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. | — | 0.6B/1.7B/4B/8B/14B/30B/32B/235B |
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 · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $1.10 | $0.70 |
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. | $4.40 | $2.80 |
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. | $0.55 | — |
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 | $0.08DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $4.40OpenAI | $0.22NextBit |
| $440OpenAI list price | $30.00DeepInfra · fp8 | |
| 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. | — | 6% |
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | 49.3% | — |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Jan 31 2025 | Apr 29 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Frequently asked questions
o3-mini and Qwen3 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Qwen3 is cheaper on both input and output: $0.70 vs $1.10 per million input tokens, and $2.80 vs $4.40 per million output tokens. Figures are base-tier rates. o3-mini shipped 88 days before Qwen3, so benchmark comparisons should account for the intervening progress.
Context windows are 200k (o3-mini) vs 128k (Qwen3). o3-mini is closed, while Qwen3 is open weight.
Direct benchmark comparisons are unavailable — o3-mini and Qwen3 don't publish scores on any of the same benchmarks.