Compare / Ling 3.0 Tiny vs Llama 3.1 8B
Ling 3.0 Tiny vs Llama 3.1 8B
Updated November 2026
Verdict
Ling 3.0 Tiny scores higher on the Small LM Index: 51.9 vs 21.3. On the 2 benchmarks both labs report, Ling 3.0 Tiny wins 2 and Llama 3.1 8B wins 0. Both need about 6.6 GB of memory at 4-bit quantization. Only Llama 3.1 8B has a hosted API on OpenRouter ($0.025 per million tokens); the other has to be self-hosted.
Pick Ling 3.0 Tiny if…
- you want the strongest model overall
- you work with long documents
- you need a permissive license (MIT)
Pick Llama 3.1 8B 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
| Ling 3.0 Tiny | Llama 3.1 8B | |
|---|---|---|
| Small LM Index | 51.9 (likely 47–57) | 21.3 (likely 15–29) |
| Lab | InclusionAI (Ling) · China | Meta · United States |
| Released | 2026-08-10 | 2024-07-23 |
| Parameters | 7.9B (1.4B active, MoE) | 8B (dense) |
| Memory at 4-bit | ~5.7 GB | ~6.6 GB |
| Context window | 131K tokens | 128K tokens |
| Input | text | text |
| Reasoning | Hybrid (thinking on/off) | No thinking |
| License | MIT | Llama 3.1 Community |
| API price / 1M tokens | Self-host only | $0.025 ($0.020 in, $0.040 out) |
| Hugging Face downloads (30 days) | 14.3K | 4.5M |
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 | Ling 3.0 Tiny | Llama 3.1 8B |
|---|---|---|
| MMLU-Pro Knowledge | 65.8* | 48.3 |
| GPQA Diamond Knowledge | 73.4 | 30.4 |
| AIME Math | 73.3* | ≈ 18 |
| IFBench Instruction following | 60.6* | — |
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, Ling 3.0 Tiny or Llama 3.1 8B?
Ling 3.0 Tiny scores higher on the Small LM Index: 51.9 vs 21.3. On the 2 benchmarks both labs report, Ling 3.0 Tiny wins 2 and Llama 3.1 8B wins 0.
Which is cheaper to run?
Only Llama 3.1 8B has a hosted API on OpenRouter ($0.025 per million tokens); the other has to be self-hosted. Both need about 6.6 GB of memory at 4-bit quantization.
Which has the longer context window?
Ling 3.0 Tiny has the longer context window: 131K tokens against 128K.
Can I use Ling 3.0 Tiny and Llama 3.1 8B commercially?
Ling 3.0 Tiny is released under MIT and Llama 3.1 8B under Llama 3.1 Community. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.