DeepSeek V3.2 vs Kimi K2 Thinking
DeepSeekMoonshot AIboth via one key, provider price + 3%
DeepSeek V3.2 and Kimi K2 Thinking are both recent text-only models built for coding and agentic tool use, but they land in different price and reasoning tiers. DeepSeek V3.2 (released Dec 2025) uses optional sparse attention reasoning and costs a fraction of Kimi K2 Thinking (released Nov 2025), which bakes mandatory reasoning into every response and charges accordingly. The gap shows up directly in per-token pricing, context length, and how each model scored on Design Arena's website-building benchmark.
Spec vs spec
| Spec | DeepSeek V3.2 | Kimi K2 Thinking |
|---|---|---|
| Context window | 131K | 262K |
| Max output | 64K | 100K |
| Input modalities | text | text |
| Output modalities | text | text |
| Released | Dec 1, 2025 | Nov 6, 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%
moonshotai/kimi-k2-thinking
Input · 1M tokens
$0.600 + 3%$0.618
Output · 1M tokens
$2.50 + 3%$2.58
Cache read · 1M tokens
$0.150 + 3%$0.154
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%
moonshotai/kimi-k2-thinking
$11.33
$11.00 provider + 3%
Benchmarks
Design Arena categories where both models have results. Higher Elo and lower rank win.
| DeepSeek V3.2 | Kimi K2 Thinking | |||
|---|---|---|---|---|
| Category | Elo | Rank | Elo | Rank |
| Websites | 1217 | #46 | 1156 | #70 |
Head-to-head preference voting. How we filter and rank
Pricing math on a real workload
Run 10M input tokens and 2M output tokens through both and the difference is stark. DeepSeek V3.2 on OpenKey costs $0.235664/M input and $0.353496/M output (provider price $0.2288 and $0.3432 respectively, ×1.03 for the flat fee) — that workload totals **$2.97**. Kimi K2 Thinking on OpenKey runs $0.618/M input and $2.575/M output (provider $0.60 and $2.50 ×1.03) — the same workload costs **$11.00**, roughly 3.7x more. DeepSeek's input price alone is 0.38x Kimi's (input_price_ratio: 0.38). If you're doing high-throughput generation — batch code review, content pipelines, agent loops that fire constantly — this ratio compounds fast. Kimi also charges for cache reads at $0.15/M vs DeepSeek's $0.02288/M, so repeated-context workloads widen the gap further.
Context and long-document work
Kimi K2 Thinking has a 262,144-token context window against DeepSeek V3.2's 131,072 — exactly double (context_ratio: 0.5). Kimi also allows a larger single output: 100,352 max completion tokens vs DeepSeek's 64,000. If you're feeding in full codebases, long legal documents, or multi-file diffs and need the model to reason across all of it in one pass, Kimi's window gives you more headroom before you have to chunk or summarize. DeepSeek's smaller window is still large enough for most single-file or moderate-document tasks, but for genuinely long-context agentic runs, Kimi is the safer default.
Reasoning behavior differs by design
This is the core architectural split. DeepSeek V3.2 treats reasoning as optional — `reasoning.mandatory: false`, `default_enabled: false` — so you can run it in a fast, non-reasoning mode for straightforward completions and only pay the reasoning-token tax when you explicitly ask for it. Kimi K2 Thinking has `reasoning.mandatory: true` — every call reasons, no opt-out. That's a deliberate design choice for a model built around long-horizon agentic tasks, but it means you can't dial cost down by skipping reasoning on simple requests the way you can with DeepSeek.
Benchmark data available
Design Arena has broad coverage for DeepSeek V3.2: 3D (elo 1210, rank 41), ASCII art (elo 1129, rank 42), code categories (elo 1213, rank 48), data viz (elo 1203, rank 48), game dev (elo 1197, rank 50), SVG (elo 1089, rank 54), UI components (elo 1203, rank 47), and website (elo 1217, rank 46). Kimi K2 Thinking only has a website score on Design Arena: elo 1156, rank 70 — meaning DeepSeek V3.2 outranks it on that specific category (46 vs 70). Kimi does report Artificial Analysis scores DeepSeek lacks: intelligence index 17.3, coding index 21, agentic index 1.8 — useful if you're weighing agentic-reasoning capability specifically, but not directly comparable to DeepSeek's Design Arena numbers.
When to pick each
Pick DeepSeek V3.2 if cost per token matters, you're generating UI components, websites, or code at volume, or you want the option to skip reasoning overhead on simpler calls. Pick Kimi K2 Thinking if your task requires reasoning on every request by design, you're working with documents or codebases that exceed 131K tokens, or you need the larger 100,352-token output ceiling for long generated artifacts. Both models run on OpenKey under one API key with a flat 3% fee on top of provider list price, so switching between them for A/B testing doesn't require separate accounts or billing setups.
Which model for which job
| Use case | Pick | Why |
|---|---|---|
| High-volume code generation at low cost | DeepSeek V3.2 | 10M-in/2M-out workload costs $2.97 vs $11.00 on Kimi |
| Long-document or full-codebase reasoning | Kimi K2 Thinking | 262,144-token context, double DeepSeek's 131,072 |
| Website/UI generation | DeepSeek V3.2 | Design Arena website rank 46 vs Kimi's rank 70 |
| Tasks needing forced multi-step reasoning every call | Kimi K2 Thinking | reasoning.mandatory is true by default, no opt-out needed |
| Cost-sensitive batch pipelines with simple completions | DeepSeek V3.2 | reasoning is optional (default_enabled: false), so you skip the reasoning-token cost |
| Generating very long output artifacts in one pass | Kimi K2 Thinking | 100,352 max completion tokens vs DeepSeek's 64,000 |
Questions
- Which model is cheaper for high-volume use?
- DeepSeek V3.2, by a wide margin. On OpenKey a 10M input / 2M output workload costs $2.97 with DeepSeek V3.2 versus $11.00 with Kimi K2 Thinking — DeepSeek's input price is roughly 0.38x Kimi's per the ratio in the pricing data.
- Which has the bigger context window?
- Kimi K2 Thinking, with 262,144 tokens versus DeepSeek V3.2's 131,072 — exactly double, per the computed context ratio of 0.5. Kimi also supports a larger max output at 100,352 tokens against DeepSeek's 64,000.
- Does either model force reasoning mode on?
- Yes, Kimi K2 Thinking does — its reasoning field is marked mandatory: true. DeepSeek V3.2's reasoning is optional and disabled by default, so you control when the reasoning-token cost applies.
- How do they compare on Design Arena benchmarks?
- DeepSeek V3.2 has scores across eight categories, including website (elo 1217, rank 46) and code categories (elo 1213, rank 48). Kimi K2 Thinking only has a website score: elo 1156, rank 70 — lower than DeepSeek's on that same category.