Ternary Bonsai 2 27B vs Hy4 preview
At a Glance
| Compare |  Ternary Bonsai 2 27BPrismML |  Hy4 previewTencent |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.075Openrouter ↗ · Sep 22, 2026 | $0.834Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | $0.50Openrouter ↗ · Sep 22, 2026 | $2.501Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 262K | 1,000K |
| Model facts checked | Sep 18, 2026View model evidence → | Sep 2, 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 | Ternary Bonsai 2 27B | Hy4 preview |
|---|---|---|
| Developer | PrismML | Tencent |
| Family | Bonsai 2 | Hy4 |
| Model | Ternary Bonsai 2 27B | Hy4 preview |
| Version | Ternary Bonsai 2 27B | Hy4 preview |
| Lifecycle | active | preview |
| Released | 2026-09-17 | 2026-08-28 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 262K | 1,000K |
| Total parameters | 27.4B | 770B |
| Active parameters | Unknown | 49B |
| License | apache-2.0 | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Openrouter (Standard) | Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, tools, vision | chat, generation, reasoning, tools |
| Base model | Qwen3.8 27B | Unknown |
| Effective bit width | 1.76 bits per weight | Unknown |
| Language model size | 5.93 GB | Unknown |
| Weight format | Ternary g128 with FP16 group scales | Unknown |
Ternary Bonsai 2 27B Capabilities
Hy4 preview Capabilities
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Primary Evidence
Sources and Freshness
Questions
Ternary Bonsai 2 27B vs Hy4 preview FAQs
Is Ternary Bonsai 2 27B or Hy4 preview better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Ternary Bonsai 2 27B and Hy4 preview, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Ternary Bonsai 2 27B or Hy4 preview?+
Ternary Bonsai 2 27B is $0.075 and Hy4 preview is $0.834 per million tokens, so Ternary Bonsai 2 27B is cheaper on this metric. Ternary Bonsai 2 27B is $0.50 and Hy4 preview is $2.501 per million tokens, so Ternary Bonsai 2 27B is cheaper on this metric.
Which has a larger context window, Ternary Bonsai 2 27B or Hy4 preview?+
Hy4 preview has the larger sourced context window. Ternary Bonsai 2 27B supports 262K and Hy4 preview supports 1,000K.
Which performs better in benchmarks, Ternary Bonsai 2 27B or Hy4 preview?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Ternary Bonsai 2 27B or Hy4 preview be self-hosted?+
Both models have the same recorded self-hosting status: supported. Ternary Bonsai 2 27B is open weight; Hy4 preview is open weight.
Can Ternary Bonsai 2 27B and Hy4 preview understand images?+
Ternary Bonsai 2 27B is documented with image input; Hy4 preview is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Ternary Bonsai 2 27B or Hy4 preview?+
Neither has a larger sourced maximum output. Ternary Bonsai 2 27B is — and Hy4 preview is —.
Do Ternary Bonsai 2 27B and Hy4 preview support reasoning and tool use?+
Ternary Bonsai 2 27B: reasoning, tool calling, and image input. Hy4 preview: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Ternary Bonsai 2 27B or Hy4 preview?+
Ternary Bonsai 2 27B has 1 sourced provider route; Hy4 preview has 1, a tie.
Which offers better value, Ternary Bonsai 2 27B or Hy4 preview?+
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.