GPT-4o mini vs Llama 4 Scout 17B Instruct

Llama 4 Scout 17B Instruct is the better all-round choice, scoring 62/100 against GPT-4o mini (56). It leads on capability and context window. GPT-4o mini wins on price and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

GPT-4o mini is cheaper at $0.15 input / $0.60 output per million tokens (official OpenAI API price). Llama 4 Scout 17B Instruct costs $0.225 input / $0.69 output per million tokens (median across 4 API providers). At a typical mix of three input tokens to one output token, that is $0.263 per million tokens for GPT-4o mini versus $0.341 for Llama 4 Scout 17B Instruct (1.3× as much).

Llama 4 Scout 17B Instruct scores higher on the Capabilities Index (ECI): Llama 4 Scout 17B Instruct 129.7 (#126 of 148) and GPT-4o mini 126.6 (#135 of 148). The confidence ranges of the top two overlap (124.8–131.4 vs 120.5–128.5), so treat the gap as small. On individual benchmarks: GPQA Diamond — Llama 4 Scout 17B Instruct 51.8%, GPT-4o mini 37.7%; OTIS Mock AIME 2024–2025 — Llama 4 Scout 17B Instruct 7.8%, GPT-4o mini 6.9%.

There are no published SWE-bench Verified results for GPT-4o mini and Llama 4 Scout 17B Instruct yet, so there is no like-for-like coding score. On overall capability, Llama 4 Scout 17B Instruct leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

Llama 4 Scout 17B Instruct has the largest context window at 10,000,000 tokens, against 128,000 for GPT-4o mini. Maximum output per response: GPT-4o mini up to 16,384, Llama 4 Scout 17B Instruct up to 16,384 tokens.

GPT-4o mini accepts text, images and PDFs; Llama 4 Scout 17B Instruct accepts text and images. GPT-4o mini handles the widest range of inputs.

Llama 4 Scout 17B Instruct publishes its weights and can be self-hosted; GPT-4o mini is proprietary.

Llama 4 Scout 17B Instruct is the newest, released Apr 5, 2025. GPT-4o mini came out Jul 18, 2024. Knowledge cutoff: GPT-4o mini Sep 2023, Llama 4 Scout 17B Instruct Aug 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 Oct 4, 2026.