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. | 9B/27B | — |
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. | 8k | 400k |
| 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. | $0.65NextBit | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.65NextBit | — |
| $163NextBit · int4 | — | |
| 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. | — | 39% |
| Overview | ||
| Company | OpenAI | |
| Release date | Jun 27 2024 | Sep 15 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Gemma 2vsClaude Haiku 5.5GPT-5-CodexvsClaude Haiku 5.5Gemma 2vsMuse Spark OpenGPT-5-CodexvsMuse Spark OpenGemma 2vsGrok 4.7GPT-5-CodexvsGrok 4.7Gemma 2vsDeepSeek-V4.1-Flash-1017GPT-5-CodexvsDeepSeek-V4.1-Flash-1017Gemma 2vsMistral Large 4GPT-5-CodexvsMistral Large 4Gemma 2vsKimi K3GPT-5-CodexvsKimi K3Frequently asked questions
Gemma 2 and GPT-5-Codex don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Gemma 2 shipped 445 days before GPT-5-Codex, so benchmark comparisons should account for the intervening progress.
Context windows are 8k (Gemma 2) vs 400k (GPT-5-Codex). Gemma 2 is open weight, while GPT-5-Codex is closed.
Direct benchmark comparisons are unavailable — Gemma 2 and GPT-5-Codex don't publish scores on any of the same benchmarks.