Llama 4 Scout 17B 16E vs Ternary Bonsai 2 27B
At a Glance
| Compare |  Ternary Bonsai 2 27BPrismML | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.075Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.50Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 10,000K | 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 | Llama-4-Scout-17B-16E | Ternary Bonsai 2 27B |
|---|---|---|
| Developer | Meta | PrismML |
| Family | Llama 4 Scout 17b 16e | Bonsai 2 |
| Model | Llama-4-Scout-17B-16E | Ternary Bonsai 2 27B |
| Version | Llama-4-Scout-17B-16E | Ternary Bonsai 2 27B |
| Lifecycle | active | active |
| Released | 2025-04-05 | 2026-09-17 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 10,000K | 262K |
| Total parameters | 108.6B | 27.4B |
| Active parameters | 17B | Unknown |
| License | other | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Together Ai (Standard) | Openrouter (Standard) |
| Capabilities | chat, generation, tools | chat, generation, reasoning, tools, vision |
| Base model | Unknown | Qwen3.8 27B |
| Effective bit width | Unknown | 1.76 bits per weight |
| Language model size | Unknown | 5.93 GB |
| Weight format | Unknown | Ternary g128 with FP16 group scales |
Llama 4 Scout 17B 16E Capabilities
Ternary Bonsai 2 27B Capabilities
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Primary Evidence
Sources and Freshness
Questions
Llama 4 Scout 17B 16E vs Ternary Bonsai 2 27B FAQs
Is Llama 4 Scout 17B 16E or Ternary Bonsai 2 27B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 4 Scout 17B 16E and Ternary Bonsai 2 27B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Llama 4 Scout 17B 16E or Ternary Bonsai 2 27B?+
Only Ternary Bonsai 2 27B has a directly sourced input price: $0.075 per million tokens. Only Ternary Bonsai 2 27B has a directly sourced output price: $0.50 per million tokens.
Which has a larger context window, Llama 4 Scout 17B 16E or Ternary Bonsai 2 27B?+
Llama 4 Scout 17B 16E has the larger sourced context window. Llama 4 Scout 17B 16E supports 10,000K and Ternary Bonsai 2 27B supports 262K.
Which performs better in benchmarks, Llama 4 Scout 17B 16E or Ternary Bonsai 2 27B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Llama 4 Scout 17B 16E or Ternary Bonsai 2 27B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Llama 4 Scout 17B 16E is open weight; Ternary Bonsai 2 27B is open weight.
Can Llama 4 Scout 17B 16E and Ternary Bonsai 2 27B understand images?+
Llama 4 Scout 17B 16E is documented with image input; Ternary Bonsai 2 27B is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Llama 4 Scout 17B 16E or Ternary Bonsai 2 27B?+
Neither has a larger sourced maximum output. Llama 4 Scout 17B 16E is — and Ternary Bonsai 2 27B is —.
Do Llama 4 Scout 17B 16E and Ternary Bonsai 2 27B support reasoning and tool use?+
Llama 4 Scout 17B 16E: tool calling and image input. Ternary Bonsai 2 27B: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 4 Scout 17B 16E or Ternary Bonsai 2 27B?+
Llama 4 Scout 17B 16E has 1 sourced provider route; Ternary Bonsai 2 27B has 1, a tie.
Which offers better value, Llama 4 Scout 17B 16E or Ternary Bonsai 2 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.