Llama 4 Maverick 17B 128E vs Bonsai Image Ternary 4B
At a Glance
| Compare |  Bonsai Image Ternary 4BPrismML | |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 1,000K | Not reported |
| Model facts checked | Aug 28, 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 | Llama-4-Maverick-17B-128E | Bonsai Image Ternary 4B |
|---|---|---|
| Developer | Meta | PrismML |
| Family | Llama 4 Maverick 17b 128e | Bonsai Image 4b |
| Model | Llama-4-Maverick-17B-128E | Bonsai Image Ternary 4B |
| Version | Llama-4-Maverick-17B-128E | Bonsai Image Ternary 4B |
| Lifecycle | active | active |
| Released | 2025-04-05 | 2026-05-21 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Image |
| Context window | 1,000K | Unknown |
| Total parameters | 401.6B | 4B |
| Active parameters | 17B | Unknown |
| License | other | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Unknown | No |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Unknown |
| Capabilities | chat, generation, tools | generation |
| Base model | Unknown | FLUX.2 Klein 4B |
| Default resolution | Unknown | 512 × 512 |
| Transformer size | Unknown | 1.21 GB |
| Weight format | Unknown | Ternary weights with FP16 group scales |
Llama 4 Maverick 17B 128E Capabilities
Bonsai Image Ternary 4B Capabilities
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Primary Evidence
Sources and Freshness
Questions
Llama 4 Maverick 17B 128E vs Bonsai Image Ternary 4B FAQs
Is Llama 4 Maverick 17B 128E or Bonsai Image Ternary 4B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 4 Maverick 17B 128E and Bonsai Image Ternary 4B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Llama 4 Maverick 17B 128E or Bonsai Image Ternary 4B?+
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, Llama 4 Maverick 17B 128E or Bonsai Image Ternary 4B?+
Neither model has a larger sourced context window in this comparison. Llama 4 Maverick 17B 128E is 1,000K and Bonsai Image Ternary 4B is —.
Which performs better in benchmarks, Llama 4 Maverick 17B 128E or Bonsai Image Ternary 4B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Llama 4 Maverick 17B 128E or Bonsai Image Ternary 4B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Llama 4 Maverick 17B 128E is open weight; Bonsai Image Ternary 4B is open weight.
Can Llama 4 Maverick 17B 128E and Bonsai Image Ternary 4B understand images?+
Llama 4 Maverick 17B 128E is documented with image input; Bonsai Image Ternary 4B is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Llama 4 Maverick 17B 128E or Bonsai Image Ternary 4B?+
Neither has a larger sourced maximum output. Llama 4 Maverick 17B 128E is — and Bonsai Image Ternary 4B is —.
Do Llama 4 Maverick 17B 128E and Bonsai Image Ternary 4B support reasoning and tool use?+
Llama 4 Maverick 17B 128E: tool calling and image input. Bonsai Image Ternary 4B: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 4 Maverick 17B 128E or Bonsai Image Ternary 4B?+
Llama 4 Maverick 17B 128E has 0 sourced provider routes; Bonsai Image Ternary 4B has 0, a tie.
Which offers better value, Llama 4 Maverick 17B 128E or Bonsai Image Ternary 4B?+
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.