Qwen3.8 2.4T A95B vs Bonsai 1.7B
At a Glance
| Compare |  Bonsai 1.7BPrismML | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $2.00Deepinfra ↗ · Sep 22, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $6.00Deepinfra ↗ · 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 | Qwen3.8-2.4T-A95B | Bonsai 1.7B |
|---|---|---|
| Developer | Qwen | PrismML |
| Family | Qwen3 8 2 4t A95b | Bonsai 1 7b |
| Model | Qwen3.8-2.4T-A95B | Bonsai 1.7B |
| Version | Qwen3.8-2.4T-A95B | Bonsai 1.7B |
| Lifecycle | active | active |
| Released | Unknown | 2026-03-29 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 262K | 33K |
| Total parameters | 2.4T | 1.7B |
| Active parameters | 95B | Unknown |
| License | other | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | No |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Unknown |
| Capabilities | chat, generation, reasoning, tools | chat, generation |
| Effective bit width | Unknown | 1 bit per weight |
| Weight size | Unknown | 0.25 GB |
| Weight format | Unknown | Binary Q1_0 |
Qwen3.8 2.4T A95B Capabilities
Bonsai 1.7B Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
| vs |  GPT-6 AstraOpenAI | cross-developer peerstext | |
| vs | Claude Fable 5.1Anthropic | cross-developer peerstext | |
| vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext | |
| vs |  DeepSeek-V4.1-FlashDeepSeek | cross-developer peerstext | |
| vs | Grok 4.6xAI | cross-developer peerstext | |
| vs |  Qwen3.8-MaxQwen | family variantstext | |
| vs |  Kimi-K3Moonshot AI | cross-developer peerstext | |
 MiniMax-M3MiniMax | vs | cross-developer peerstext | |
| vs |  GLM-5.3Z.ai | cross-developer peerstext | |
| vs |  Hy4 previewTencent | cross-developer peerstext | |
| vs | Seed 2.1 ProByteDance Seed | cross-developer peerstext | |
| vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Qwen3.8 2.4T A95B vs Bonsai 1.7B FAQs
Is Qwen3.8 2.4T A95B or Bonsai 1.7B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.8 2.4T A95B and Bonsai 1.7B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Qwen3.8 2.4T A95B or Bonsai 1.7B?+
Only Qwen3.8 2.4T A95B has a directly sourced input price: $2.00 per million tokens. Only Qwen3.8 2.4T A95B has a directly sourced output price: $6.00 per million tokens.
Which has a larger context window, Qwen3.8 2.4T A95B or Bonsai 1.7B?+
Qwen3.8 2.4T A95B has the larger sourced context window. Qwen3.8 2.4T A95B supports 262K and Bonsai 1.7B supports 33K.
Which performs better in benchmarks, Qwen3.8 2.4T A95B 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 Qwen3.8 2.4T A95B or Bonsai 1.7B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Qwen3.8 2.4T A95B is open weight; Bonsai 1.7B is open weight.
Can Qwen3.8 2.4T A95B and Bonsai 1.7B understand images?+
Qwen3.8 2.4T A95B 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, Qwen3.8 2.4T A95B or Bonsai 1.7B?+
Neither has a larger sourced maximum output. Qwen3.8 2.4T A95B is — and Bonsai 1.7B is —.
Do Qwen3.8 2.4T A95B and Bonsai 1.7B support reasoning and tool use?+
Qwen3.8 2.4T A95B: reasoning and 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, Qwen3.8 2.4T A95B or Bonsai 1.7B?+
Qwen3.8 2.4T A95B has 5 sourced provider routes; Bonsai 1.7B has 0, so Qwen3.8 2.4T A95B has broader tracked availability.
Which offers better value, Qwen3.8 2.4T A95B 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.