Ministral 3 14B Instruct 2512 vs Ternary Bonsai 27B
At a Glance
| Compare |  Ministral 3 14B Instruct 2512Mistral AI |  Ternary Bonsai 27BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.20Openrouter ↗ · Aug 29, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $0.20Openrouter ↗ · Aug 29, 2026 | Not reported |
| Context windowMaximum documented tokens | 262K | 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 | Ministral-3-14B-Instruct-2512 | Ternary Bonsai 27B |
|---|---|---|
| Developer | Mistral AI | PrismML |
| Family | Ministral 3 14b Instruct 2512 | Bonsai 27b |
| Model | Ministral-3-14B-Instruct-2512 | Ternary Bonsai 27B |
| Version | Ministral-3-14B-Instruct-2512 | Ternary Bonsai 27B |
| Lifecycle | active | active |
| Released | Unknown | 2026-07-04 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 262K | 262K |
| Total parameters | 13.9B | 27B |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Openrouter (Standard), Together Ai (Standard) | Together Ai (Standard) |
| Capabilities | chat, generation, tools | 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 |
Ministral 3 14B Instruct 2512 Capabilities
Ternary Bonsai 27B Capabilities
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 MiniMax-M3MiniMax | vs | Ministral-3-14B-Instruct-2512Mistral AI | cross-developer peersimage, text |
Ministral-3-14B-Instruct-2512Mistral AI | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Ministral-3-14B-Instruct-2512Mistral AI | vs |  Hy4 previewTencent | cross-developer peerstext |
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Ministral-3-14B-Instruct-2512Mistral AI | vs | Mistral Large 3Mistral AI | developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Ministral 3 14B Instruct 2512 vs Ternary Bonsai 27B FAQs
Is Ministral 3 14B Instruct 2512 or Ternary Bonsai 27B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Ministral 3 14B Instruct 2512 and Ternary Bonsai 27B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Ministral 3 14B Instruct 2512 or Ternary Bonsai 27B?+
Only Ministral 3 14B Instruct 2512 has a directly sourced input price: $0.20 per million tokens. Only Ministral 3 14B Instruct 2512 has a directly sourced output price: $0.20 per million tokens.
Which has a larger context window, Ministral 3 14B Instruct 2512 or Ternary Bonsai 27B?+
Neither model has a larger sourced context window in this comparison. Ministral 3 14B Instruct 2512 is 262K and Ternary Bonsai 27B is 262K.
Which performs better in benchmarks, Ministral 3 14B Instruct 2512 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 Ministral 3 14B Instruct 2512 or Ternary Bonsai 27B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Ministral 3 14B Instruct 2512 is open weight; Ternary Bonsai 27B is open weight.
Can Ministral 3 14B Instruct 2512 and Ternary Bonsai 27B understand images?+
Ministral 3 14B Instruct 2512 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, Ministral 3 14B Instruct 2512 or Ternary Bonsai 27B?+
Neither has a larger sourced maximum output. Ministral 3 14B Instruct 2512 is — and Ternary Bonsai 27B is —.
Do Ministral 3 14B Instruct 2512 and Ternary Bonsai 27B support reasoning and tool use?+
Ministral 3 14B Instruct 2512: tool calling and 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, Ministral 3 14B Instruct 2512 or Ternary Bonsai 27B?+
Ministral 3 14B Instruct 2512 has 2 sourced provider routes; Ternary Bonsai 27B has 1, so Ministral 3 14B Instruct 2512 has broader tracked availability.
Which offers better value, Ministral 3 14B Instruct 2512 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.