Meta: Llama 3.3 70B Instruct Rank
Llama 3.3 70B Instruct has an overall score of 55 out of 100, tied for rank 241 of 291. The published scores are price 77, context 44, parameters 46, and freshness 51. The overall score is their average. recency is not published and is left out of the average.
Data sourced from OpenRouter API and manual data collection | Last updated: Nov 3, 2026, 8:01 AM UTC
Component scores are ranked in
tied for rank 241 of 291
Component scores
| Score | Result | Published value | Source |
|---|---|---|---|
| Freshness | 51 | AI Knowledge Cutoff | |
| Recency | — | — | AI Release Date |
| Context | 44 | 131K tokens, output 128K | AI Context Window |
| Parameters | 46 | 70B total | AI Parameter Count |
| Price | 77 | $0.28 / 1M blended, $0.2 / 1M input, $0.52 / 1M output | AI Token Price |
240 listed models have a higher overall score. Among Meta models, it ranks 3 of 8.
The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model...
Higher overall scores
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Frequently asked questions
What is the Llama 3.3 70B Instruct rank?
Llama 3.3 70B Instruct has an overall score of 55 out of 100, tied for rank 241 of 291. The published scores are price 77, context 44, parameters 46, and freshness 51. The overall score is their average. recency is not published and is left out of the average.
Where does Llama 3.3 70B Instruct rank?
Llama 3.3 70B Instruct is tied for rank 241 of 291. 240 listed models have a higher overall score. Among Meta models, it ranks 3 of 8.
What scores make up Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct scores price 77 (AI Token Price), context 44 (AI Context Window), parameters 46 (AI Parameter Count), and freshness 51 (AI Knowledge Cutoff). The overall score of 55 is the average of those 4.