Gemini 3.8 Flash Cyber vs Ternary Bonsai 27B
At a Glance
| Compare | Gemini 3.8 Flash CyberGoogle DeepMind | Ternary Bonsai 27BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | Not reported | 262K |
| 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 | Ternary Bonsai 27B |
|---|---|---|
| Developer | Google DeepMind | PrismML |
| Family | Gemini 3 | Bonsai 27b |
| Model | Gemini 3.8 Flash Cyber | Ternary Bonsai 27B |
| Version | Gemini 3.8 Flash Cyber | Ternary Bonsai 27B |
| Lifecycle | active | active |
| Released | 2026-09-02 | 2026-07-04 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image |
| Output modalities | Text | Text |
| Context window | Unknown | 262K |
| Total parameters | Unknown | 27B |
| Active parameters | Unknown | Unknown |
| License | Unknown | apache-2.0 |
| Open weights | No | Yes |
| API available | No | Yes |
| Self-hostable | No | Yes |
| Provider access | Unknown | Together Ai (Standard) |
| Capabilities | automated-patching, cybersecurity, reasoning, vulnerability-detection | chat, generation, reasoning, tools, vision |
| Base model | Unknown | Qwen3.6 27B |
| Effective bit width | Unknown | 1.58 bits per weight |
| Language model size | Unknown | 6.66 GiB |
| Weight format | Unknown | Ternary Q2_0 |
Gemini 3.8 Flash Cyber Capabilities
Ternary Bonsai 27B Capabilities
Primary Evidence
Sources and Freshness
Questions
Gemini 3.8 Flash Cyber vs Ternary Bonsai 27B FAQs
Is Gemini 3.8 Flash Cyber or Ternary Bonsai 27B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.8 Flash Cyber and Ternary Bonsai 27B, 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 Ternary Bonsai 27B?+
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 Ternary Bonsai 27B?+
Neither model has a larger sourced context window in this comparison. Gemini 3.8 Flash Cyber is — and Ternary Bonsai 27B is 262K.
Which performs better in benchmarks, Gemini 3.8 Flash Cyber or Ternary Bonsai 27B?+
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 Ternary Bonsai 27B be self-hosted?+
Ternary Bonsai 27B is the only model in this pair currently marked as self-hostable. Gemini 3.8 Flash Cyber is not marked open weight; Ternary Bonsai 27B is open weight.
Can Gemini 3.8 Flash Cyber and Ternary Bonsai 27B understand images?+
Gemini 3.8 Flash Cyber is not documented with image input; Ternary Bonsai 27B is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini 3.8 Flash Cyber or Ternary Bonsai 27B?+
Neither has a larger sourced maximum output. Gemini 3.8 Flash Cyber is — and Ternary Bonsai 27B is —.
Do Gemini 3.8 Flash Cyber and Ternary Bonsai 27B support reasoning and tool use?+
Gemini 3.8 Flash Cyber: reasoning. Ternary Bonsai 27B: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Gemini 3.8 Flash Cyber or Ternary Bonsai 27B?+
Gemini 3.8 Flash Cyber has 0 sourced provider routes; Ternary Bonsai 27B has 1, so Ternary Bonsai 27B has broader tracked availability.
Which offers better value, Gemini 3.8 Flash Cyber or Ternary 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.