DeepSeek V3.2 vs GPT-5.2
DeepSeek V3.2 and GPT-5.2 sit at opposite ends of the price-performance curve. DeepSeek V3.2 (released 2025-12-01) is built for efficient reasoning and tool use at a fraction of the cost. GPT-5.2 (released 2025-12-10) is OpenAI's frontier model with adaptive reasoning effort, a much larger context window, and multimodal input. Both are available on OpenKey with one API key and a flat 3% fee over provider list price — no separate accounts, no markup games.
Spec vs spec
| Spec | DeepSeek V3.2 | GPT-5.2 |
|---|---|---|
| Context window | 131K | 400K |
| Max output | 64K | 128K |
| Input modalities | text | file, image, text |
| Output modalities | text | text |
| Released | Dec 1, 2025 | Dec 10, 2025 |
| Reasoning | optional | optional |
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
Input · 1M tokens
$1.75 + 3%$1.80
Output · 1M tokens
$14.00 + 3%$14.42
Cache read · 1M tokens
$0.175 + 3%$0.180
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
$46.87
$45.50 provider + 3%
Benchmarks
Design Arena categories where both models have results. Higher Elo and lower rank win.
| DeepSeek V3.2 | GPT-5.2 | |||
|---|---|---|---|---|
| Category | Elo | Rank | Elo | Rank |
| 3D | 1210 | #41 | 1156 | #60 |
| ASCII art | 1129 | #42 | 1199 | #22 |
| Code | 1213 | #48 | 1219 | #42 |
| Data viz | 1203 | #48 | 1245 | #31 |
| Game dev | 1197 | #50 | 1260 | #29 |
| SVG | 1089 | #54 | 1197 | #31 |
| UI components | 1203 | #47 | 1243 | #32 |
| Websites | 1217 | #46 | 1237 | #34 |
Head-to-head preference voting. How we filter and rank
Pricing math on a real workload
Provider list price for DeepSeek V3.2 is $0.2288/M input and $0.3432/M output; on OpenKey that's $0.235664/M input and $0.353496/M output after the 3% fee. GPT-5.2 lists at $1.75/M input and $14.00/M output, which becomes $1.8025/M and $14.42/M on OpenKey.
Run the numbers on a 10M-input/2M-output job: DeepSeek V3.2 costs $2.97 total, GPT-5.2 costs $45.50. That's roughly a 15x gap, driven mostly by output pricing — GPT-5.2's completion rate alone is over 40x DeepSeek V3.2's. If you're generating a lot of tokens (long completions, multi-turn agents), that multiplier compounds fast. DeepSeek V3.2 also has a cheaper cache-read rate ($0.02288/M vs GPT-5.2's $0.175/M), which matters if you're doing repeated-context calls.
Design and coding benchmarks
Both models are scored on Design Arena. In the shared "models" arena, GPT-5.2 leads on dataviz (elo 1245, rank 31 vs DeepSeek's 1203, rank 48), gamedev (elo 1260, rank 29 vs 1197, rank 50), and UI components (elo 1243, rank 32 vs 1203, rank 47). DeepSeek V3.2 is competitive on website design (elo 1217, rank 46 vs GPT-5.2's elo 1237, rank 34 — GPT-5.2 still ahead) and code categories (elo 1213, rank 48 vs GPT-5.2's 1219, rank 42, a near tie).
GPT-5.2 also has scores in the "agents" arena — fullstack (elo 1110, rank 21), webapps (elo 1156, rank 19), mobile apps (elo 1173, rank 23) — where DeepSeek V3.2 has no reported data, so if agentic coding benchmarks matter to your decision, GPT-5.2 is the only one with numbers to check.
Context window and modality
GPT-5.2 supports a 400,000-token context window and 128,000 max completion tokens. DeepSeek V3.2 caps at 131,072 context and 64,000 max completion tokens — GPT-5.2's context is roughly 3x larger (DeepSeek is about 0.33x of GPT-5.2's window). If you're processing long documents, large codebases, or need big output budgets, GPT-5.2 has more room.
Modality is also a hard split: GPT-5.2 accepts text, image, and file input and outputs text (text+image+file->text). DeepSeek V3.2 is text-only in and out. If your workload involves screenshots, PDFs, or any non-text input, DeepSeek V3.2 is not an option regardless of price.
Reasoning controls
GPT-5.2 exposes explicit reasoning effort levels — xhigh, high, medium, low, none — with medium as the default, letting you dial compute up or down per request. DeepSeek V3.2 supports a reasoning parameter but it's not mandatory and isn't enabled by default, with no effort tiers exposed. If you need fine-grained control over how hard the model thinks (and pays for thinking), GPT-5.2's tiered system gives you more knobs; DeepSeek V3.2 is closer to on/off.
Which model for which job
| Use case | Pick | Why |
|---|---|---|
| High-volume batch generation | DeepSeek V3.2 | Costs $2.97 vs $45.50 on a 10M-in/2M-out workload — roughly 15x cheaper |
| Long-document analysis | GPT-5.2 | 400,000-token context vs DeepSeek's 131,072 — about 3x the room |
| Image or file-based input | GPT-5.2 | Only GPT-5.2 accepts image and file input; DeepSeek V3.2 is text-only |
| Website design generation | GPT-5.2 | Elo 1237 (rank 34) vs DeepSeek's elo 1217 (rank 46) on Design Arena website category |
| Cost-sensitive agentic tool use | DeepSeek V3.2 | Cache-read pricing of $0.02288/M is far below GPT-5.2's $0.175/M for repeated context |
| Dialing reasoning effort per request | GPT-5.2 | Exposes five effort tiers (xhigh to none); DeepSeek V3.2 only toggles reasoning on/off |
Questions
- Which model is cheaper to run at scale?
- DeepSeek V3.2, by a wide margin. On a 10M-input/2M-output workload it costs $2.97 total versus $45.50 for GPT-5.2 — about 15x less. The gap comes mostly from output pricing: $0.353496/M on OpenKey for DeepSeek V3.2 versus $14.42/M for GPT-5.2.
- Which has the bigger context window?
- GPT-5.2, with 400,000 tokens versus DeepSeek V3.2's 131,072 — roughly 3x larger. GPT-5.2 also allows up to 128,000 max completion tokens compared to DeepSeek V3.2's 64,000, so it has more headroom on both ends.
- Does DeepSeek V3.2 support images or files?
- No. DeepSeek V3.2 is text-to-text only. GPT-5.2 accepts text, image, and file input (text+image+file->text), so it's the only choice of the two for multimodal workloads.
- How do they compare on coding benchmarks?
- They're close on Design Arena's code-categories score: GPT-5.2 scores elo 1219 (rank 42) versus DeepSeek V3.2's elo 1213 (rank 48). GPT-5.2 also has agent-specific benchmarks like fullstack (elo 1110) that DeepSeek V3.2 doesn't report.