 per million tokens.
Which has a larger context window, Bonsai Image Binary 4B or Solar Pro 4?+
Neither model has a larger sourced context window in this comparison. Bonsai Image Binary 4B is — and Solar Pro 4 is 524K.
Which performs better in benchmarks, Bonsai Image Binary 4B or Solar Pro 4?+
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 Solar Pro 4 be self-hosted?+
Both models have the same recorded self-hosting status: supported. Bonsai Image Binary 4B is open weight; Solar Pro 4 is not marked open weight.
Can Bonsai Image Binary 4B and Solar Pro 4 understand images?+
Bonsai Image Binary 4B is not documented with image input; Solar Pro 4 is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Bonsai Image Binary 4B or Solar Pro 4?+
Neither has a larger sourced maximum output. Bonsai Image Binary 4B is — and Solar Pro 4 is 131K.
Do Bonsai Image Binary 4B and Solar Pro 4 support reasoning and tool use?+
Bonsai Image Binary 4B: none of these features are definitively sourced. Solar Pro 4: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Bonsai Image Binary 4B or Solar Pro 4?+
Bonsai Image Binary 4B has 0 sourced provider routes; Solar Pro 4 has 1, so Solar Pro 4 has broader tracked availability.
Which offers better value, Bonsai Image Binary 4B or Solar Pro 4?+
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.