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. | 400k | 256k |
| 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. | — | $0.30DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $1.00DeepInfra |
| — | $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. | 39% | 20% |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Sep 15 2025 | Jul 22 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
GPT-5-CodexvsClaude Haiku 5.5Qwen3-CodervsClaude Haiku 5.5GPT-5-CodexvsGemini 4 CarbonQwen3-CodervsGemini 4 CarbonGPT-5-CodexvsMuse Spark OpenQwen3-CodervsMuse Spark OpenGPT-5-CodexvsGrok 4.7Qwen3-CodervsGrok 4.7GPT-5-CodexvsDeepSeek-V4.1-Flash-1017Qwen3-CodervsDeepSeek-V4.1-Flash-1017GPT-5-CodexvsMistral Large 4Qwen3-CodervsMistral Large 4Frequently asked questions
GPT-5-Codex leads Qwen3-Coder on 1 of the 1 benchmark they both report (BullshitBench v2). Qwen3-Coder shipped 55 days before GPT-5-Codex, so benchmark comparisons should account for the intervening progress.
Context windows are 400k (GPT-5-Codex) vs 256k (Qwen3-Coder). GPT-5-Codex is closed, while Qwen3-Coder is open weight.
On BullshitBench v2, GPT-5-Codex leads at 39% vs Qwen3-Coder at 20%.