Qwen3 Coder Next vs Bonsai 1.7B
At a Glance
| Compare |  Qwen3 Coder NextQwen |  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 | Bonsai 1.7B |
|---|---|---|
| Developer | Qwen | PrismML |
| Family | Qwen3 Coder Next | Bonsai 1 7b |
| Model | Qwen3-Coder-Next | Bonsai 1.7B |
| Version | Qwen3-Coder-Next | Bonsai 1.7B |
| Lifecycle | active | active |
| Released | 2026-02-02 | 2026-03-29 |
| 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 bit per weight |
| Weight size | Unknown | 0.25 GB |
| Weight format | Unknown | Binary Q1_0 |
Qwen3 Coder Next Capabilities
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 Bonsai 1.7B FAQs
Is Qwen3 Coder Next or Bonsai 1.7B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3 Coder Next and 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 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 Bonsai 1.7B?+
Qwen3 Coder Next has the larger sourced context window. Qwen3 Coder Next supports 262K and Bonsai 1.7B supports 33K.
Which performs better in benchmarks, Qwen3 Coder Next or 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 Bonsai 1.7B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Qwen3 Coder Next is open weight; Bonsai 1.7B is open weight.
Can Qwen3 Coder Next and Bonsai 1.7B understand images?+
Qwen3 Coder Next is not documented with image input; 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 Bonsai 1.7B?+
Neither has a larger sourced maximum output. Qwen3 Coder Next is — and Bonsai 1.7B is —.
Do Qwen3 Coder Next and Bonsai 1.7B support reasoning and tool use?+
Qwen3 Coder Next: tool calling. 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 Bonsai 1.7B?+
Qwen3 Coder Next has 2 sourced provider routes; Bonsai 1.7B has 0, so Qwen3 Coder Next has broader tracked availability.
Which offers better value, Qwen3 Coder Next or 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.