Llama 4 Scout 17B Instruct vs Phi-4-multimodal-instruct

It is close. Our weighted score puts them within a point (Phi-4-multimodal-instruct 72/100, Llama 4 Scout 17B Instruct 72/100), so choose by what matters most for your work: Phi-4-multimodal-instruct on price and Llama 4 Scout 17B Instruct for long inputs. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.

Phi-4-multimodal-instruct is cheaper at $0.07 input / $0.11 output per million tokens (median across 1 API provider). Llama 4 Scout 17B Instruct costs $0.17 input / $0.59 output per million tokens (median across 13 API providers). At a typical mix of three input tokens to one output token, that is $0.08 per million tokens for Phi-4-multimodal-instruct versus $0.275 for Llama 4 Scout 17B Instruct (3.4× as much).

There is no independent benchmark that covers both models yet. Llama 4 Scout 17B Instruct has an ECI of 129.6 and Phi-4-multimodal-instruct has not been scored yet.

There are no published SWE-bench Verified results for Llama 4 Scout 17B Instruct and Phi-4-multimodal-instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.

Llama 4 Scout 17B Instruct has the largest context window at 10,000,000 tokens, against 131,072 for Phi-4-multimodal-instruct. Maximum output per response: Llama 4 Scout 17B Instruct up to 16,384, Phi-4-multimodal-instruct up to 131,072 tokens.

Llama 4 Scout 17B Instruct accepts text and images; Phi-4-multimodal-instruct accepts text, images and audio. Phi-4-multimodal-instruct handles the widest range of inputs.

Yes, both publish their weights (MIT), so you can self-host them.

Llama 4 Scout 17B Instruct is the newest, released Apr 5, 2025. Phi-4-multimodal-instruct came out Feb 26, 2025. Knowledge cutoff: Llama 4 Scout 17B Instruct Aug 2024, Phi-4-multimodal-instruct Jun 2024.

Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.