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. | 5.1B/8B/26B/31B | 7B/13B/34B |
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. | 256k | 16k |
| 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. | $0.042Darkbloom | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.18Makora | — |
| $19.40Darkbloom | — | |
| These models have no shared benchmark scores. | ||
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | 25% | — |
| Overview | ||
| Company | Meta | |
| Release date | Apr 2 2026 | Aug 24 2023 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
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Gemma 4 and Code Llama don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Code Llama shipped 952 days before Gemma 4, so benchmark comparisons should account for the intervening progress.
Gemma 4 has 5.1B/8B/26B/31B parameters, while Code Llama has 7B/13B/34B. Context windows are 256k (Gemma 4) vs 16k (Code Llama).
Direct benchmark comparisons are unavailable — Gemma 4 and Code Llama don't publish scores on any of the same benchmarks.