DeepSeek V3.2 vs GPT-5.2-Codex

Pricing math: what a real workload costs

Provider list price for DeepSeek V3.2 is $0.2288/M input and $0.3432/M output; on OpenKey that's $0.2288 × 1.03 = $0.235664/M input and $0.3432 × 1.03 = $0.353496/M output. GPT-5.2-Codex lists at $1.75/M input and $14.00/M output, which becomes $1.75 × 1.03 = $1.8025/M input and $14.00 × 1.03 = $14.42/M output on OpenKey.

Run the numbers on a 10M-input / 2M-output workload and the gap is stark: $2.97 for DeepSeek V3.2 versus $45.50 for GPT-5.2-Codex. That's a 15.3x difference for the same token volume. The input price ratio alone is 0.13 — Codex input costs about 7.6x more than DeepSeek's. If your workload is mostly volume (RAG, summarization, batch generation), that difference compounds fast.

Coding and agent benchmarks

The two models aren't scored on the same Design Arena boards, which limits direct comparison. DeepSeek V3.2 is measured on the general **models** arena: codecategories (elo 1213, rank 48), website (elo 1217, rank 46), dataviz (elo 1203, rank 48), uicomponent (elo 1203, rank 47), gamedev (elo 1197, rank 50), svg (elo 1089, rank 54), 3d (elo 1210, rank 41), asciiart (elo 1129, rank 42).

GPT-5.2-Codex is measured on the **agents** arena instead — a category built for autonomous coding tasks: godotgamedev (elo 1187, rank 12), androidnative (elo 1176, rank 15), mobileapps (elo 1172, rank 24), webapps (elo 1125, rank 22), fullstack (elo 1060, rank 27). Its rank-12 finish on godotgamedev is its strongest showing. Since the arenas differ, treat this as evidence of specialization, not a head-to-head elo gap.

Context, output limits, and modality

GPT-5.2-Codex has a 400,000-token context window against DeepSeek V3.2's 131,072 — a context ratio of 0.33, meaning DeepSeek holds about a third of Codex's window. Codex also allows up to 128,000 max completion tokens versus 64,000 for DeepSeek, useful for long autonomous coding runs that need to emit a lot of code in one pass.

Modality is a real functional split: GPT-5.2-Codex accepts text and image input (useful for screenshots, diagrams, UI mockups in a coding workflow), while DeepSeek V3.2 is text-only in and out. If your pipeline needs to reason over a screenshot or a design file, DeepSeek is out regardless of price.

Reasoning control and parameters

GPT-5.2-Codex makes reasoning mandatory, with four effort levels (xhigh, high, medium, low) defaulting to medium — you get an explicit dial for how hard it thinks per request. DeepSeek V3.2 supports reasoning but it's optional and off by default, and it exposes a longer list of sampling parameters (temperature, top_k, top_p, min_p, repetition_penalty, logit_bias, logprobs, top_logprobs) that Codex's parameter list doesn't include. If you need fine-grained sampling control, V3.2 gives you more knobs; if you need a straightforward effort-vs-cost tradeoff for coding, Codex's structure is more direct.

Questions

How much cheaper is DeepSeek V3.2 than GPT-5.2-Codex?

On a 10M-input/2M-output workload, DeepSeek V3.2 costs $2.97 on OpenKey versus $45.50 for GPT-5.2-Codex — about 15.3x cheaper. The input price ratio is 0.13, meaning Codex input tokens alone cost roughly 7.6x more than DeepSeek's.

Which model has the bigger context window?

GPT-5.2-Codex supports 400,000 tokens of context versus DeepSeek V3.2's 131,072 — a context ratio of 0.33. If your task involves large codebases or long documents that exceed 131K tokens, Codex is the only option of the two.

Can DeepSeek V3.2 process images?

No. DeepSeek V3.2 is text-in, text-out only. GPT-5.2-Codex accepts text and image input, which matters if your coding workflow includes screenshots, diagrams, or design mockups as context.

Does either model require reasoning mode?

GPT-5.2-Codex makes reasoning mandatory with four effort settings (xhigh, high, medium, low), defaulting to medium. DeepSeek V3.2 supports reasoning too but it's optional and disabled by default, giving you the choice per request.