NVIDIA Nemotron 3.5 Lightning 30B A3B vs Bonsai 1.7B
At a Glance
| Compare | Bonsai 1.7BPrismML | |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 1,049K | 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 | NVIDIA Nemotron 3.5 Lightning 30B-A3B | Bonsai 1.7B |
|---|---|---|
| Developer | NVIDIA | PrismML |
| Family | Nvidia Nemotron 3 5 Lightning | Bonsai 1 7b |
| Model | NVIDIA Nemotron 3.5 Lightning 30B-A3B | Bonsai 1.7B |
| Version | NVIDIA Nemotron 3.5 Lightning 30B-A3B | Bonsai 1.7B |
| Lifecycle | active | active |
| Released | 2026-08-11 | 2026-03-29 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 1,049K | 33K |
| Total parameters | 30B | 1.7B |
| Active parameters | 3B | Unknown |
| License | openmdw-1.1 | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | No |
| Self-hostable | Yes | Yes |
| Provider access | Fireworks Ai (Standard) | Unknown |
| Capabilities | agents, chat, generation, reasoning, tools | chat, generation |
| Effective bit width | Unknown | 1 bit per weight |
| Weight size | Unknown | 0.25 GB |
| Weight format | Unknown | Binary Q1_0 |
NVIDIA Nemotron 3.5 Lightning 30B A3B Capabilities
Bonsai 1.7B Capabilities
Primary Evidence
Sources and Freshness
Questions
NVIDIA Nemotron 3.5 Lightning 30B A3B vs Bonsai 1.7B FAQs
Is NVIDIA Nemotron 3.5 Lightning 30B A3B or Bonsai 1.7B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both NVIDIA Nemotron 3.5 Lightning 30B A3B and Bonsai 1.7B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, NVIDIA Nemotron 3.5 Lightning 30B A3B or Bonsai 1.7B?+
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, NVIDIA Nemotron 3.5 Lightning 30B A3B or Bonsai 1.7B?+
NVIDIA Nemotron 3.5 Lightning 30B A3B has the larger sourced context window. NVIDIA Nemotron 3.5 Lightning 30B A3B supports 1,049K and Bonsai 1.7B supports 33K.
Which performs better in benchmarks, NVIDIA Nemotron 3.5 Lightning 30B A3B 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 NVIDIA Nemotron 3.5 Lightning 30B A3B or Bonsai 1.7B be self-hosted?+
Both models have the same recorded self-hosting status: supported. NVIDIA Nemotron 3.5 Lightning 30B A3B is open weight; Bonsai 1.7B is open weight.
Can NVIDIA Nemotron 3.5 Lightning 30B A3B and Bonsai 1.7B understand images?+
NVIDIA Nemotron 3.5 Lightning 30B A3B is not 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, NVIDIA Nemotron 3.5 Lightning 30B A3B or Bonsai 1.7B?+
Neither has a larger sourced maximum output. NVIDIA Nemotron 3.5 Lightning 30B A3B is — and Bonsai 1.7B is —.
Do NVIDIA Nemotron 3.5 Lightning 30B A3B and Bonsai 1.7B support reasoning and tool use?+
NVIDIA Nemotron 3.5 Lightning 30B A3B: reasoning and tool calling. Bonsai 1.7B: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, NVIDIA Nemotron 3.5 Lightning 30B A3B or Bonsai 1.7B?+
NVIDIA Nemotron 3.5 Lightning 30B A3B has 1 sourced provider route; Bonsai 1.7B has 0, so NVIDIA Nemotron 3.5 Lightning 30B A3B has broader tracked availability.
Which offers better value, NVIDIA Nemotron 3.5 Lightning 30B A3B 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.