Olmo 3.1 32B Instruct vs Ternary Bonsai 1.7B
At a Glance
| Compare |  Ternary Bonsai 1.7BPrismML | |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 66K | 33K |
| Model facts checked | Aug 28, 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 | Olmo-3.1-32B-Instruct | Ternary Bonsai 1.7B |
|---|---|---|
| Developer | Ai2 | PrismML |
| Family | Olmo 3 1 32b Instruct | Bonsai 1 7b |
| Model | Olmo-3.1-32B-Instruct | Ternary Bonsai 1.7B |
| Version | Olmo-3.1-32B-Instruct | Ternary Bonsai 1.7B |
| Lifecycle | active | active |
| Released | Unknown | 2026-04-18 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 66K | 33K |
| Total parameters | 32.2B | 1.7B |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Unknown | No |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Unknown |
| Capabilities | chat, generation, tools | chat, generation |
| Effective bit width | Unknown | 1.58 bits per weight |
| Weight size | Unknown | 0.46 GB |
| Weight format | Unknown | Ternary Q2_0 |
Olmo 3.1 32B Instruct Capabilities
Ternary Bonsai 1.7B Capabilities
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| vs |  Kimi-K3Moonshot AI | cross-developer peerstext | |
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| vs |  GLM-5.3Z.ai | cross-developer peerstext | |
| vs |  Hy4 previewTencent | cross-developer peerstext | |
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| vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Olmo 3.1 32B Instruct vs Ternary Bonsai 1.7B FAQs
Is Olmo 3.1 32B Instruct or Ternary Bonsai 1.7B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Olmo 3.1 32B Instruct and Ternary Bonsai 1.7B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Olmo 3.1 32B Instruct or Ternary Bonsai 1.7B?+
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, Olmo 3.1 32B Instruct or Ternary Bonsai 1.7B?+
Olmo 3.1 32B Instruct has the larger sourced context window. Olmo 3.1 32B Instruct supports 66K and Ternary Bonsai 1.7B supports 33K.
Which performs better in benchmarks, Olmo 3.1 32B Instruct 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 Olmo 3.1 32B Instruct or Ternary Bonsai 1.7B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Olmo 3.1 32B Instruct is open weight; Ternary Bonsai 1.7B is open weight.
Can Olmo 3.1 32B Instruct and Ternary Bonsai 1.7B understand images?+
Olmo 3.1 32B Instruct is not 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, Olmo 3.1 32B Instruct or Ternary Bonsai 1.7B?+
Neither has a larger sourced maximum output. Olmo 3.1 32B Instruct is 33K and Ternary Bonsai 1.7B is —.
Do Olmo 3.1 32B Instruct and Ternary Bonsai 1.7B support reasoning and tool use?+
Olmo 3.1 32B Instruct: tool calling. 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, Olmo 3.1 32B Instruct or Ternary Bonsai 1.7B?+
Olmo 3.1 32B Instruct has 0 sourced provider routes; Ternary Bonsai 1.7B has 0, a tie.
Which offers better value, Olmo 3.1 32B Instruct 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.