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. | — | 80B |
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. | — | $0.072DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.88DeepInfra |
| — | $73.00DeepInfra · fp8 | |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Feb 27 2025 | Sep 11 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
GPT-4.5vsClaude Haiku 5.5Qwen3-NextvsClaude Haiku 5.5GPT-4.5vsGemini 4 ArgonQwen3-NextvsGemini 4 ArgonGPT-4.5vsMuse Spark OpenQwen3-NextvsMuse Spark OpenGPT-4.5vsGrok 4.7Qwen3-NextvsGrok 4.7GPT-4.5vsDeepSeek-V4.1-Flash-1017Qwen3-NextvsDeepSeek-V4.1-Flash-1017GPT-4.5vsMistral Large 4Qwen3-NextvsMistral Large 4Frequently asked questions
GPT-4.5 and Qwen3-Next don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. GPT-4.5 shipped 196 days before Qwen3-Next, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (GPT-4.5) vs 256k (Qwen3-Next). GPT-4.5 is closed, while Qwen3-Next is open weight.
Direct benchmark comparisons are unavailable — GPT-4.5 and Qwen3-Next don't publish scores on any of the same benchmarks.