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 | — |
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 | 32k |
| 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 | Qwen |
| Release date | Dec 6 2024 | Jan 28 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
LLaMA 3.3vsClaude Haiku 5.5Qwen2.5-MaxvsClaude Haiku 5.5LLaMA 3.3vsGPT-6.1 SolQwen2.5-MaxvsGPT-6.1 SolLLaMA 3.3vsGemini 4 ArgonQwen2.5-MaxvsGemini 4 ArgonLLaMA 3.3vsGrok 4.7Qwen2.5-MaxvsGrok 4.7LLaMA 3.3vsDeepSeek-V4.1-Flash-1017Qwen2.5-MaxvsDeepSeek-V4.1-Flash-1017LLaMA 3.3vsMistral Large 4Qwen2.5-MaxvsMistral Large 4Frequently asked questions
LLaMA 3.3 and Qwen2.5-Max don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 3.3 shipped 53 days before Qwen2.5-Max, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (LLaMA 3.3) vs 32k (Qwen2.5-Max). LLaMA 3.3 is open weight, while Qwen2.5-Max is closed.
Direct benchmark comparisons are unavailable — LLaMA 3.3 and Qwen2.5-Max don't publish scores on any of the same benchmarks.