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. | 236B | 5.1B/8B/26B/31B |
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 | 256k |
| 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 | DeepSeek | |
| Release date | Sep 5 2024 | Apr 2 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek V2.5vsClaude Haiku 5.5Gemma 4vsClaude Haiku 5.5DeepSeek V2.5vsGPT-6.1 SolGemma 4vsGPT-6.1 SolDeepSeek V2.5vsMuse Spark OpenGemma 4vsMuse Spark OpenDeepSeek V2.5vsGrok 4.7Gemma 4vsGrok 4.7DeepSeek V2.5vsMistral Large 4Gemma 4vsMistral Large 4DeepSeek V2.5vsKimi K3Gemma 4vsKimi K3Frequently asked questions
DeepSeek V2.5 and Gemma 4 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek V2.5 shipped 574 days before Gemma 4, so benchmark comparisons should account for the intervening progress.
DeepSeek V2.5 has 236B parameters, while Gemma 4 has 5.1B/8B/26B/31B. Context windows are 128k (DeepSeek V2.5) vs 256k (Gemma 4).
Direct benchmark comparisons are unavailable — DeepSeek V2.5 and Gemma 4 don't publish scores on any of the same benchmarks.