DeepSeek V3.2 vs Ternary Bonsai 27B
At a Glance
| Compare |  DeepSeek V3.2DeepSeek |  Ternary Bonsai 27BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.26Deepinfra ↗ · Sep 23, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $0.38Deepinfra ↗ · Sep 23, 2026 | Not reported |
| Context windowMaximum documented tokens | 164K | 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 | DeepSeek-V3.2 | Ternary Bonsai 27B |
|---|---|---|
| Developer | DeepSeek | PrismML |
| Family | Deepseek V3 2 | Bonsai 27b |
| Model | DeepSeek-V3.2 | Ternary Bonsai 27B |
| Version | DeepSeek-V3.2 | Ternary Bonsai 27B |
| Lifecycle | active | active |
| Released | 2025-12-01 | 2026-07-04 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image |
| Output modalities | Text | Text |
| Context window | 164K | 262K |
| Total parameters | 685.4B | 27B |
| Active parameters | Unknown | Unknown |
| License | mit | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard) | Together Ai (Standard) |
| Capabilities | chat, generation, reasoning | chat, generation, reasoning, tools, vision |
| Base model | Unknown | Qwen3.6 27B |
| Effective bit width | Unknown | 1.58 bits per weight |
| Language model size | Unknown | 6.66 GiB |
| Weight format | Unknown | Ternary Q2_0 |
DeepSeek V3.2 Capabilities
Ternary Bonsai 27B Capabilities
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DeepSeek-V3.2DeepSeek | vs |  Kimi-K3Moonshot AI | cross-developer peerstext |
 MiniMax-M3MiniMax | vs | DeepSeek-V3.2DeepSeek | cross-developer peerstext |
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Primary Evidence
Sources and Freshness
Questions
DeepSeek V3.2 vs Ternary Bonsai 27B FAQs
Is DeepSeek V3.2 or Ternary Bonsai 27B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V3.2 and Ternary Bonsai 27B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, DeepSeek V3.2 or Ternary Bonsai 27B?+
Only DeepSeek V3.2 has a directly sourced input price: $0.26 per million tokens. Only DeepSeek V3.2 has a directly sourced output price: $0.38 per million tokens.
Which has a larger context window, DeepSeek V3.2 or Ternary Bonsai 27B?+
Ternary Bonsai 27B has the larger sourced context window. DeepSeek V3.2 supports 164K and Ternary Bonsai 27B supports 262K.
Which performs better in benchmarks, DeepSeek V3.2 or Ternary Bonsai 27B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can DeepSeek V3.2 or Ternary Bonsai 27B be self-hosted?+
Both models have the same recorded self-hosting status: supported. DeepSeek V3.2 is open weight; Ternary Bonsai 27B is open weight.
Can DeepSeek V3.2 and Ternary Bonsai 27B understand images?+
DeepSeek V3.2 is not documented with image input; Ternary Bonsai 27B is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, DeepSeek V3.2 or Ternary Bonsai 27B?+
Neither has a larger sourced maximum output. DeepSeek V3.2 is — and Ternary Bonsai 27B is —.
Do DeepSeek V3.2 and Ternary Bonsai 27B support reasoning and tool use?+
DeepSeek V3.2: reasoning. Ternary Bonsai 27B: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V3.2 or Ternary Bonsai 27B?+
DeepSeek V3.2 has 3 sourced provider routes; Ternary Bonsai 27B has 1, so DeepSeek V3.2 has broader tracked availability.
Which offers better value, DeepSeek V3.2 or Ternary 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.