Bonsai 1.7B vs Grok 4.3
At a Glance
| Compare |  Bonsai 1.7BPrismML | Grok 4.3xAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #40 of 4616.6 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 11.1–44.4 |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #7 of 44$0.028 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #33 of 3846.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 43.3–59.9 |
| 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 → | Sep 3, 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 4.3 |
|---|---|---|
| Developer | PrismML | xAI |
| Family | Bonsai 1 7b | Grok 4 |
| Model | Bonsai 1.7B | Grok 4.3 |
| Version | Bonsai 1.7B | Grok 4.3 |
| Lifecycle | active | active |
| Released | 2026-03-29 | 2026-06-17 |
| 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 | chat, generation, reasoning, structured_outputs, 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 4.3 Capabilities
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Primary Evidence
Sources and Freshness
Questions
Bonsai 1.7B vs Grok 4.3 FAQs
Is Bonsai 1.7B or Grok 4.3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Bonsai 1.7B and Grok 4.3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Bonsai 1.7B or Grok 4.3?+
Only Grok 4.3 has a directly sourced input price: $1.25 per million tokens. Only Grok 4.3 has a directly sourced output price: $2.50 per million tokens.
Which has a larger context window, Bonsai 1.7B or Grok 4.3?+
Grok 4.3 has the larger sourced context window. Bonsai 1.7B supports 33K and Grok 4.3 supports 1,000K.
Which performs better in benchmarks, Bonsai 1.7B or Grok 4.3?+
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 4.3 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 4.3 is not marked open weight.
Can Bonsai 1.7B and Grok 4.3 understand images?+
Bonsai 1.7B is not documented with image input; Grok 4.3 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Bonsai 1.7B or Grok 4.3?+
Neither has a larger sourced maximum output. Bonsai 1.7B is — and Grok 4.3 is —.
Do Bonsai 1.7B and Grok 4.3 support reasoning and tool use?+
Bonsai 1.7B: none of these features are definitively sourced. Grok 4.3: 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 4.3?+
Bonsai 1.7B has 0 sourced provider routes; Grok 4.3 has 1, so Grok 4.3 has broader tracked availability.
Which offers better value, Bonsai 1.7B or Grok 4.3?+
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.