GPT-5.4 nano vs o3

GPT-5.4 nano is the better all-round choice, scoring 68/100 against o3 (58). It leads on price and context window. o3 wins on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

GPT-5.4 nano is cheaper at $0.20 input / $1.25 output per million tokens (official OpenAI API price). o3 costs $2.00 input / $8.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $0.463 per million tokens for GPT-5.4 nano versus $3.50 for o3 (7.6× as much).

o3 scores higher on the Capabilities Index (ECI): o3 146.9 (#63 of 148) and GPT-5.4 nano 145.8 (#75 of 148). The confidence ranges of the top two overlap (144.9–148.6 vs 143.2–147.7), so treat the gap as small. On individual benchmarks: GPQA Diamond — o3 81.8%, GPT-5.4 nano 78.5%; FrontierMath Tiers 1–3 — GPT-5.4 nano 44.9%, o3 33.3%; OTIS Mock AIME 2024–2025 — GPT-5.4 nano 87.8%, o3 84.4%; SimpleQA Verified — o3 49.4%, GPT-5.4 nano 11.7%.

There are no published SWE-bench Verified results for GPT-5.4 nano yet, so there is no like-for-like coding score. On overall capability, o3 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

GPT-5.4 nano has the largest context window at 400,000 tokens, against 200,000 for o3. Maximum output per response: GPT-5.4 nano up to 128,000, o3 up to 100,000 tokens.

GPT-5.4 nano accepts text and images; o3 accepts text, images and PDFs. o3 handles the widest range of inputs.

No. GPT-5.4 nano and o3 are proprietary and only available through APIs and apps.

GPT-5.4 nano is the newest, released Mar 17, 2026. o3 came out Apr 16, 2025. Knowledge cutoff: GPT-5.4 nano Aug 31, 2025, o3 May 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.