DeepSeek V3.2 vs GPT-5.2 Pro
DeepSeek V3.2 and GPT-5.2 Pro sit at opposite ends of the cost spectrum. DeepSeek V3.2 (released Dec 1, 2025) is a sparse-attention model built for efficient reasoning and tool use at a low price. GPT-5.2 Pro (Dec 10, 2025) is OpenAI's top-end model, built for long-context, multimodal, step-by-step reasoning work — and priced accordingly. Both run on OpenKey behind one key with a flat 3% fee on top of provider list price, so the numbers below are what you'd actually pay.
Spec vs spec
| Spec | DeepSeek V3.2 | GPT-5.2 Pro |
|---|---|---|
| Context window | 131K | 400K |
| Max output | 64K | 128K |
| Input modalities | text | image, text, file |
| Output modalities | text | text |
| Released | Dec 1, 2025 | Dec 10, 2025 |
| Reasoning | optional | always on |
Pricing
Per 1M tokens. Provider price plus the flat 3% fee — the sum is what you pay.
deepseek/deepseek-v3.2
Input · 1M tokens
$0.229 + 3%$0.236
Output · 1M tokens
$0.343 + 3%$0.353
Cache read · 1M tokens
$0.023 + 3%$0.024
FEE — FLAT, EVERY MODEL3%
openai/gpt-5.2-pro
Input · 1M tokens
$21.00 + 3%$21.63
Output · 1M tokens
$168.00 + 3%$173.04
FEE — FLAT, EVERY MODEL3%
One workload, priced on both
10M input + 2M output tokens at each model's price, flat 3% fee included.
deepseek/deepseek-v3.2Cheaper
$3.06
$2.97 provider + 3%
openai/gpt-5.2-pro
$562.38
$546.00 provider + 3%
Pricing math: what a real workload costs
OpenKey price = provider price × 1.03. DeepSeek V3.2 runs $0.2288/M input and $0.3432/M output at the provider, so on OpenKey that's $0.235664/M in and $0.353496/M out. GPT-5.2 Pro runs $21.00/M input and $168.00/M output at the provider, or $21.63/M in and $173.04/M out on OpenKey.
For a workload of 10M input tokens + 2M output tokens: DeepSeek V3.2 costs $2.97 total. GPT-5.2 Pro costs $546.00 for the same workload — roughly 184x more. The input price ratio alone is 0.01 (DeepSeek is about 1% of GPT-5.2 Pro's input price). If you're running batch jobs, agent loops, or anything at scale, this gap compounds fast.
Coding and generation benchmarks
DeepSeek V3.2 has Design Arena rankings across eight categories: codecategories (elo 1213, rank 48), UI components (elo 1203, rank 47), website (elo 1217, rank 46), dataviz (elo 1203, rank 48), gamedev (elo 1197, rank 50), 3D (elo 1210, rank 41), ASCII art (elo 1129, rank 42), and SVG (elo 1089, rank 54). Win rates cluster in the 41-51% range — solidly mid-pack, not top-tier, but usable for real coding tasks at a fraction of the cost.
GPT-5.2 Pro has no Design Arena data in this comparison, so there's no apples-to-apples elo gap to cite. What we do know: it's built with mandatory reasoning (xhigh, high, or medium effort, defaulting to medium), which suggests OpenAI is optimizing for correctness on hard problems rather than throughput on routine ones.
Context window and long documents
GPT-5.2 Pro supports 400,000 tokens of context with 128,000 max output tokens. DeepSeek V3.2 supports 131,072 tokens of context with 64,000 max output. That's a context ratio of 0.33 — DeepSeek gives you about a third of GPT-5.2 Pro's window. If your job is summarizing a codebase, a legal contract set, or a long research corpus that exceeds 131K tokens, GPT-5.2 Pro is the only one of the two that fits it in a single pass without chunking.
Modality and input types
GPT-5.2 Pro accepts text, image, and file inputs and outputs text — it's built for multimodal work like reading screenshots, PDFs, or diagrams. DeepSeek V3.2 is text-to-text only: text in, text out. If your pipeline needs to parse images or uploaded files directly, DeepSeek V3.2 is off the table regardless of price, and GPT-5.2 Pro is your only option here.
When to pick each
Pick DeepSeek V3.2 for high-volume coding assistants, internal tools, dataviz generation, or any agentic loop where you're calling the model thousands of times a day — the $2.97 vs $546.00 gap on a 10M/2M workload isn't marginal, it changes what's economically viable. Pick GPT-5.2 Pro when you need multimodal input, a context window beyond 131K tokens, or when you're willing to pay for its mandatory high-effort reasoning on a problem where the cost of an error outweighs 184x the token spend.
Which model for which job
| Use case | Pick | Why |
|---|---|---|
| High-volume coding agent | DeepSeek V3.2 | Costs $2.97 vs $546.00 on a 10M-in/2M-out workload — the volume math doesn't work with GPT-5.2 Pro |
| Reading screenshots or PDFs | GPT-5.2 Pro | Only GPT-5.2 Pro accepts image and file input; DeepSeek V3.2 is text-only |
| Summarizing a 200K-token document | GPT-5.2 Pro | 400,000 token context vs DeepSeek V3.2's 131,072 — the document won't fit in DeepSeek's window |
| UI component and website generation at scale | DeepSeek V3.2 | Design Arena elo 1203-1217 on UI/website categories at roughly 1% of GPT-5.2 Pro's input price |
| High-stakes reasoning where errors are costly | GPT-5.2 Pro | Mandatory reasoning with xhigh effort option built for step-by-step correctness |
| Prototyping and dev/test loops | DeepSeek V3.2 | Cheap enough ($0.235664/M input on OpenKey) to iterate without watching the bill |
Questions
- How much cheaper is DeepSeek V3.2 than GPT-5.2 Pro?
- On a 10M input + 2M output token workload, DeepSeek V3.2 costs $2.97 total while GPT-5.2 Pro costs $546.00 — about 184x more. The input price ratio is 0.01, meaning DeepSeek's input tokens cost roughly 1% of GPT-5.2 Pro's.
- Which model has a bigger context window?
- GPT-5.2 Pro supports 400,000 tokens of context versus DeepSeek V3.2's 131,072 tokens — a context ratio of 0.33. GPT-5.2 Pro also allows 128,000 max output tokens versus DeepSeek's 64,000.
- Can DeepSeek V3.2 handle images or files?
- No. DeepSeek V3.2 is text-to-text only. GPT-5.2 Pro accepts text, image, and file inputs, making it the only choice of the two for multimodal tasks like reading screenshots or PDFs.
- Does OpenKey add extra fees on top of provider pricing?
- OpenKey charges a flat 3% fee on provider list price. DeepSeek V3.2's provider price of $0.2288/M input becomes $0.235664/M on OpenKey; GPT-5.2 Pro's $21.00/M becomes $21.63/M. Same math applies to output pricing for both models.