Bonsai 1.7B vs Grok 4.20 Multi Agent
At a Glance
| Compare |  Bonsai 1.7BPrismML | |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #22 of 4663.4 score · 2/3 sources · provisional · missing LiveBench · full-core range 42.3–75.6 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $1.25Xai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $2.50Xai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 33K | 1,000K |
| Model facts checked | Sep 18, 2026View model evidence → | Aug 29, 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 | Bonsai 1.7B | Grok 4.20 Multi-Agent |
|---|---|---|
| Developer | PrismML | xAI |
| Family | Bonsai 1 7b | Grok 4 20 |
| Model | Bonsai 1.7B | Grok 4.20 Multi-Agent |
| Version | Bonsai 1.7B | Grok 4.20 Multi-Agent |
| Lifecycle | active | preview |
| Released | 2026-03-29 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image |
| Output modalities | Text | Text |
| Context window | 33K | 1,000K |
| Total parameters | 1.7B | Unknown |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | Unknown |
| Open weights | Yes | No |
| API available | No | Yes |
| Self-hostable | Yes | No |
| Provider access | Unknown | Xai (Standard) |
| Capabilities | chat, generation | generation, reasoning, research, tools |
| Effective bit width | 1 bit per weight | Unknown |
| Weight size | 0.25 GB | Unknown |
| Weight format | Binary Q1_0 | Unknown |
Bonsai 1.7B Capabilities
Grok 4.20 Multi Agent Capabilities
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Primary Evidence
Sources and Freshness
Questions
Bonsai 1.7B vs Grok 4.20 Multi Agent FAQs
Is Bonsai 1.7B or Grok 4.20 Multi Agent better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Bonsai 1.7B and Grok 4.20 Multi Agent, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Bonsai 1.7B or Grok 4.20 Multi Agent?+
Only Grok 4.20 Multi Agent has a directly sourced input price: $1.25 per million tokens. Only Grok 4.20 Multi Agent has a directly sourced output price: $2.50 per million tokens.
Which has a larger context window, Bonsai 1.7B or Grok 4.20 Multi Agent?+
Grok 4.20 Multi Agent has the larger sourced context window. Bonsai 1.7B supports 33K and Grok 4.20 Multi Agent supports 1,000K.
Which performs better in benchmarks, Bonsai 1.7B or Grok 4.20 Multi Agent?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Bonsai 1.7B or Grok 4.20 Multi Agent be self-hosted?+
Bonsai 1.7B is the only model in this pair currently marked as self-hostable. Bonsai 1.7B is open weight; Grok 4.20 Multi Agent is not marked open weight.
Can Bonsai 1.7B and Grok 4.20 Multi Agent understand images?+
Bonsai 1.7B is not documented with image input; Grok 4.20 Multi Agent is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Bonsai 1.7B or Grok 4.20 Multi Agent?+
Neither has a larger sourced maximum output. Bonsai 1.7B is — and Grok 4.20 Multi Agent is —.
Do Bonsai 1.7B and Grok 4.20 Multi Agent support reasoning and tool use?+
Bonsai 1.7B: none of these features are definitively sourced. Grok 4.20 Multi Agent: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Bonsai 1.7B or Grok 4.20 Multi Agent?+
Bonsai 1.7B has 0 sourced provider routes; Grok 4.20 Multi Agent has 1, so Grok 4.20 Multi Agent has broader tracked availability.
Which offers better value, Bonsai 1.7B or Grok 4.20 Multi Agent?+
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.