DeepSeek V3.2 vs Kimi K2.5
DeepSeekMoonshot AIboth via one key, provider price + 3%
DeepSeek V3.2 and Kimi K2.5 are both recent releases (December 2025 vs. late January 2026) aimed at coding and agentic use, but they land in different spots on the cost-vs-capability curve. DeepSeek V3.2 is text-only, cheaper, and reasoning is off by default. Kimi K2.5 adds image input, reasoning on by default, a bigger context window, and higher Design Arena scores across the board — at roughly 2.6x the input price and nearly 6x the output price.
Spec vs spec
| Spec | DeepSeek V3.2 | Kimi K2.5 |
|---|---|---|
| Context window | 131K | 262K |
| Max output | 64K | — |
| Input modalities | text | text, image |
| Output modalities | text | text |
| Released | Dec 1, 2025 | Jan 27, 2026 |
| 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%
moonshotai/kimi-k2.5
Input · 1M tokens
$0.375 + 3%$0.386
Output · 1M tokens
$2.02 + 3%$2.09
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-k2.5
$8.03
$7.80 provider + 3%
Benchmarks
Design Arena categories where both models have results. Higher Elo and lower rank win.
| DeepSeek V3.2 | Kimi K2.5 | |||
|---|---|---|---|---|
| Category | Elo | Rank | Elo | Rank |
| 3D | 1210 | #41 | 1286 | #22 |
| ASCII art | 1129 | #42 | 1214 | #17 |
| Code | 1213 | #48 | 1286 | #20 |
| Data viz | 1203 | #48 | 1270 | #21 |
| Game dev | 1197 | #50 | 1272 | #23 |
| SVG | 1089 | #54 | 1210 | #25 |
| UI components | 1203 | #47 | 1290 | #19 |
| Websites | 1217 | #46 | 1291 | #16 |
Head-to-head preference voting. How we filter and rank
Pricing math
On OpenKey, provider list price gets a flat 3% fee: DeepSeek V3.2 runs $0.2288/M input and $0.3432/M output from the provider, which becomes $0.235664/M and $0.353496/M on OpenKey (0.2288 × 1.03, 0.3432 × 1.03). Kimi K2.5 is $0.375/M input and $2.025/M output from the provider, or $0.38625/M and $2.08575/M on OpenKey.
For a workload of 10M input tokens and 2M output tokens, DeepSeek V3.2 costs $2.97 total versus $7.80 for Kimi K2.5 — DeepSeek is about 2.6x cheaper on this mix. The input price ratio alone is 0.61 (DeepSeek input is 61% of Kimi's input price). DeepSeek V3.2 also supports cache reads at $0.02288/M; Kimi K2.5 has no listed cache pricing, so repeated-context workloads favor DeepSeek even more.
Coding and design benchmarks
On Design Arena's model-category benchmarks, Kimi K2.5 outranks DeepSeek V3.2 in every shared category: codecategories (rank 20 vs. 48), dataviz (rank 21 vs. 48), gamedev (rank 23 vs. 50), uicomponent (rank 19 vs. 47), website (rank 16 vs. 46), svg (rank 25 vs. 54), and asciiart (rank 17 vs. 42). The one close matchup is 3d, where Kimi K2.5 still ranks better (22 vs. 41) despite DeepSeek V3.2 posting a lower elo gap there than elsewhere (1210 vs. 1286).
Kimi K2.5 also has agent-arena scores DeepSeek V3.2 lacks entirely — androidnative (rank 17), fullstack (rank 14), godotgamedev (rank 2), mobileapps (rank 20), and webapps (rank 15) — which matters if you're routing agentic coding tasks rather than one-shot completions.
Context and modality
Kimi K2.5 supports 262,144 tokens of context against DeepSeek V3.2's 131,072 — exactly double, per the context ratio of 0.5. Kimi K2.5 also accepts image input alongside text; DeepSeek V3.2 is text-only in both directions. If your workload involves long documents, multi-file codebases, or screenshots as input, Kimi K2.5 is the only option of the two. DeepSeek V3.2 caps output at 64,000 tokens; Kimi K2.5 has no listed max-completion limit in this data.
Reasoning behavior
Kimi K2.5 ships with reasoning enabled by default. DeepSeek V3.2 has reasoning available but off by default — you opt in per request. Neither model requires reasoning mode to run. If you're benchmarking latency or cost on a task that doesn't need chain-of-thought, DeepSeek V3.2's default-off setting avoids paying for reasoning tokens you didn't ask for; Kimi K2.5 users should check whether they need to explicitly disable it for simple calls.
Which model for which job
| Use case | Pick | Why |
|---|---|---|
| High-volume text API calls | DeepSeek V3.2 | $2.97 vs $7.80 for a 10M-input/2M-output workload |
| Coding/UI-generation tasks | Kimi K2.5 | Ranks better in every shared Design Arena model category, e.g. codecategories rank 20 vs. 48 |
| Agentic coding pipelines | Kimi K2.5 | Has agent-arena scores DeepSeek V3.2 doesn't, including godotgamedev rank 2 |
| Image-in, text-out tasks | Kimi K2.5 | Only model of the two with image input support |
| Long-document analysis | Kimi K2.5 | 262,144 token context vs. 131,072, double the room |
| Repeated-context / cached prompts | DeepSeek V3.2 | Has cache-read pricing at $0.02288/M; Kimi K2.5 lists none |
Questions
- Which model is cheaper for a typical workload?
- DeepSeek V3.2, by a wide margin. On 10M input and 2M output tokens it costs $2.97 total on OpenKey versus $7.80 for Kimi K2.5 — the input price ratio is 0.61, meaning DeepSeek's input rate is 61% of Kimi's.
- Does Kimi K2.5 actually beat DeepSeek V3.2 on coding benchmarks?
- Yes, on every shared Design Arena category. In codecategories, Kimi K2.5 ranks 20th versus DeepSeek V3.2's 48th. In website generation, Kimi K2.5 ranks 16th versus 46th. The gap holds across all eight comparable categories.
- Which model supports image input?
- Only Kimi K2.5. It's text+image-to-text with 262,144 tokens of context. DeepSeek V3.2 is text-to-text only, capped at 131,072 tokens of context and 64,000 tokens of output.
- Do both models work with the same API key?
- Yes. Both DeepSeek V3.2 and Kimi K2.5 run on OpenKey with one API key across all 329 models, and pricing is the provider's list price plus a flat 3% fee — no separate accounts or billing per lab.