Gemini 3 Flash vs Ternary Bonsai 1.7B
At a Glance
| Compare |  Gemini 3 FlashGoogle DeepMind |  Ternary Bonsai 1.7BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.50Google AI ↗ · Aug 29, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $3.00Google AI ↗ · Aug 29, 2026 | Not reported |
| Context windowMaximum documented tokens | 1,049K | 33K |
| Model facts checked | Aug 29, 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 Flash | Ternary Bonsai 1.7B |
|---|---|---|
| Developer | Google DeepMind | PrismML |
| Family | Gemini 3 | Bonsai 1 7b |
| Model | Gemini 3 Flash | Ternary Bonsai 1.7B |
| Version | Gemini 3 Flash | Ternary Bonsai 1.7B |
| Lifecycle | preview | active |
| Released | Unknown | 2026-04-18 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video, Audio, Document | Text |
| Output modalities | Text | Text |
| Context window | 1,049K | 33K |
| Total parameters | Unknown | 1.7B |
| Active parameters | Unknown | Unknown |
| License | Unknown | apache-2.0 |
| Open weights | No | Yes |
| API available | Yes | No |
| Self-hostable | No | Yes |
| Provider access | Google AI (Standard), Google Gemini (Standard) | Unknown |
| Capabilities | chat, generation, reasoning, tools | chat, generation |
| Effective bit width | Unknown | 1.58 bits per weight |
| Weight size | Unknown | 0.46 GB |
| Weight format | Unknown | Ternary Q2_0 |
Gemini 3 Flash Capabilities
Ternary Bonsai 1.7B Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemini 3 Flash vs Ternary Bonsai 1.7B FAQs
Is Gemini 3 Flash or Ternary Bonsai 1.7B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3 Flash and Ternary Bonsai 1.7B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini 3 Flash or Ternary Bonsai 1.7B?+
Only Gemini 3 Flash has a directly sourced input price: $0.50 per million tokens. Only Gemini 3 Flash has a directly sourced output price: $3.00 per million tokens.
Which has a larger context window, Gemini 3 Flash or Ternary Bonsai 1.7B?+
Gemini 3 Flash has the larger sourced context window. Gemini 3 Flash supports 1,049K and Ternary Bonsai 1.7B supports 33K.
Which performs better in benchmarks, Gemini 3 Flash or Ternary Bonsai 1.7B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Gemini 3 Flash or Ternary Bonsai 1.7B be self-hosted?+
Ternary Bonsai 1.7B is the only model in this pair currently marked as self-hostable. Gemini 3 Flash is not marked open weight; Ternary Bonsai 1.7B is open weight.
Can Gemini 3 Flash and Ternary Bonsai 1.7B understand images?+
Gemini 3 Flash is documented with image input; Ternary Bonsai 1.7B is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini 3 Flash or Ternary Bonsai 1.7B?+
Neither has a larger sourced maximum output. Gemini 3 Flash is 66K and Ternary Bonsai 1.7B is —.
Do Gemini 3 Flash and Ternary Bonsai 1.7B support reasoning and tool use?+
Gemini 3 Flash: reasoning, tool calling, and image input. Ternary Bonsai 1.7B: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, Gemini 3 Flash or Ternary Bonsai 1.7B?+
Gemini 3 Flash has 2 sourced provider routes; Ternary Bonsai 1.7B has 0, so Gemini 3 Flash has broader tracked availability.
Which offers better value, Gemini 3 Flash or Ternary Bonsai 1.7B?+
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.