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 | 32B |
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 | — | |
| These models have no shared benchmark scores. | ||
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | — | 50% |
| Overview | ||
| Company | Meta | Qwen |
| Release date | Dec 6 2024 | Nov 28 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
LLaMA 3.3vsClaude Haiku 5.5QwQ-32B-PreviewvsClaude Haiku 5.5LLaMA 3.3vsGPT-6.1 TerraQwQ-32B-PreviewvsGPT-6.1 TerraLLaMA 3.3vsGemini 4 CarbonQwQ-32B-PreviewvsGemini 4 CarbonLLaMA 3.3vsGrok 4.7QwQ-32B-PreviewvsGrok 4.7LLaMA 3.3vsDeepSeek-V4.1-Flash-1017QwQ-32B-PreviewvsDeepSeek-V4.1-Flash-1017LLaMA 3.3vsMistral Large 4QwQ-32B-PreviewvsMistral Large 4Frequently asked questions
LLaMA 3.3 and QwQ-32B-Preview don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. QwQ-32B-Preview shipped 8 days before LLaMA 3.3, so benchmark comparisons should account for the intervening progress.
LLaMA 3.3 has 70B parameters, while QwQ-32B-Preview has 32B. Context windows are 128k (LLaMA 3.3) vs 32k (QwQ-32B-Preview).
Direct benchmark comparisons are unavailable — LLaMA 3.3 and QwQ-32B-Preview don't publish scores on any of the same benchmarks.