Qwen3 Coder Next vs Ternary Bonsai 1.7B
At a Glance
| Compare |  Qwen3 Coder NextQwen |  Ternary Bonsai 1.7BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.12Openrouter ↗ · Sep 22, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $0.80Openrouter ↗ · Sep 22, 2026 | Not reported |
| Context windowMaximum documented tokens | 262K | 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 | Qwen3-Coder-Next | Ternary Bonsai 1.7B |
|---|---|---|
| Developer | Qwen | PrismML |
| Family | Qwen3 Coder Next | Bonsai 1 7b |
| Model | Qwen3-Coder-Next | Ternary Bonsai 1.7B |
| Version | Qwen3-Coder-Next | Ternary Bonsai 1.7B |
| Lifecycle | active | active |
| Released | 2026-02-02 | 2026-04-18 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 262K | 33K |
| Total parameters | 79.7B | 1.7B |
| Active parameters | 3B | Unknown |
| License | apache-2.0 | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | No |
| Self-hostable | Yes | Yes |
| Provider access | Hugging Face (Standard), Openrouter (Standard) | 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 |
Qwen3 Coder Next Capabilities
Ternary Bonsai 1.7B Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
Qwen3-Coder-NextQwen | vs |  GPT-6 AstraOpenAI | cross-developer peerstext |
Qwen3-Coder-NextQwen | vs | Claude Fable 5.1Anthropic | cross-developer peerstext |
Qwen3-Coder-NextQwen | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
Qwen3-Coder-NextQwen | vs |  DeepSeek-V4.1-FlashDeepSeek | cross-developer peerstext |
Qwen3-Coder-NextQwen | vs | Grok 4.6xAI | cross-developer peerstext |
Qwen3-Coder-NextQwen | vs | Qwen3.8-MaxQwen | family variantstext |
Qwen3-Coder-NextQwen | vs |  Kimi-K3Moonshot AI | cross-developer peerstext |
 MiniMax-M3MiniMax | vs | Qwen3-Coder-NextQwen | cross-developer peerstext |
Qwen3-Coder-NextQwen | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Qwen3-Coder-NextQwen | vs |  Hy4 previewTencent | cross-developer peerstext |
Qwen3-Coder-NextQwen | vs | Seed 2.1 ProByteDance Seed | cross-developer peerstext |
Qwen3-Coder-NextQwen | vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Qwen3 Coder Next vs Ternary Bonsai 1.7B FAQs
Is Qwen3 Coder Next or Ternary Bonsai 1.7B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3 Coder Next 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 Coder Next or Ternary Bonsai 1.7B?+
Only Qwen3 Coder Next has a directly sourced input price: $0.12 per million tokens. Only Qwen3 Coder Next has a directly sourced output price: $0.80 per million tokens.
Which has a larger context window, Qwen3 Coder Next or Ternary Bonsai 1.7B?+
Qwen3 Coder Next has the larger sourced context window. Qwen3 Coder Next supports 262K and Ternary Bonsai 1.7B supports 33K.
Which performs better in benchmarks, Qwen3 Coder Next 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 Coder Next or Ternary Bonsai 1.7B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Qwen3 Coder Next is open weight; Ternary Bonsai 1.7B is open weight.
Can Qwen3 Coder Next and Ternary Bonsai 1.7B understand images?+
Qwen3 Coder Next 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 Coder Next or Ternary Bonsai 1.7B?+
Neither has a larger sourced maximum output. Qwen3 Coder Next is — and Ternary Bonsai 1.7B is —.
Do Qwen3 Coder Next and Ternary Bonsai 1.7B support reasoning and tool use?+
Qwen3 Coder Next: 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, Qwen3 Coder Next or Ternary Bonsai 1.7B?+
Qwen3 Coder Next has 2 sourced provider routes; Ternary Bonsai 1.7B has 0, so Qwen3 Coder Next has broader tracked availability.
Which offers better value, Qwen3 Coder Next 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.