Ternary Bonsai 1.7B vs Qwen3 Coder Flash
At a Glance
| Compare |  Ternary Bonsai 1.7BPrismML | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.195Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.975Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 33K | 1,000K |
| 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 | Qwen3 Coder Flash |
|---|---|---|
| Developer | PrismML | Qwen |
| Family | Bonsai 1 7b | Qwen3 Coder |
| Model | Ternary Bonsai 1.7B | Qwen3 Coder Flash |
| Version | Ternary Bonsai 1.7B | Qwen3 Coder Flash |
| Lifecycle | active | active |
| Released | 2026-04-18 | 2025-07-28 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 33K | 1,000K |
| 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 | Alibaba Cloud Model Studio (Standard), Openrouter (Standard) |
| Capabilities | chat, generation | agents, 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
Qwen3 Coder Flash Capabilities
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Primary Evidence
Sources and Freshness
Questions
Ternary Bonsai 1.7B vs Qwen3 Coder Flash FAQs
Is Ternary Bonsai 1.7B or Qwen3 Coder Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Ternary Bonsai 1.7B and Qwen3 Coder Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Ternary Bonsai 1.7B or Qwen3 Coder Flash?+
Only Qwen3 Coder Flash has a directly sourced input price: $0.195 per million tokens. Only Qwen3 Coder Flash has a directly sourced output price: $0.975 per million tokens.
Which has a larger context window, Ternary Bonsai 1.7B or Qwen3 Coder Flash?+
Qwen3 Coder Flash has the larger sourced context window. Ternary Bonsai 1.7B supports 33K and Qwen3 Coder Flash supports 1,000K.
Which performs better in benchmarks, Ternary Bonsai 1.7B or Qwen3 Coder Flash?+
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 Qwen3 Coder Flash 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; Qwen3 Coder Flash is not marked open weight.
Can Ternary Bonsai 1.7B and Qwen3 Coder Flash understand images?+
Ternary Bonsai 1.7B is not documented with image input; Qwen3 Coder Flash is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Ternary Bonsai 1.7B or Qwen3 Coder Flash?+
Neither has a larger sourced maximum output. Ternary Bonsai 1.7B is — and Qwen3 Coder Flash is —.
Do Ternary Bonsai 1.7B and Qwen3 Coder Flash support reasoning and tool use?+
Ternary Bonsai 1.7B: none of these features are definitively sourced. Qwen3 Coder Flash: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Ternary Bonsai 1.7B or Qwen3 Coder Flash?+
Ternary Bonsai 1.7B has 0 sourced provider routes; Qwen3 Coder Flash has 2, so Qwen3 Coder Flash has broader tracked availability.
Which offers better value, Ternary Bonsai 1.7B or Qwen3 Coder Flash?+
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.