Qwen3 Embedding 8B vs Ternary Bonsai 1.7B
At a Glance
| Compare |  Ternary Bonsai 1.7BPrismML | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.010Deepinfra ↗ · Sep 22, 2026 | Not reported |
| Context windowMaximum documented tokens | 33K | 33K |
| Model facts checked | Sep 3, 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 | Qwen3 Embedding 8B | Ternary Bonsai 1.7B |
|---|---|---|
| Developer | Qwen | PrismML |
| Family | Qwen3 Embedding | Bonsai 1 7b |
| Model | Qwen3 Embedding 8B | Ternary Bonsai 1.7B |
| Version | Qwen3 Embedding 8B | Ternary Bonsai 1.7B |
| Lifecycle | active | active |
| Released | 2025-06-03 | 2026-04-18 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Embedding | Text |
| Context window | 33K | 33K |
| Total parameters | 8B | 1.7B |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | No |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Fireworks Ai (Standard) | Unknown |
| Capabilities | embeddings, multilingual, retrieval | chat, generation |
| Effective bit width | Unknown | 1.58 bits per weight |
| Weight size | Unknown | 0.46 GB |
| Weight format | Unknown | Ternary Q2_0 |
Qwen3 Embedding 8B Capabilities
Ternary Bonsai 1.7B Capabilities
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Primary Evidence
Sources and Freshness
Questions
Qwen3 Embedding 8B vs Ternary Bonsai 1.7B FAQs
Is Qwen3 Embedding 8B or Ternary Bonsai 1.7B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3 Embedding 8B and Ternary Bonsai 1.7B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Qwen3 Embedding 8B or Ternary Bonsai 1.7B?+
Only Qwen3 Embedding 8B has a directly sourced input price: $0.010 per million tokens. Neither model has a directly sourced output price in this comparison.
Which has a larger context window, Qwen3 Embedding 8B or Ternary Bonsai 1.7B?+
Neither model has a larger sourced context window in this comparison. Qwen3 Embedding 8B is 33K and Ternary Bonsai 1.7B is 33K.
Which performs better in benchmarks, Qwen3 Embedding 8B 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 Qwen3 Embedding 8B or Ternary Bonsai 1.7B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Qwen3 Embedding 8B is open weight; Ternary Bonsai 1.7B is open weight.
Can Qwen3 Embedding 8B and Ternary Bonsai 1.7B understand images?+
Qwen3 Embedding 8B 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, Qwen3 Embedding 8B or Ternary Bonsai 1.7B?+
Neither has a larger sourced maximum output. Qwen3 Embedding 8B is — and Ternary Bonsai 1.7B is —.
Do Qwen3 Embedding 8B and Ternary Bonsai 1.7B support reasoning and tool use?+
Qwen3 Embedding 8B: none of these features are definitively sourced. 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, Qwen3 Embedding 8B or Ternary Bonsai 1.7B?+
Qwen3 Embedding 8B has 2 sourced provider routes; Ternary Bonsai 1.7B has 0, so Qwen3 Embedding 8B has broader tracked availability.
Which offers better value, Qwen3 Embedding 8B 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.