Phi-4 Multimodal Instruct vs Ternary Bonsai 27B
At a Glance
| Compare |  Phi-4 Multimodal InstructMicrosoft |  Ternary Bonsai 27BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 131K | 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 | Phi-4-multimodal-instruct | Ternary Bonsai 27B |
|---|---|---|
| Developer | Microsoft | PrismML |
| Family | Phi 4 Multimodal Instruct | Bonsai 27b |
| Model | Phi-4-multimodal-instruct | Ternary Bonsai 27B |
| Version | Phi-4-multimodal-instruct | Ternary Bonsai 27B |
| Lifecycle | active | active |
| Released | 2025-02-26 | 2026-07-04 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Audio | Text, Image |
| Output modalities | Text | Text |
| Context window | 131K | 262K |
| Total parameters | 5.6B | 27B |
| Active parameters | Unknown | Unknown |
| License | mit | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Unknown | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Together Ai (Standard) |
| Capabilities | chat, generation | 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 |
Phi-4 Multimodal Instruct Capabilities
Ternary Bonsai 27B Capabilities
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Phi-4-multimodal-instructMicrosoft | vs |  Qwen3.8-MaxQwen | cross-developer peersimage, text |
Phi-4-multimodal-instructMicrosoft | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
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Phi-4-multimodal-instructMicrosoft | vs |  GLM-5.3Z.ai | cross-developer peerstext |
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Phi-4-multimodal-instructMicrosoft | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Phi-4 Multimodal Instruct vs Ternary Bonsai 27B FAQs
Is Phi-4 Multimodal Instruct or Ternary Bonsai 27B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Phi-4 Multimodal Instruct and Ternary Bonsai 27B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Phi-4 Multimodal Instruct or Ternary Bonsai 27B?+
Neither model has a directly sourced input price in this comparison. Neither model has a directly sourced output price in this comparison.
Which has a larger context window, Phi-4 Multimodal Instruct or Ternary Bonsai 27B?+
Ternary Bonsai 27B has the larger sourced context window. Phi-4 Multimodal Instruct supports 131K and Ternary Bonsai 27B supports 262K.
Which performs better in benchmarks, Phi-4 Multimodal Instruct 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 Phi-4 Multimodal Instruct or Ternary Bonsai 27B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Phi-4 Multimodal Instruct is open weight; Ternary Bonsai 27B is open weight.
Can Phi-4 Multimodal Instruct and Ternary Bonsai 27B understand images?+
Phi-4 Multimodal Instruct is 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, Phi-4 Multimodal Instruct or Ternary Bonsai 27B?+
Neither has a larger sourced maximum output. Phi-4 Multimodal Instruct is — and Ternary Bonsai 27B is —.
Do Phi-4 Multimodal Instruct and Ternary Bonsai 27B support reasoning and tool use?+
Phi-4 Multimodal Instruct: image input. Ternary Bonsai 27B: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Phi-4 Multimodal Instruct or Ternary Bonsai 27B?+
Phi-4 Multimodal Instruct has 0 sourced provider routes; Ternary Bonsai 27B has 1, so Ternary Bonsai 27B has broader tracked availability.
Which offers better value, Phi-4 Multimodal Instruct 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.