GPT-4o vs Gemma 2 27B

GPT-4o is the better all-round choice, scoring 43/100 against Gemma 2 27B (35). It leads on capability, inputs & features and context window. Gemma 2 27B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Gemma 2 27B is cheaper at $0.65 input / $0.65 output per million tokens (median across 3 API providers). GPT-4o costs $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 $0.65 per million tokens for Gemma 2 27B versus $4.38 for GPT-4o (6.7× as much).

GPT-4o scores higher on the Capabilities Index (ECI): GPT-4o 129.0 (#141 of 165) and Gemma 2 27B 122.1 (#152 of 165). Their confidence ranges do not overlap (124.7–131.3 vs 115.8–124.1), so the gap is a real one. On individual benchmarks: GPQA Diamond — GPT-4o 48.9%, Gemma 2 27B 36.5%; OTIS Mock AIME 2024–2025 — GPT-4o 6.3%, Gemma 2 27B 1.4%.

There are no published SWE-bench Verified results for GPT-4o and Gemma 2 27B 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. Note that Gemma 2 27B does not support tool calling, which most coding agents need.

GPT-4o has the largest context window at 128,000 tokens, against 8,192 for Gemma 2 27B. Maximum output per response: GPT-4o up to 16,384, Gemma 2 27B up to 2,048 tokens.

GPT-4o accepts text, images and PDFs; Gemma 2 27B accepts text. GPT-4o handles the widest range of inputs.

Gemma 2 27B publishes its weights and can be self-hosted; GPT-4o is proprietary.

Gemma 2 27B is the newest, released Jul 13, 2024. GPT-4o came out May 13, 2024. Knowledge cutoff: GPT-4o Sep 2023, Gemma 2 27B Jun 30, 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.