Gemini 3.8 Flash Cyber vs Bonsai Image Binary 4B
At a Glance
| Compare |  Gemini 3.8 Flash CyberGoogle DeepMind |  Bonsai Image Binary 4BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | Not reported | Not reported |
| Model facts checked | Sep 2, 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 | Gemini 3.8 Flash Cyber | Bonsai Image Binary 4B |
|---|---|---|
| Developer | Google DeepMind | PrismML |
| Family | Gemini 3 | Bonsai Image 4b |
| Model | Gemini 3.8 Flash Cyber | Bonsai Image Binary 4B |
| Version | Gemini 3.8 Flash Cyber | Bonsai Image Binary 4B |
| Lifecycle | active | active |
| Released | 2026-09-02 | 2026-05-18 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Image |
| Context window | Unknown | Unknown |
| Total parameters | Unknown | 4B |
| Active parameters | Unknown | Unknown |
| License | Unknown | apache-2.0 |
| Open weights | No | Yes |
| API available | No | No |
| Self-hostable | No | Yes |
| Provider access | Unknown | Unknown |
| Capabilities | automated-patching, cybersecurity, reasoning, vulnerability-detection | 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 |
Gemini 3.8 Flash Cyber Capabilities
Bonsai Image Binary 4B Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemini 3.8 Flash Cyber vs Bonsai Image Binary 4B FAQs
Is Gemini 3.8 Flash Cyber or Bonsai Image Binary 4B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.8 Flash Cyber and Bonsai Image Binary 4B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini 3.8 Flash Cyber or Bonsai Image Binary 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, Gemini 3.8 Flash Cyber or Bonsai Image Binary 4B?+
Neither model has a larger sourced context window in this comparison. Gemini 3.8 Flash Cyber is — and Bonsai Image Binary 4B is —.
Which performs better in benchmarks, Gemini 3.8 Flash Cyber 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 Gemini 3.8 Flash Cyber or Bonsai Image Binary 4B be self-hosted?+
Bonsai Image Binary 4B is the only model in this pair currently marked as self-hostable. Gemini 3.8 Flash Cyber is not marked open weight; Bonsai Image Binary 4B is open weight.
Can Gemini 3.8 Flash Cyber and Bonsai Image Binary 4B understand images?+
Gemini 3.8 Flash Cyber is not 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, Gemini 3.8 Flash Cyber or Bonsai Image Binary 4B?+
Neither has a larger sourced maximum output. Gemini 3.8 Flash Cyber is — and Bonsai Image Binary 4B is —.
Do Gemini 3.8 Flash Cyber and Bonsai Image Binary 4B support reasoning and tool use?+
Gemini 3.8 Flash Cyber: reasoning. 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, Gemini 3.8 Flash Cyber or Bonsai Image Binary 4B?+
Gemini 3.8 Flash Cyber has 0 sourced provider routes; Bonsai Image Binary 4B has 0, a tie.
Which offers better value, Gemini 3.8 Flash Cyber 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.