GPT-4o vs Llama-3.2-90B-Vision-Instruct

GPT-4o is the better all-round choice, scoring 51/100 against Llama-3.2-90B-Vision-Instruct (44). It leads on capability and inputs & features. The score weighs capability 67%, inputs & features 20%, context window 13%.

GPT-4o is cheaper at $2.50 input / $10.00 output per million tokens (official OpenAI API price). . At a typical mix of three input tokens to one output token, that is $4.38 per million tokens for GPT-4o versus . Llama-3.2-90B-Vision-Instruct has no published per-token price.

GPT-4o scores higher on the Capabilities Index (ECI): GPT-4o 129.0 (#141 of 165) and Llama-3.2-90B-Vision-Instruct 125.5 (#150 of 165). The confidence ranges of the top two overlap (124.7–131.3 vs 120.3–127.4), so treat the gap as small. On individual benchmarks: GPQA Diamond — GPT-4o 48.9%, Llama-3.2-90B-Vision-Instruct 41.0%; OTIS Mock AIME 2024–2025 — GPT-4o 6.3%, Llama-3.2-90B-Vision-Instruct 2.6%.

There are no published SWE-bench Verified results for GPT-4o and Llama-3.2-90B-Vision-Instruct yet, so there is no like-for-like coding score. On overall capability, GPT-4o leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

GPT-4o and Llama-3.2-90B-Vision-Instruct share the same 128,000-token context window. Maximum output per response: GPT-4o up to 16,384, Llama-3.2-90B-Vision-Instruct up to 4,096 tokens.

GPT-4o accepts text, images and PDFs; Llama-3.2-90B-Vision-Instruct accepts text and images. GPT-4o handles the widest range of inputs.

Llama-3.2-90B-Vision-Instruct publishes its weights (Llama 3.2 Community License) and can be self-hosted; GPT-4o is proprietary.

Llama-3.2-90B-Vision-Instruct is the newest, released Sep 25, 2024. GPT-4o came out May 13, 2024. Knowledge cutoff: GPT-4o Sep 2023, Llama-3.2-90B-Vision-Instruct Dec 2023.

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.