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. | — | >1T |
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. | 1.05M | 256k |
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $30.00 | $1.20 |
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. | $180.00 | $6.00 |
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. | $15.00OpenAI | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $90.00OpenAI | — |
| $15,000OpenAI list price | $540Qwen list price | |
| These models have no shared benchmark scores. | ||
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | 89.3% | — |
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. | 58.7% | — |
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. | 83.3% | — |
FrontierMath · Tier 1–3Advanced math — Very hard, research-level math problems. Tiers 1–3 are the (still extremely difficult) lower tiers. Higher is better. | 50% | — |
FrontierMath · Tier 4Advanced math — Very hard, research-level math problems. Tier 4 is the hardest — close to what professional research mathematicians tackle. Higher is better. | 38% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 94.4% | — |
GDPval (win/tie rate)Knowledge work — How often the AI's work matches or beats a human expert's on real knowledge-work tasks. Higher is better. | 82% | — |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Mar 5 2026 | Sep 24 2025 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
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GPT-5.4-Pro and Qwen3-Max don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Qwen3-Max is cheaper on both input and output: $1.20 vs $30.00 per million input tokens, and $6.00 vs $180.00 per million output tokens. Figures are base-tier rates. Qwen3-Max shipped 162 days before GPT-5.4-Pro, so benchmark comparisons should account for the intervening progress.
Context windows are 1.05M (GPT-5.4-Pro) vs 256k (Qwen3-Max).
Direct benchmark comparisons are unavailable — GPT-5.4-Pro and Qwen3-Max don't publish scores on any of the same benchmarks.