GPT-5.4 nano vs Kimi K2 Thinking

GPT-5.4 nano is the better all-round choice, scoring 68/100 against Kimi K2 Thinking (58). It leads on price, inputs & features and context window. 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). Kimi K2 Thinking costs $0.60 input / $2.50 output per million tokens (median across 10 API providers). 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 $1.07 for Kimi K2 Thinking (2.3× as much).

Kimi K2 Thinking scores higher on the Capabilities Index (ECI): Kimi K2 Thinking 146.0 (#72 of 148) and GPT-5.4 nano 145.8 (#75 of 148). The confidence ranges of the top two overlap (143.4–147.6 vs 143.2–147.7), so treat the gap as small. On individual benchmarks: GPQA Diamond — Kimi K2 Thinking 84.2%, GPT-5.4 nano 78.5%; OTIS Mock AIME 2024–2025 — GPT-5.4 nano 87.8%, Kimi K2 Thinking 83.1%.

There are no published SWE-bench Verified results for GPT-5.4 nano and Kimi K2 Thinking yet, so there is no like-for-like coding score. On overall capability, Kimi K2 Thinking 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 262,144 for Kimi K2 Thinking. Maximum output per response: GPT-5.4 nano up to 128,000, Kimi K2 Thinking up to 262,144 tokens.

GPT-5.4 nano accepts text and images; Kimi K2 Thinking accepts text. GPT-5.4 nano handles the widest range of inputs.

Kimi K2 Thinking publishes its weights and can be self-hosted; GPT-5.4 nano is proprietary.

GPT-5.4 nano is the newest, released Mar 17, 2026. Kimi K2 Thinking came out Nov 6, 2025. Knowledge cutoff: GPT-5.4 nano Aug 31, 2025, Kimi K2 Thinking 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.