DeepSeek V3.2 vs Kimi K2 Thinking
DeepSeek V3.2 comes out ahead, 64 to 58 on our weighted score, and it is the cheaper option too.
- Our pick
DeepSeekDeepSeek V3.2
64/100- ECI146.3
- Price$0.296 / $0.48
- Context128K
Moonshot AIKimi K2 Thinking
58/100- ECI146.0
- Price$0.60 / $2.50
- Context262K
Add a model
Make it a three-way comparison.
DeepSeek V3.2 is our pick
DeepSeek V3.2 is the better all-round choice, scoring 64/100 against Kimi K2 Thinking (58). It leads on price and inputs & features. Kimi K2 Thinking wins on context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek V3.2Capabilities Index (ECI): DeepSeek V3.2 146.3 · Kimi K2 Thinking 146.0
- Lowest priceDeepSeek V3.2DeepSeek V3.2 $0.342 · Kimi K2 Thinking $1.07 per 1M tokens (3:1 blend)
- Longest contextKimi K2 ThinkingKimi K2 Thinking 262,144 · DeepSeek V3.2 128,000 tokens
- Widest inputsSame inputsDeepSeek V3.2: Text · Kimi K2 Thinking: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | DeepSeek V3.2 | Kimi K2 Thinking |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 73 |
| Price | 25% | 72 | 48 |
| Inputs & features | 15% | 45 | 35 |
| Context window | 10% | 24 | 37 |
| Overall | 100% | 64/100 | 58/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek V3.2DeepSeek | Kimi K2 ThinkingMoonshot AI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 146.3 (best) | 146.0 |
| ECI rank | #69 of 148 (best) | #72 of 148 |
| GPQA DiamondGraduate-level science questions | 83.4% | 84.2% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 87.8% (best) | 83.1% |
| Price per million tokens | ||
| Input | $0.296 (best) | $0.60 |
| Output | $0.48 (best) | $2.50 |
| Cached input | — | — |
| Blended (3:1) | $0.342 (best) | $1.07 |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 15 providers | Median of 10 providers |
| Limits | ||
| Context window | 128,000 tokens | 262,144 tokens (best) |
| Max output | 64,000 tokens | 262,144 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | Yes |
| Tool calling | Yes | Yes |
| Structured output | Yes | No |
| Availability | ||
| Weights | OpenMIT License | Open |
| API model ID | — | — |
| API providers | 15 (best) | 10 |
| Released | Dec 1, 2025 | Nov 6, 2025 |
| Knowledge cutoff | Jul 2024 | Aug 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- DeepSeek V3.2$3.92
- Kimi K2 Thinking$11.00
Which should you choose?
Which is better: DeepSeek V3.2 or Kimi K2 Thinking?
DeepSeek V3.2 is the better all-round choice, scoring 64/100 against Kimi K2 Thinking (58). It leads on price and inputs & features. Kimi K2 Thinking wins on context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek V3.2 or Kimi K2 Thinking?
DeepSeek V3.2 is cheaper at $0.296 input / $0.48 output per million tokens (median across 15 API providers). Kimi K2 Thinking costs $0.60 input / $2.50 output per million tokens (median across 10 API providers). At a typical mix of three input tokens to one output token, that is $0.342 per million tokens for DeepSeek V3.2 versus $1.07 for Kimi K2 Thinking (3.1× as much).
Which scores higher on benchmarks?
DeepSeek V3.2 scores higher on the Capabilities Index (ECI): DeepSeek V3.2 146.3 (#69 of 148) and Kimi K2 Thinking 146.0 (#72 of 148). The confidence ranges of the top two overlap (144.4–147.5 vs 143.4–147.6), so treat the gap as small. On individual benchmarks: GPQA Diamond — Kimi K2 Thinking 84.2%, DeepSeek V3.2 83.4%; OTIS Mock AIME 2024–2025 — DeepSeek V3.2 87.8%, Kimi K2 Thinking 83.1%.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek V3.2 and Kimi K2 Thinking yet, so there is no like-for-like coding score. On overall capability, DeepSeek V3.2 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.
Which has the bigger context window?
Kimi K2 Thinking has the largest context window at 262,144 tokens, against 128,000 for DeepSeek V3.2. Maximum output per response: DeepSeek V3.2 up to 64,000, Kimi K2 Thinking up to 262,144 tokens.
Which can read images, PDFs, audio or video?
DeepSeek V3.2 accepts text; Kimi K2 Thinking accepts text. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights (MIT License), so you can self-host them.
Which is newer?
DeepSeek V3.2 is the newest, released Dec 1, 2025. Kimi K2 Thinking came out Nov 6, 2025. Knowledge cutoff: DeepSeek V3.2 Jul 2024, Kimi K2 Thinking Aug 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
Related comparisons
- Add GPT-5.4 nanoThree-way comparison
- DeepSeek V3.2 vs GPT-5.4 nanoOpenAI · ECI 145.8
- DeepSeek V3.2 vs MiniMax-M2.5MiniMax · ECI 146.7
- DeepSeek V3.2 vs Qwen3.6 FlashAlibaba (Qwen) · ECI 143.3
- Kimi K2 Thinking vs Qwen3.5 PlusAlibaba (Qwen) · ECI 146.8
- Kimi K2 Thinking vs GLM-4.7Z.ai (Zhipu) · ECI 143.5
- Kimi K2 Thinking vs Nemotron 3 Ultra 550B A55BNVIDIA · ECI 146.2
