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/1.8B/4B/7B/14B/72B |
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. | 400k | 32k |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $0.20 | — |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | $1.25 | — |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | $0.02 | — |
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. | $0.10OpenAI | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.625OpenAI | — |
| $103OpenAI list price | — | |
| 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. | 14% | — |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | 37.7% | — |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | 5.7% | — |
OSWorld-VerifiedAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | 39% | — |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Mar 17 2026 | Feb 4 2024 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
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GPT-5.4 nano and Qwen1.5 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-5.4 nano has a verified first-party API price: $0.20 per million input tokens and $1.25 per million output tokens. No pay-as-you-go API rate is tracked for Qwen1.5. Qwen1.5 shipped 772 days before GPT-5.4 nano, so benchmark comparisons should account for the intervening progress.
Context windows are 400k (GPT-5.4 nano) vs 32k (Qwen1.5). GPT-5.4 nano is closed, while Qwen1.5 is open weight.
Direct benchmark comparisons are unavailable — GPT-5.4 nano and Qwen1.5 don't publish scores on any of the same benchmarks.