Llama 4 Scout 17B 16E vs Ternary Bonsai 4B

This comparison does not currently contain a protocol-matched coding benchmark for both Llama 4 Scout 17B 16E and Ternary Bonsai 4B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Neither model has a directly sourced input price in this comparison. Neither model has a directly sourced output price in this comparison.

Llama 4 Scout 17B 16E has the larger sourced context window. Llama 4 Scout 17B 16E supports 10,000K and Ternary Bonsai 4B supports 33K.

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Both models have the same recorded self-hosting status: supported. Llama 4 Scout 17B 16E is open weight; Ternary Bonsai 4B is open weight.

Llama 4 Scout 17B 16E is documented with image input; Ternary Bonsai 4B is not documented with image input. This reflects supported input modalities, not vision quality.

Neither has a larger sourced maximum output. Llama 4 Scout 17B 16E is — and Ternary Bonsai 4B is —.

Llama 4 Scout 17B 16E: tool calling and image input. Ternary Bonsai 4B: none of these features are definitively sourced. Feature support does not establish relative quality.

Llama 4 Scout 17B 16E has 1 sourced provider route; Ternary Bonsai 4B has 0, so Llama 4 Scout 17B 16E has broader tracked availability.

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.