Llama 3.1 70B vs Llama 3.1 8B
At a Glance
| Compare |  Llama 3.1 70BMeta | Llama 3.1 8BMeta |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 131K | 131K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 28, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | Llama-3.1-70B | Llama-3.1-8B |
|---|---|---|
| Developer | Meta | Meta |
| Family | Llama 3 1 70b | Llama 3 1 8b |
| Model | Llama-3.1-70B | Llama-3.1-8B |
| Version | Llama-3.1-70B | Llama-3.1-8B |
| Lifecycle | active | active |
| Released | 2024-07-23 | 2024-07-23 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 131K | 131K |
| Total parameters | 70.6B | 8B |
| Active parameters | Unknown | Unknown |
| License | llama3.1 | llama3.1 |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Hugging Face (Standard) | Hugging Face (Standard) |
| Capabilities | generation | generation |
Llama 3.1 70B Capabilities
Llama 3.1 8B Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
| vs | Llama-3.1-70BMeta | cross-developer peerstext | |
 DeepSeek-V3.2DeepSeek | vs | Llama-3.1-70BMeta | cross-developer peerstext |
| vs | Llama-3.1-70BMeta | cross-developer peerstext | |
Llama-3.1-405BMeta | vs | Llama-3.1-70BMeta | family variantstext |
Llama-3.1-70BMeta | vs | family variantstext | |
Llama-3.1-70BMeta | vs | family variantstext | |
Llama-3.1-70BMeta | vs | cross-developer peerstext | |
Llama-3.1-70BMeta | vs | cross-developer peerstext | |
Llama-3.1-70BMeta | vs |  gpt-oss-120bOpenAI | cross-developer peerstext |
 Qwen3-Coder-NextQwen | vs | Llama-3.1-70BMeta | cross-developer peerstext |
| vs | Llama-3.1-70BMeta | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Llama 3.1 70B vs Llama 3.1 8B FAQs
Is Llama 3.1 70B or Llama 3.1 8B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 70B and Llama 3.1 8B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Llama 3.1 70B or Llama 3.1 8B?+
Neither model has a directly sourced input price in this comparison. Neither model has a directly sourced output price in this comparison.
Which has a larger context window, Llama 3.1 70B or Llama 3.1 8B?+
Both models have the same sourced context window: 131,072 tokens each.
Which performs better in benchmarks, Llama 3.1 70B or Llama 3.1 8B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Llama 3.1 70B or Llama 3.1 8B be self-hosted?+
Both models have the same sourced self-hosting status: supported. Llama 3.1 70B is open weight; Llama 3.1 8B is open weight.
Can Llama 3.1 70B and Llama 3.1 8B understand images?+
Llama 3.1 70B is not documented with image input; Llama 3.1 8B is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Llama 3.1 70B or Llama 3.1 8B?+
Neither model has a sourced maximum output in this comparison, so a larger limit cannot be determined.
Do Llama 3.1 70B and Llama 3.1 8B support reasoning and tool use?+
Llama 3.1 70B: none of these features are definitively sourced. Llama 3.1 8B: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 3.1 70B or Llama 3.1 8B?+
Llama 3.1 70B has 1 sourced provider route; Llama 3.1 8B has 1, a tie.
Which offers better value, Llama 3.1 70B or Llama 3.1 8B?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.