Kimi K2.5 vs GPT-5.2

Design Arena results: Kimi sweeps

Across the 13 Design Arena categories where both models have scores, Kimi K2.5 outranks GPT-5.2 in every single one — agents and models arenas both. In godotgamedev, Kimi ranks #2 (elo 1254) against GPT-5.2's #13 (elo 1183). In fullstack agents, Kimi is #14 (elo 1182, 54.2% win rate) versus GPT-5.2's #21 (elo 1110, 44% win rate). Even in categories where GPT-5.2 posts respectable numbers, like website (elo 1237, rank #34) and dataviz (elo 1245, rank #31), Kimi still leads: website elo 1291 at rank #16, dataviz elo 1270 at rank #21. There's no category in this data where GPT-5.2 comes out ahead.

Pricing: a 10M-in/2M-out workload

Provider pricing: Kimi K2.5 runs $0.375/M input and $2.025/M output; GPT-5.2 runs $1.75/M input and $14.00/M output. On OpenKey, that's provider price × 1.03: Kimi at $0.38625/M input and $2.08575/M output, GPT-5.2 at $1.8025/M input and $14.42/M output. For a 10M-input / 2M-output workload, Kimi K2.5 costs $7.80 total versus GPT-5.2's $45.50 — nearly 6x more. The input price ratio alone is 0.21, meaning GPT-5.2's input tokens cost roughly 4.7x Kimi's. GPT-5.2 does offer cache-read pricing at $0.175/M, which Kimi doesn't list, so repeated-context workloads narrow the gap somewhat.

Context and modality

GPT-5.2 supports a 400,000-token context window with a 128,000-token max completion, plus text, image, and file inputs. Kimi K2.5 caps at 262,144 tokens (a context ratio of 0.66 relative to GPT-5.2) with text and image input only, no file modality, and no published max completion cap. If your workload involves ingesting large PDFs or multi-file document sets, GPT-5.2's file input and larger window matter more than the benchmark gap. For codebases or chat sessions under 262K tokens, the context difference won't bite.

Reasoning controls

GPT-5.2 exposes explicit reasoning effort levels — xhigh, high, medium, low, none — defaulting to medium, giving you a dial to trade latency for depth. Kimi K2.5's reasoning is not mandatory but is enabled by default, without the granular effort tiers GPT-5.2 offers. If you need to throttle reasoning cost per request programmatically, GPT-5.2's parameter surface (`reasoning`, `include_reasoning`) gives finer control, though Kimi supports a wider set of sampling parameters overall, including `top_k`, `min_p`, and `logprobs`, which GPT-5.2 doesn't expose.

When to pick each

Kimi K2.5 is the default choice for agentic coding, game dev tooling, web/app generation, and any high-volume pipeline where the 6x cost gap compounds fast. GPT-5.2 earns its place when you need the 400K context window, file-input handling, or fine-grained reasoning-effort control for latency-sensitive production paths. Given Kimi's clean sweep of the shared Design Arena categories at a sixth of the cost, GPT-5.2's case rests almost entirely on context size and modality, not raw benchmark performance.

Questions

Is Kimi K2.5 cheaper than GPT-5.2 on OpenKey?

Yes, by a wide margin. OpenKey pricing (provider price x 1.03) puts Kimi K2.5 at $0.38625/M input and $2.08575/M output, versus GPT-5.2 at $1.8025/M input and $14.42/M output. A 10M-input/2M-output workload runs $7.80 on Kimi versus $45.50 on GPT-5.2.

Does Kimi K2.5 actually beat GPT-5.2 on benchmarks?

In this dataset, yes, across all 13 Design Arena categories both models share. Examples: godotgamedev rank #2 (elo 1254) vs GPT-5.2's #13 (elo 1183), and website rank #16 (elo 1291) vs GPT-5.2's #34 (elo 1237).

Which model has the bigger context window?

GPT-5.2, at 400,000 tokens versus Kimi K2.5's 262,144 tokens — a context ratio of 0.66. GPT-5.2 also supports a 128,000-token max completion and accepts file inputs, which Kimi K2.5 does not.

Can I use both models through one API key?

Yes. Both Kimi K2.5 and GPT-5.2 are available on OpenKey with a single API key across OpenKey's full catalog, and pricing is provider list price plus a flat 3% fee on both models.