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. | — | 480B |
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 | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $0.15 | — |
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. | $0.60 | — |
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. | $0.075 | — |
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. | $0.15Azure | $0.30DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.60Azure | $1.00DeepInfra |
| $60.00OpenAI list price | $110DeepInfra · turbo · fp4 | |
| Benchmarks | ||
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. | 2% | 20% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 40.2% | — |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Jul 18 2024 | Jul 22 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
GPT-4o minivsClaude Haiku 5.5Qwen3-CodervsClaude Haiku 5.5GPT-4o minivsGemini 4 CarbonQwen3-CodervsGemini 4 CarbonGPT-4o minivsMuse Spark OpenQwen3-CodervsMuse Spark OpenGPT-4o minivsGrok 4.7Qwen3-CodervsGrok 4.7GPT-4o minivsDeepSeek-V4.1-Flash-1017Qwen3-CodervsDeepSeek-V4.1-Flash-1017GPT-4o minivsMistral Large 4Qwen3-CodervsMistral Large 4Frequently asked questions
Qwen3-Coder leads GPT-4o mini on 1 of the 1 benchmark they both report (BullshitBench v2). Only GPT-4o mini has a verified first-party price for an available API: $0.15 per million input tokens and $0.60 per million output tokens. No currently available pay-as-you-go API rate is tracked for Qwen3-Coder. GPT-4o mini shipped 369 days before Qwen3-Coder, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (GPT-4o mini) vs 256k (Qwen3-Coder). GPT-4o mini is closed, while Qwen3-Coder is open weight.
On BullshitBench v2, Qwen3-Coder leads at 20% vs GPT-4o mini at 2%.