GPT-4.1 Mini vs Ternary Bonsai 2 27B
At a Glance
| Compare |  GPT-4.1 MiniOpenAI |  Ternary Bonsai 2 27BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.20Openrouter ↗ · Sep 3, 2026 | $0.075Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | $0.80Openrouter ↗ · Sep 3, 2026 | $0.50Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 1,048K | 262K |
| Model facts checked | Aug 29, 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 | GPT-4.1 Mini | Ternary Bonsai 2 27B |
|---|---|---|
| Developer | OpenAI | PrismML |
| Family | Gpt 4 1 | Bonsai 2 |
| Model | GPT-4.1 Mini | Ternary Bonsai 2 27B |
| Version | GPT-4.1 Mini | Ternary Bonsai 2 27B |
| Lifecycle | active | active |
| Released | Unknown | 2026-09-17 |
| Knowledge cutoff | 2024-06-01 | Unknown |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,048K | 262K |
| Total parameters | Unknown | 27.4B |
| Active parameters | Unknown | Unknown |
| License | Unknown | apache-2.0 |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Openai (Standard), Openrouter (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 |
GPT-4.1 Mini Capabilities
Ternary Bonsai 2 27B Capabilities
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Primary Evidence
Sources and Freshness
Questions
GPT-4.1 Mini vs Ternary Bonsai 2 27B FAQs
Is GPT-4.1 Mini or Ternary Bonsai 2 27B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GPT-4.1 Mini and Ternary Bonsai 2 27B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, GPT-4.1 Mini or Ternary Bonsai 2 27B?+
GPT-4.1 Mini is $0.20 and Ternary Bonsai 2 27B is $0.075 per million tokens, so Ternary Bonsai 2 27B is cheaper on this metric. GPT-4.1 Mini is $0.80 and Ternary Bonsai 2 27B is $0.50 per million tokens, so Ternary Bonsai 2 27B is cheaper on this metric.
Which has a larger context window, GPT-4.1 Mini or Ternary Bonsai 2 27B?+
GPT-4.1 Mini has the larger sourced context window. GPT-4.1 Mini supports 1,048K and Ternary Bonsai 2 27B supports 262K.
Which performs better in benchmarks, GPT-4.1 Mini 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 GPT-4.1 Mini or Ternary Bonsai 2 27B be self-hosted?+
Ternary Bonsai 2 27B is the only model in this pair currently marked as self-hostable. GPT-4.1 Mini is not marked open weight; Ternary Bonsai 2 27B is open weight.
Can GPT-4.1 Mini and Ternary Bonsai 2 27B understand images?+
GPT-4.1 Mini 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, GPT-4.1 Mini or Ternary Bonsai 2 27B?+
Neither has a larger sourced maximum output. GPT-4.1 Mini is 33K and Ternary Bonsai 2 27B is —.
Do GPT-4.1 Mini and Ternary Bonsai 2 27B support reasoning and tool use?+
GPT-4.1 Mini: 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, GPT-4.1 Mini or Ternary Bonsai 2 27B?+
GPT-4.1 Mini has 2 sourced provider routes; Ternary Bonsai 2 27B has 1, so GPT-4.1 Mini has broader tracked availability.
Which offers better value, GPT-4.1 Mini 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.