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. | — | 0.5B/1.5B/3B/7B/14B/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. | 200k | 128k |
| 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. | $1.10OpenAI | $0.66Cloudflare |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $4.40OpenAI | $1.00Cloudflare |
| $440OpenAI | $182Cloudflare | |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Apr 16 2025 | Nov 12 2024 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
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o4-mini-high and Qwen2.5-Coder don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Qwen2.5-Coder shipped 155 days before o4-mini-high, so benchmark comparisons should account for the intervening progress.
Context windows are 200k (o4-mini-high) vs 128k (Qwen2.5-Coder). o4-mini-high is closed, while Qwen2.5-Coder is open weight.
Direct benchmark comparisons are unavailable — o4-mini-high and Qwen2.5-Coder don't publish scores on any of the same benchmarks.