Model Comparison

DeepSeek R1 Distill Llama 70B vs Kimi-k1.5

Both models are evenly matched across the benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek R1 Distill Llama 70B outperforms in 1 benchmarks (AIME 2024), while Kimi-k1.5 is better at 1 benchmark (MATH-500).

Both models are evenly matched across the benchmarks.

Mon Apr 06 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Mon Apr 06 2026 • llm-stats.com
DeepSeek
DeepSeek R1 Distill Llama 70B
Input tokens$0.10
Output tokens$0.40
Best providerDeepinfra
Moonshot AI
Kimi-k1.5
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
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Context Window

Maximum input and output token capacity

Only DeepSeek R1 Distill Llama 70B specifies input context (128,000 tokens). Only DeepSeek R1 Distill Llama 70B specifies output context (128,000 tokens).

DeepSeek
DeepSeek R1 Distill Llama 70B
Input128,000 tokens
Output128,000 tokens
Moonshot AI
Kimi-k1.5
Input- tokens
Output- tokens
Mon Apr 06 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Kimi-k1.5 supports multimodal inputs, whereas DeepSeek R1 Distill Llama 70B does not.

Kimi-k1.5 can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek R1 Distill Llama 70B

Text
Images
Audio
Video

Kimi-k1.5

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek R1 Distill Llama 70B is licensed under MIT, while Kimi-k1.5 uses a proprietary license.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek R1 Distill Llama 70B

MIT

Open weights

Kimi-k1.5

Proprietary

Closed source

Release Timeline

When each model was launched

Both models were released on 2025-01-20.

They likely represent similar generations of model development.

DeepSeek R1 Distill Llama 70B

Jan 20, 2025

1.2 years ago

Kimi-k1.5

Jan 20, 2025

1.2 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Larger context window (128,000 tokens)
Has open weights
Higher AIME 2024 score (86.7% vs 77.5%)
Moonshot AI
Supports multimodal inputs
Higher MATH-500 score (96.2% vs 94.5%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek R1 Distill Llama 70B
Moonshot AI
Kimi-k1.5

FAQ

Common questions about DeepSeek R1 Distill Llama 70B vs Kimi-k1.5

Both models are evenly matched across the benchmarks. DeepSeek R1 Distill Llama 70B is made by DeepSeek and Kimi-k1.5 is made by Moonshot AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek R1 Distill Llama 70B scores MATH-500: 94.5%, AIME 2024: 86.7%, GPQA: 65.2%, LiveCodeBench: 57.5%. Kimi-k1.5 scores MATH-500: 96.2%, CLUEWSC: 91.4%, C-Eval: 88.3%, MMLU: 87.4%, IFEval: 87.2%.
DeepSeek R1 Distill Llama 70B supports 128K tokens and Kimi-k1.5 supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include multimodal support (no vs yes), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.
DeepSeek R1 Distill Llama 70B is developed by DeepSeek and Kimi-k1.5 is developed by Moonshot AI.