DeepSeek-R1 vs DeepSeek R1 0528 Qwen3 8B vs Kimi K2 Instruct
DeepSeek R1 0528 Qwen3 8B comes out ahead, 58 to 36 and 34 on our weighted score, and it is the cheaper option too.
DeepSeekDeepSeek-R1
34/100- ECI138.8
- Price$0.70 / $4.88
- Context128K
- Our pickDeepSeek
DeepSeek R1 0528 Qwen3 8B
58/100- ECI—
- Price$0.06 / $0.09
- Context131K
Moonshot AIKimi K2 Instruct
36/100- ECI139.9
- Price$0.57 / $2.30
- Context131K
DeepSeek R1 0528 Qwen3 8B is our pick
DeepSeek R1 0528 Qwen3 8B is the better all-round choice, scoring 58/100 against Kimi K2 Instruct (36) and DeepSeek-R1 (34). It leads on price. DeepSeek-R1 wins on inputs & features. 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 0528 Qwen3 8BDeepSeek R1 0528 Qwen3 8B $0.068 · Kimi K2 Instruct $1.00 · DeepSeek-R1 $1.75 per 1M tokens (3:1 blend)
- Longest contextDeepSeek R1 0528 Qwen3 8B and Kimi K2 InstructDeepSeek R1 0528 Qwen3 8B 131,072 · Kimi K2 Instruct 131,072 · DeepSeek-R1 128,000 tokens
- Widest inputsSame inputsDeepSeek-R1: Text · DeepSeek R1 0528 Qwen3 8B: Text · Kimi K2 Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | DeepSeek-R1 | DeepSeek R1 0528 Qwen3 8B | Kimi K2 Instruct |
|---|---|---|---|---|
| Price | 50% | 37 | 100 | 48 |
| Inputs & features | 30% | 35 | 10 | 25 |
| Context window | 20% | 24 | 25 | 25 |
| Overall | 100% | 34/100 | 58/100 | 36/100 |
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-R1DeepSeek | DeepSeek R1 0528 Qwen3 8BDeepSeek | Kimi K2 InstructMoonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 138.8 | — | 139.9 (best) |
| ECI rank | #119 of 165 | — | #112 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 71.7% (best) | 9.3% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 53.3% (best) | 43.9% | — |
| Price per million tokens | |||
| Input | $0.70 | $0.06 (best) | $0.57 |
| Output | $4.88 | $0.09 (best) | $2.30 |
| Cached input | — | — | — |
| Blended (3:1) | $1.75 | $0.068 (best) | $1.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 11 providers | Median of 1 providers | Median of 11 providers |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Max output | 32,768 tokens (best) | 32,000 tokens | 32,768 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenMIT | OpenModified MIT |
| API model ID | — | — | — |
| API providers | 12 (best) | 1 | 11 |
| Released | Jan 20, 2025 | May 29, 2025 | Jul 11, 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.
- DeepSeek-R1$16.76
- DeepSeek R1 0528 Qwen3 8B$0.78
- Kimi K2 Instruct$10.30
Which should you choose?
Which is better: DeepSeek-R1, DeepSeek R1 0528 Qwen3 8B or Kimi K2 Instruct?
DeepSeek R1 0528 Qwen3 8B is the better all-round choice, scoring 58/100 against Kimi K2 Instruct (36) and DeepSeek-R1 (34). It leads on price. DeepSeek-R1 wins on inputs & features. 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, DeepSeek R1 0528 Qwen3 8B or Kimi K2 Instruct?
DeepSeek R1 0528 Qwen3 8B is cheaper at $0.06 input / $0.09 output per million tokens (median across 1 API provider). Kimi K2 Instruct costs $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 $0.068 per million tokens for DeepSeek R1 0528 Qwen3 8B versus $1.00 for Kimi K2 Instruct (15× as much) and $1.75 for DeepSeek-R1 (26× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. DeepSeek-R1 has an ECI of 138.8, DeepSeek R1 0528 Qwen3 8B has not been scored yet and Kimi K2 Instruct has an ECI of 139.9.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek-R1, DeepSeek R1 0528 Qwen3 8B and Kimi K2 Instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that DeepSeek R1 0528 Qwen3 8B does not support tool calling, which most coding agents need.
Which has the bigger context window?
DeepSeek R1 0528 Qwen3 8B and Kimi K2 Instruct have the largest context windows (131,072 and 131,072 tokens), against 128,000 for DeepSeek-R1. Maximum output per response: DeepSeek-R1 up to 32,768, DeepSeek R1 0528 Qwen3 8B up to 32,000, Kimi K2 Instruct up to 32,768 tokens.
Which can read images, PDFs, audio or video?
DeepSeek-R1 accepts text; DeepSeek R1 0528 Qwen3 8B accepts text; Kimi K2 Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (MIT and Modified MIT), so you can self-host them.
Which is newer?
Kimi K2 Instruct is the newest, released Jul 11, 2025. DeepSeek R1 0528 Qwen3 8B came out May 29, 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.
Related comparisons
- DeepSeek-R1 vs GPT-4.1OpenAI · ECI 136.7
- DeepSeek-R1 vs QwQ 32BAlibaba (Qwen) · ECI 137.5
- DeepSeek-R1 vs Gemini 2.5 FlashGoogle · ECI 140.6
- DeepSeek R1 0528 Qwen3 8B vs Baichuan M2 32BBaichuan
- DeepSeek R1 0528 Qwen3 8B vs Command R7B ArabicCohere
- DeepSeek R1 0528 Qwen3 8B vs Phi-4-multimodal-instructMicrosoft
