Gemma 4 E2B vs Bonsai 1.7B
At a Glance
| Compare |  Gemma 4 E2BGoogle DeepMind |  Bonsai 1.7BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 131K | 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 | Gemma 4 E2B | Bonsai 1.7B |
|---|---|---|
| Developer | Google DeepMind | PrismML |
| Family | Gemma 4 | Bonsai 1 7b |
| Model | Gemma 4 E2B | Bonsai 1.7B |
| Version | Gemma 4 E2B | Bonsai 1.7B |
| Lifecycle | active | active |
| Released | 2026-03-02 | 2026-03-29 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video, Audio | Text |
| Output modalities | Text | Text |
| Context window | 131K | 33K |
| Total parameters | Unknown | 1.7B |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | No |
| Self-hostable | Yes | Yes |
| Provider access | Together Ai (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 |
Gemma 4 E2B Capabilities
Bonsai 1.7B Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemma 4 E2B vs Bonsai 1.7B FAQs
Is Gemma 4 E2B or Bonsai 1.7B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemma 4 E2B and Bonsai 1.7B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemma 4 E2B 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, Gemma 4 E2B or Bonsai 1.7B?+
Gemma 4 E2B has the larger sourced context window. Gemma 4 E2B supports 131K and Bonsai 1.7B supports 33K.
Which performs better in benchmarks, Gemma 4 E2B 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 Gemma 4 E2B or Bonsai 1.7B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Gemma 4 E2B is open weight; Bonsai 1.7B is open weight.
Can Gemma 4 E2B and Bonsai 1.7B understand images?+
Gemma 4 E2B 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, Gemma 4 E2B or Bonsai 1.7B?+
Neither has a larger sourced maximum output. Gemma 4 E2B is — and Bonsai 1.7B is —.
Do Gemma 4 E2B and Bonsai 1.7B support reasoning and tool use?+
Gemma 4 E2B: 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, Gemma 4 E2B or Bonsai 1.7B?+
Gemma 4 E2B has 1 sourced provider route; Bonsai 1.7B has 0, so Gemma 4 E2B has broader tracked availability.
Which offers better value, Gemma 4 E2B 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.