Qwen3.5 35B A3B vs Bonsai 27B
At a Glance
| Compare |  Qwen3.5 35B A3BQwen |  Bonsai 27BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.14Deepinfra ↗ · Sep 22, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $1.00Deepinfra ↗ · Sep 22, 2026 | Not reported |
| Context windowMaximum documented tokens | 262K | 262K |
| 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 | Qwen3.5-35B-A3B | Bonsai 27B |
|---|---|---|
| Developer | Qwen | PrismML |
| Family | Qwen3 5 35b A3b | Bonsai 27b |
| Model | Qwen3.5-35B-A3B | Bonsai 27B |
| Version | Qwen3.5-35B-A3B | Bonsai 27B |
| Lifecycle | active | active |
| Released | Unknown | 2026-07-04 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 262K | 262K |
| Total parameters | 36B | 27B |
| 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 | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Unknown |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, tools, vision |
| Base model | Unknown | Qwen3.6 27B |
| Effective bit width | Unknown | 1 bit per weight |
| Language model size | Unknown | 3.53 GiB |
| Weight format | Unknown | Binary Q1_0 |
Qwen3.5 35B A3B Capabilities
Bonsai 27B Capabilities
Related Comparisons
All image comparisons →| A | Pair | B | Context |
|---|---|---|---|
Qwen3.5-35B-A3BQwen | vs |  GPT-6 AstraOpenAI | cross-developer peersimage, text |
Qwen3.5-35B-A3BQwen | vs | Claude Fable 5.1Anthropic | cross-developer peersimage, text |
Qwen3.5-35B-A3BQwen | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
Qwen3.5-35B-A3BQwen | vs |  DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
Qwen3.5-35B-A3BQwen | vs | Grok 4.6xAI | cross-developer peersimage, text |
Qwen3.5-35B-A3BQwen | vs | Qwen3.8-MaxQwen | family variantsimage, text |
Qwen3.5-35B-A3BQwen | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | Qwen3.5-35B-A3BQwen | cross-developer peersimage, text |
Qwen3.5-35B-A3BQwen | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Qwen3.5-35B-A3BQwen | vs |  Hy4 previewTencent | cross-developer peerstext |
Qwen3.5-35B-A3BQwen | vs | Seed 2.1 ProByteDance Seed | cross-developer peersimage, text |
Qwen3.5-35B-A3BQwen | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Qwen3.5 35B A3B vs Bonsai 27B FAQs
Is Qwen3.5 35B A3B or Bonsai 27B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.5 35B A3B and Bonsai 27B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Qwen3.5 35B A3B or Bonsai 27B?+
Only Qwen3.5 35B A3B has a directly sourced input price: $0.14 per million tokens. Only Qwen3.5 35B A3B has a directly sourced output price: $1.00 per million tokens.
Which has a larger context window, Qwen3.5 35B A3B or Bonsai 27B?+
Neither model has a larger sourced context window in this comparison. Qwen3.5 35B A3B is 262K and Bonsai 27B is 262K.
Which performs better in benchmarks, Qwen3.5 35B A3B or Bonsai 27B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Qwen3.5 35B A3B or Bonsai 27B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Qwen3.5 35B A3B is open weight; Bonsai 27B is open weight.
Can Qwen3.5 35B A3B and Bonsai 27B understand images?+
Qwen3.5 35B A3B is documented with image input; Bonsai 27B is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Qwen3.5 35B A3B or Bonsai 27B?+
Neither has a larger sourced maximum output. Qwen3.5 35B A3B is — and Bonsai 27B is —.
Do Qwen3.5 35B A3B and Bonsai 27B support reasoning and tool use?+
Qwen3.5 35B A3B: reasoning, tool calling, and image input. Bonsai 27B: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Qwen3.5 35B A3B or Bonsai 27B?+
Qwen3.5 35B A3B has 4 sourced provider routes; Bonsai 27B has 0, so Qwen3.5 35B A3B has broader tracked availability.
Which offers better value, Qwen3.5 35B A3B or Bonsai 27B?+
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.