GPT-5.5 vs Bonsai 1.7B
At a Glance
| Compare |  GPT-5.5OpenAI |  Bonsai 1.7BPrismML |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #7 of 4683.9 score · 3/3 sources · complete | UnrankedNot in the 46-model eligible cohort |
| CostLower is better · Published-token output estimate | #38 of 44$0.341 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.5 | #18 of 3853.4 score · 3/3 sources · complete | UnrankedNot in the 38-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $5.00Openai ↗ · Sep 3, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $30.00Openai ↗ · Sep 3, 2026 | Not reported |
| Context windowMaximum documented tokens | 1,050K | 33K |
| Model facts checked | Sep 3, 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-5.5 | Bonsai 1.7B |
|---|---|---|
| Developer | OpenAI | PrismML |
| Family | Gpt 5 5 | Bonsai 1 7b |
| Model | GPT-5.5 | Bonsai 1.7B |
| Version | GPT-5.5 | Bonsai 1.7B |
| Lifecycle | active | active |
| Released | 2026-04-23 | 2026-03-29 |
| Knowledge cutoff | 2025-12-01 | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 1,050K | 33K |
| Total parameters | Unknown | 1.7B |
| Active parameters | Unknown | Unknown |
| License | Unknown | apache-2.0 |
| Open weights | No | Yes |
| API available | Yes | No |
| Self-hostable | No | Yes |
| Provider access | Openai (Standard), Openrouter (Standard) | Unknown |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | chat, generation |
| Effective bit width | Unknown | 1 bit per weight |
| Weight size | Unknown | 0.25 GB |
| Weight format | Unknown | Binary Q1_0 |
GPT-5.5 Capabilities
Bonsai 1.7B Capabilities
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Primary Evidence
Sources and Freshness
Questions
GPT-5.5 vs Bonsai 1.7B FAQs
Is GPT-5.5 or Bonsai 1.7B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GPT-5.5 and Bonsai 1.7B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, GPT-5.5 or Bonsai 1.7B?+
Only GPT-5.5 has a directly sourced input price: $5.00 per million tokens. Only GPT-5.5 has a directly sourced output price: $30.00 per million tokens.
Which has a larger context window, GPT-5.5 or Bonsai 1.7B?+
GPT-5.5 has the larger sourced context window. GPT-5.5 supports 1,050K and Bonsai 1.7B supports 33K.
Which performs better in benchmarks, GPT-5.5 or Bonsai 1.7B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can GPT-5.5 or Bonsai 1.7B be self-hosted?+
Bonsai 1.7B is the only model in this pair currently marked as self-hostable. GPT-5.5 is not marked open weight; Bonsai 1.7B is open weight.
Can GPT-5.5 and Bonsai 1.7B understand images?+
GPT-5.5 is documented with image input; Bonsai 1.7B is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, GPT-5.5 or Bonsai 1.7B?+
Neither has a larger sourced maximum output. GPT-5.5 is 128K and Bonsai 1.7B is —.
Do GPT-5.5 and Bonsai 1.7B support reasoning and tool use?+
GPT-5.5: reasoning, tool calling, and image input. Bonsai 1.7B: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, GPT-5.5 or Bonsai 1.7B?+
GPT-5.5 has 2 sourced provider routes; Bonsai 1.7B has 0, so GPT-5.5 has broader tracked availability.
Which offers better value, GPT-5.5 or Bonsai 1.7B?+
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.