Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | 70B | 8B |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 128k | — |
| API pricingUSD per 1M tokens · lower wins | ||
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.10DeepInfra | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.32DeepInfra | — |
| $36.00DeepInfra · turbo · fp8 | — | |
| Overview | ||
| Company | Meta | NVIDIA |
| Release date | Dec 6 2024 | Oct 23 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
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LLaMA 3.3 and Nemotron 3.5 Bolt E4B 4GB don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 3.3 shipped 686 days before Nemotron 3.5 Bolt E4B 4GB, so benchmark comparisons should account for the intervening progress.
LLaMA 3.3 has 70B parameters, while Nemotron 3.5 Bolt E4B 4GB has 8B.
Direct benchmark comparisons are unavailable — LLaMA 3.3 and Nemotron 3.5 Bolt E4B 4GB don't publish scores on any of the same benchmarks.