Qwen3.5 35B A3B vs Bonsai Image Ternary 4B
At a Glance
| Compare |  Qwen3.5 35B A3BQwen |  Bonsai Image Ternary 4BPrismML |
|---|---|---|
| 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 | Not reported |
| 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 Image Ternary 4B |
|---|---|---|
| Developer | Qwen | PrismML |
| Family | Qwen3 5 35b A3b | Bonsai Image 4b |
| Model | Qwen3.5-35B-A3B | Bonsai Image Ternary 4B |
| Version | Qwen3.5-35B-A3B | Bonsai Image Ternary 4B |
| Lifecycle | active | active |
| Released | Unknown | 2026-05-21 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Image |
| Context window | 262K | Unknown |
| Total parameters | 36B | 4B |
| 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 | generation |
| Base model | Unknown | FLUX.2 Klein 4B |
| Default resolution | Unknown | 512 × 512 |
| Transformer size | Unknown | 1.21 GB |
| Weight format | Unknown | Ternary weights with FP16 group scales |
Qwen3.5 35B A3B Capabilities
Bonsai Image Ternary 4B 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 Image Ternary 4B FAQs
Is Qwen3.5 35B A3B or Bonsai Image Ternary 4B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.5 35B A3B and Bonsai Image Ternary 4B, 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 Image Ternary 4B?+
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 Image Ternary 4B?+
Neither model has a larger sourced context window in this comparison. Qwen3.5 35B A3B is 262K and Bonsai Image Ternary 4B is —.
Which performs better in benchmarks, Qwen3.5 35B A3B or Bonsai Image Ternary 4B?+
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 Image Ternary 4B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Qwen3.5 35B A3B is open weight; Bonsai Image Ternary 4B is open weight.
Can Qwen3.5 35B A3B and Bonsai Image Ternary 4B understand images?+
Qwen3.5 35B A3B is documented with image input; Bonsai Image Ternary 4B is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Qwen3.5 35B A3B or Bonsai Image Ternary 4B?+
Neither has a larger sourced maximum output. Qwen3.5 35B A3B is — and Bonsai Image Ternary 4B is —.
Do Qwen3.5 35B A3B and Bonsai Image Ternary 4B support reasoning and tool use?+
Qwen3.5 35B A3B: reasoning, tool calling, and image input. Bonsai Image Ternary 4B: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, Qwen3.5 35B A3B or Bonsai Image Ternary 4B?+
Qwen3.5 35B A3B has 4 sourced provider routes; Bonsai Image Ternary 4B has 0, so Qwen3.5 35B A3B has broader tracked availability.
Which offers better value, Qwen3.5 35B A3B or Bonsai Image Ternary 4B?+
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.