Compare / K2-V2 Instruct vs Llama 3.3 70B
K2-V2 Instruct vs Llama 3.3 70B
Updated November 2026
Verdict
K2-V2 Instruct scores higher on the Small LM Index: 57.9 vs 40.8. On the 1 benchmark both labs report, K2-V2 Instruct wins 1 and Llama 3.3 70B wins 0. Both need about 47.4 GB of memory at 4-bit quantization. Only Llama 3.3 70B has a hosted API on OpenRouter ($0.15 per million tokens); the other has to be self-hosted.
Pick K2-V2 Instruct if…
- you want the strongest model overall
- you work with long documents
- you need a permissive license (Apache-2.0)
Pick Llama 3.3 70B if…
- you want a hosted API rather than running it yourself
- you want the more widely used model (more quantized builds and community support)
Specifications
| K2-V2 Instruct | Llama 3.3 70B | |
|---|---|---|
| Small LM Index | 57.9 (likely 54–62) | 40.8 (likely 31–51) |
| Lab | LLM360 / MBZUAI (K2) · Other | Meta · United States |
| Released | 2025-12-03 | 2024-12-06 |
| 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) | No thinking |
| License | Apache-2.0 | Llama 3.3 Community |
| API price / 1M tokens | Self-host only | $0.15 ($0.10 in, $0.32 out) |
| Hugging Face downloads (30 days) | 1.2K | 363.3K |
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 | Llama 3.3 70B |
|---|---|---|
| MMLU-Pro Knowledge | ≈ 77 | 68.9 |
| GPQA Diamond Knowledge | 69.3 | 50.5 |
| AIME Math | 80.2 | ≈ 54 |
| 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 Llama 3.3 70B?
K2-V2 Instruct scores higher on the Small LM Index: 57.9 vs 40.8. On the 1 benchmark both labs report, K2-V2 Instruct wins 1 and Llama 3.3 70B wins 0.
Which is cheaper to run?
Only Llama 3.3 70B has a hosted API on OpenRouter ($0.15 per million tokens); the other has to be self-hosted. 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 Llama 3.3 70B commercially?
K2-V2 Instruct is released under Apache-2.0 and Llama 3.3 70B under Llama 3.3 Community. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.