Kimi K2 Instruct vs DeepSeek-R1
Too close to call on our weighted score (Kimi K2 Instruct 50, DeepSeek-R1 48). The right pick depends on what you value most.
Moonshot AIKimi K2 Instruct
50/100- ECI139.9
- Price$0.57 / $2.30
- Context131K
DeepSeekDeepSeek-R1
48/100- ECI138.8
- Price$0.70 / $4.88
- Context128K
Add a model
Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within 2 points (Kimi K2 Instruct 50/100, DeepSeek-R1 48/100), so choose by what matters most for your work: Kimi K2 Instruct for raw capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityKimi K2 InstructCapabilities Index (ECI): Kimi K2 Instruct 139.9 · DeepSeek-R1 138.8
- Lowest priceKimi K2 InstructKimi K2 Instruct $1.00 · DeepSeek-R1 $1.75 per 1M tokens (3:1 blend)
- Longest contextKimi K2 InstructKimi K2 Instruct 131,072 · DeepSeek-R1 128,000 tokens
- Widest inputsSame inputsKimi K2 Instruct: Text · DeepSeek-R1: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Kimi K2 Instruct | DeepSeek-R1 |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 64 | 63 |
| Price | 25% | 48 | 37 |
| Inputs & features | 15% | 25 | 35 |
| Context window | 10% | 25 | 24 |
| Overall | 100% | 50/100 | 48/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Kimi K2 InstructMoonshot AI | DeepSeek-R1DeepSeek |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 139.9 (best) | 138.8 |
| ECI rank | #112 of 165 (best) | #119 of 165 |
| GPQA DiamondGraduate-level science questions | — | 71.7% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 53.3% |
| Price per million tokens | ||
| Input | $0.57 (best) | $0.70 |
| Output | $2.30 (best) | $4.88 |
| Cached input | — | — |
| Blended (3:1) | $1.00 (best) | $1.75 |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 11 providers | Median of 11 providers |
| Limits | ||
| Context window | 131,072 tokens (best) | 128,000 tokens |
| Max output | 32,768 tokens | 32,768 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | OpenModified MIT | Open |
| API model ID | — | — |
| API providers | 11 | 12 (best) |
| Released | Jul 11, 2025 | Jan 20, 2025 |
| Knowledge cutoff | — | Jul 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Kimi K2 Instruct$10.30
- DeepSeek-R1$16.76
Which should you choose?
Which is better: Kimi K2 Instruct or DeepSeek-R1?
It is close. Our weighted score puts them within 2 points (Kimi K2 Instruct 50/100, DeepSeek-R1 48/100), so choose by what matters most for your work: Kimi K2 Instruct for raw capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Kimi K2 Instruct or DeepSeek-R1?
Kimi K2 Instruct is cheaper at $0.57 input / $2.30 output per million tokens (median across 11 API providers). DeepSeek-R1 costs $0.70 input / $4.88 output per million tokens (median across 11 API providers). At a typical mix of three input tokens to one output token, that is $1.00 per million tokens for Kimi K2 Instruct versus $1.75 for DeepSeek-R1 (1.7× as much).
Which scores higher on benchmarks?
Kimi K2 Instruct scores higher on the Capabilities Index (ECI): Kimi K2 Instruct 139.9 (#112 of 165) and DeepSeek-R1 138.8 (#119 of 165). The confidence ranges of the top two overlap (136.3–141.6 vs 136.5–140.2), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Kimi K2 Instruct and DeepSeek-R1 yet, so there is no like-for-like coding score. On overall capability, Kimi K2 Instruct 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 Instruct has the largest context window at 131,072 tokens, against 128,000 for DeepSeek-R1. Maximum output per response: Kimi K2 Instruct up to 32,768, DeepSeek-R1 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Kimi K2 Instruct accepts text; DeepSeek-R1 accepts text. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights (Modified MIT), so you can self-host them.
Which is newer?
Kimi K2 Instruct is the newest, released Jul 11, 2025. DeepSeek-R1 came out Jan 20, 2025. Knowledge cutoff: DeepSeek-R1 Jul 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 Nov 2, 2026.
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