Bonsai 4B vs GLM 5.3
At a Glance
| Compare |  Bonsai 4BPrismML |  GLM 5.3Z.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #16 of 4670.4 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 46.9–80.3 |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #34 of 44$0.248 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #26 of 3850.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 38.2–54.9 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $1.20Deepinfra ↗ · Sep 21, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $4.00Deepinfra ↗ · Sep 21, 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 4B | GLM-5.3 |
|---|---|---|
| Developer | PrismML | Z.ai |
| Family | Bonsai 4b | Glm 5 3 |
| Model | Bonsai 4B | GLM-5.3 |
| Version | Bonsai 4B | GLM-5.3 |
| Lifecycle | active | active |
| Released | 2026-03-29 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 33K | 1,000K |
| Total parameters | 4B | 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 | Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard) |
| Capabilities | chat, generation | agents, chat, reasoning, structured_outputs, tools |
| Effective bit width | 1 bit per weight | Unknown |
| Weight size | 0.57 GB | Unknown |
| Weight format | Binary Q1_0 | Unknown |
Bonsai 4B Capabilities
GLM 5.3 Capabilities
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Primary Evidence
Sources and Freshness
Questions
Bonsai 4B vs GLM 5.3 FAQs
Is Bonsai 4B or GLM 5.3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Bonsai 4B and GLM 5.3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Bonsai 4B or GLM 5.3?+
Only GLM 5.3 has a directly sourced input price: $1.20 per million tokens. Only GLM 5.3 has a directly sourced output price: $4.00 per million tokens.
Which has a larger context window, Bonsai 4B or GLM 5.3?+
GLM 5.3 has the larger sourced context window. Bonsai 4B supports 33K and GLM 5.3 supports 1,000K.
Which performs better in benchmarks, Bonsai 4B or GLM 5.3?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Bonsai 4B or GLM 5.3 be self-hosted?+
Bonsai 4B is the only model in this pair currently marked as self-hostable. Bonsai 4B is open weight; GLM 5.3 is not marked open weight.
Can Bonsai 4B and GLM 5.3 understand images?+
Bonsai 4B is not documented with image input; GLM 5.3 is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Bonsai 4B or GLM 5.3?+
Neither has a larger sourced maximum output. Bonsai 4B is — and GLM 5.3 is 131K.
Do Bonsai 4B and GLM 5.3 support reasoning and tool use?+
Bonsai 4B: none of these features are definitively sourced. GLM 5.3: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Bonsai 4B or GLM 5.3?+
Bonsai 4B has 0 sourced provider routes; GLM 5.3 has 4, so GLM 5.3 has broader tracked availability.
Which offers better value, Bonsai 4B or GLM 5.3?+
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.