Compare / K2-V2 Instruct vs DeepSeek-R1-Distill-Llama-70B
K2-V2 Instruct vs DeepSeek-R1-Distill-Llama-70B
Updated November 2026
Verdict
K2-V2 Instruct scores higher on the Small LM Index: 57.9 vs 50.1, although their likely ranges overlap, so the lead is not certain. On the 2 benchmarks both labs report, K2-V2 Instruct wins 2 and DeepSeek-R1-Distill-Llama-70B wins 0. Both need about 47.4 GB of memory at 4-bit quantization. Neither model has a hosted API on OpenRouter, so you would run both yourself.
Pick K2-V2 Instruct if…
- you want the strongest model overall
- you need math and reasoning
- you work with long documents
Pick DeepSeek-R1-Distill-Llama-70B if…
- you want the more widely used model (more quantized builds and community support)
Specifications
| K2-V2 Instruct | DeepSeek-R1-Distill-Llama-70B | |
|---|---|---|
| Small LM Index | 57.9 (likely 54–62) | 50.1 (likely 45–55) |
| Lab | LLM360 / MBZUAI (K2) · Other | DeepSeek · China |
| Released | 2025-12-03 | 2025-01-20 |
| Parameters | 70B (dense) | 70.6B (dense) |
| Memory at 4-bit | ~47 GB | ~47.4 GB |
| Context window | 512K tokens | 128K tokens |
| Input | text | text |
| Reasoning | Hybrid (thinking on/off) | Always thinks |
| License | Apache-2.0 | MIT |
| API price / 1M tokens | Self-host only | Self-host only |
| Hugging Face downloads (30 days) | 1.2K | 30.5K |
Benchmarks
Higher is better. Bold marks the winner where both labs report a score. ≈ marks the index model's estimate for a score that wasn't reported, ✓ an independent measurement and * a score measured by another lab.
| Benchmark | K2-V2 Instruct | DeepSeek-R1-Distill-Llama-70B |
|---|---|---|
| GPQA Diamond Knowledge | 69.3 | 65.2 |
| AIME Math | 80.2 | 70.0 |
| LiveCodeBench Coding | ≈ 67 | 57.5 |
| IFEval Instruction following | 90.0 | — |
| HMMT Math | 71.4 | — |
Scores are mostly reported by each lab with its own settings, so small gaps are noise. See the methodology. Open in the interactive comparison to add more models.
Questions
Which is better, K2-V2 Instruct or DeepSeek-R1-Distill-Llama-70B?
K2-V2 Instruct scores higher on the Small LM Index: 57.9 vs 50.1, although their likely ranges overlap, so the lead is not certain. On the 2 benchmarks both labs report, K2-V2 Instruct wins 2 and DeepSeek-R1-Distill-Llama-70B wins 0.
Which is cheaper to run?
Neither model has a hosted API on OpenRouter, so you would run both yourself. Both need about 47.4 GB of memory at 4-bit quantization.
Which has the longer context window?
K2-V2 Instruct has the longer context window: 512K tokens against 128K.
Can I use K2-V2 Instruct and DeepSeek-R1-Distill-Llama-70B commercially?
K2-V2 Instruct is released under Apache-2.0 and DeepSeek-R1-Distill-Llama-70B under MIT. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.
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