Ternary Bonsai 1.7B vs Grok Build 0.1
At a Glance
| Compare |  Ternary Bonsai 1.7BPrismML | |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #1 of 44$0.0020 per LiveBench case |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $1.00Xai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $2.00Xai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 33K | 256K |
| Model facts checked | Sep 18, 2026View model evidence → | Sep 3, 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 | Ternary Bonsai 1.7B | Grok Build 0.1 |
|---|---|---|
| Developer | PrismML | xAI |
| Family | Bonsai 1 7b | Grok Build |
| Model | Ternary Bonsai 1.7B | Grok Build 0.1 |
| Version | Ternary Bonsai 1.7B | Grok Build 0.1 |
| Lifecycle | active | preview |
| Released | 2026-04-18 | 2026-05-29 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image |
| Output modalities | Text | Text |
| Context window | 33K | 256K |
| Total parameters | 1.7B | Unknown |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | Unknown |
| Open weights | Yes | No |
| API available | No | Yes |
| Self-hostable | Yes | No |
| Provider access | Unknown | Xai (Standard) |
| Capabilities | chat, generation | chat, generation, reasoning, structured_outputs, tools |
| Effective bit width | 1.58 bits per weight | Unknown |
| Weight size | 0.46 GB | Unknown |
| Weight format | Ternary Q2_0 | Unknown |
Ternary Bonsai 1.7B Capabilities
Grok Build 0.1 Capabilities
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Primary Evidence
Sources and Freshness
Questions
Ternary Bonsai 1.7B vs Grok Build 0.1 FAQs
Is Ternary Bonsai 1.7B or Grok Build 0.1 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Ternary Bonsai 1.7B and Grok Build 0.1, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Ternary Bonsai 1.7B or Grok Build 0.1?+
Only Grok Build 0.1 has a directly sourced input price: $1.00 per million tokens. Only Grok Build 0.1 has a directly sourced output price: $2.00 per million tokens.
Which has a larger context window, Ternary Bonsai 1.7B or Grok Build 0.1?+
Grok Build 0.1 has the larger sourced context window. Ternary Bonsai 1.7B supports 33K and Grok Build 0.1 supports 256K.
Which performs better in benchmarks, Ternary Bonsai 1.7B or Grok Build 0.1?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Ternary Bonsai 1.7B or Grok Build 0.1 be self-hosted?+
Ternary Bonsai 1.7B is the only model in this pair currently marked as self-hostable. Ternary Bonsai 1.7B is open weight; Grok Build 0.1 is not marked open weight.
Can Ternary Bonsai 1.7B and Grok Build 0.1 understand images?+
Ternary Bonsai 1.7B is not documented with image input; Grok Build 0.1 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Ternary Bonsai 1.7B or Grok Build 0.1?+
Neither has a larger sourced maximum output. Ternary Bonsai 1.7B is — and Grok Build 0.1 is —.
Do Ternary Bonsai 1.7B and Grok Build 0.1 support reasoning and tool use?+
Ternary Bonsai 1.7B: none of these features are definitively sourced. Grok Build 0.1: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Ternary Bonsai 1.7B or Grok Build 0.1?+
Ternary Bonsai 1.7B has 0 sourced provider routes; Grok Build 0.1 has 1, so Grok Build 0.1 has broader tracked availability.
Which offers better value, Ternary Bonsai 1.7B or Grok Build 0.1?+
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.