DeepSeek R1 0528 vs Kimi K2 Instruct 0905
Too close to call on our weighted score (DeepSeek R1 0528 41, Kimi K2 Instruct 0905 38). The right pick depends on what you value most.
DeepSeekDeepSeek R1 0528
41/100- ECI141.2
- Price$0.512 / $2.19
- Context164K
Moonshot AIKimi K2 Instruct 0905
38/100- ECI—
- Price$0.60 / $2.50
- Context262K
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Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within 3 points (DeepSeek R1 0528 41/100, Kimi K2 Instruct 0905 38/100), so choose by what matters most for your work: DeepSeek R1 0528 on price and Kimi K2 Instruct 0905 for long inputs. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceDeepSeek R1 0528DeepSeek R1 0528 $0.932 · Kimi K2 Instruct 0905 $1.07 per 1M tokens (3:1 blend)
- Longest contextKimi K2 Instruct 0905Kimi K2 Instruct 0905 262,144 · DeepSeek R1 0528 163,840 tokens
- Widest inputsSame inputsDeepSeek R1 0528: Text · Kimi K2 Instruct 0905: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | DeepSeek R1 0528 | Kimi K2 Instruct 0905 |
|---|---|---|---|
| Price | 50% | 49 | 46 |
| Inputs & features | 30% | 35 | 25 |
| Context window | 20% | 28 | 37 |
| Overall | 100% | 41/100 | 38/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek R1 0528DeepSeek | Kimi K2 Instruct 0905Moonshot AI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 141.2 | — |
| ECI rank | #108 of 165 | — |
| GPQA DiamondGraduate-level science questions | 76.3% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 66.4% | — |
| Price per million tokens | ||
| Input | $0.512 (best) | $0.60 |
| Output | $2.19 (best) | $2.50 |
| Cached input | — | — |
| Blended (3:1) | $0.932 (best) | $1.07 |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 16 providers | Median of 12 providers |
| Limits | ||
| Context window | 163,840 tokens | 262,144 tokens (best) |
| Max output | 32,768 tokens | 98,304 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | No |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | OpenMIT | OpenModified MIT |
| API model ID | — | — |
| API providers | 16 (best) | 13 |
| Released | May 28, 2025 | Sep 5, 2025 |
| Knowledge cutoff | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- DeepSeek R1 0528$9.50
- Kimi K2 Instruct 0905$11.00
Which should you choose?
Which is better: DeepSeek R1 0528 or Kimi K2 Instruct 0905?
It is close. Our weighted score puts them within 3 points (DeepSeek R1 0528 41/100, Kimi K2 Instruct 0905 38/100), so choose by what matters most for your work: DeepSeek R1 0528 on price and Kimi K2 Instruct 0905 for long inputs. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, DeepSeek R1 0528 or Kimi K2 Instruct 0905?
DeepSeek R1 0528 is cheaper at $0.512 input / $2.19 output per million tokens (median across 16 API providers). Kimi K2 Instruct 0905 costs $0.60 input / $2.50 output per million tokens (median across 12 API providers). At a typical mix of three input tokens to one output token, that is $0.932 per million tokens for DeepSeek R1 0528 versus $1.07 for Kimi K2 Instruct 0905 (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. DeepSeek R1 0528 has an ECI of 141.2 and Kimi K2 Instruct 0905 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek R1 0528 and Kimi K2 Instruct 0905 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
Kimi K2 Instruct 0905 has the largest context window at 262,144 tokens, against 163,840 for DeepSeek R1 0528. Maximum output per response: DeepSeek R1 0528 up to 32,768, Kimi K2 Instruct 0905 up to 98,304 tokens.
Which can read images, PDFs, audio or video?
DeepSeek R1 0528 accepts text; Kimi K2 Instruct 0905 accepts text. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights (MIT and Modified MIT), so you can self-host them.
Which is newer?
Kimi K2 Instruct 0905 is the newest, released Sep 5, 2025. DeepSeek R1 0528 came out May 28, 2025.
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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