Gemini 2.5 Pro vs Ternary Bonsai 1.7B
At a Glance
| Compare |  Gemini 2.5 ProGoogle DeepMind |  Ternary Bonsai 1.7BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $1.25Google AI ↗ · Aug 29, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $10.00Google 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 2.5 Pro | Ternary Bonsai 1.7B |
|---|---|---|
| Developer | Google DeepMind | PrismML |
| Family | Gemini 2 5 | Bonsai 1 7b |
| Model | Gemini 2.5 Pro | Ternary Bonsai 1.7B |
| Version | Gemini 2.5 Pro | Ternary Bonsai 1.7B |
| Lifecycle | active | active |
| Released | Unknown | 2026-04-18 |
| Knowledge cutoff | 2025-01-01 | 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 2.5 Pro Capabilities
Ternary Bonsai 1.7B Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemini 2.5 Pro vs Ternary Bonsai 1.7B FAQs
Is Gemini 2.5 Pro or Ternary Bonsai 1.7B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 2.5 Pro 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 2.5 Pro or Ternary Bonsai 1.7B?+
Only Gemini 2.5 Pro has a directly sourced input price: $1.25 per million tokens. Only Gemini 2.5 Pro has a directly sourced output price: $10.00 per million tokens.
Which has a larger context window, Gemini 2.5 Pro or Ternary Bonsai 1.7B?+
Gemini 2.5 Pro has the larger sourced context window. Gemini 2.5 Pro supports 1,049K and Ternary Bonsai 1.7B supports 33K.
Which performs better in benchmarks, Gemini 2.5 Pro 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 2.5 Pro 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 2.5 Pro is not marked open weight; Ternary Bonsai 1.7B is open weight.
Can Gemini 2.5 Pro and Ternary Bonsai 1.7B understand images?+
Gemini 2.5 Pro 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 2.5 Pro or Ternary Bonsai 1.7B?+
Neither has a larger sourced maximum output. Gemini 2.5 Pro is 66K and Ternary Bonsai 1.7B is —.
Do Gemini 2.5 Pro and Ternary Bonsai 1.7B support reasoning and tool use?+
Gemini 2.5 Pro: 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 2.5 Pro or Ternary Bonsai 1.7B?+
Gemini 2.5 Pro has 2 sourced provider routes; Ternary Bonsai 1.7B has 0, so Gemini 2.5 Pro has broader tracked availability.
Which offers better value, Gemini 2.5 Pro 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.