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 | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $1.75 | — |
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. | $14.00 | — |
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. | $0.175 | — |
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. | $1.75Azure | $0.30DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $14.00Azure | $1.00DeepInfra |
| $1,050OpenAI 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. | 24% | 20% |
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. | 85% | — |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | 68% | — |
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. | 64.7% | — |
threejsevalCommunity preference (Three.js) — Every model gets the same prompt — "the Eiffel Tower", "a glass fishbowl", "a robot arm picking toys into a box" — and builds a 3D scene in Three.js. Real people then see two scenes side by side, names hidden, and vote for the one they prefer. The votes become a chess-style Elo rating on threejseval.com, averaged across all the prompts. It measures whether the scene looks and moves right to a human eye, not whether the code passes a test. Higher is better. | 1153 | — |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Feb 5 2026 | Jul 22 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
GPT-5.3-CodexvsClaude Haiku 5.5Qwen3-CodervsClaude Haiku 5.5GPT-5.3-CodexvsGemini 4 CarbonQwen3-CodervsGemini 4 CarbonGPT-5.3-CodexvsMuse Spark OpenQwen3-CodervsMuse Spark OpenGPT-5.3-CodexvsGrok 4.7Qwen3-CodervsGrok 4.7GPT-5.3-CodexvsDeepSeek-V4.1-Flash-1017Qwen3-CodervsDeepSeek-V4.1-Flash-1017GPT-5.3-CodexvsMistral Large 4Qwen3-CodervsMistral Large 4Frequently asked questions
GPT-5.3-Codex leads Qwen3-Coder on 1 of the 1 benchmark they both report (BullshitBench v2). Only GPT-5.3-Codex has a verified first-party price for an available API: $1.75 per million input tokens and $14.00 per million output tokens. No currently available pay-as-you-go API rate is tracked for Qwen3-Coder. Qwen3-Coder shipped 198 days before GPT-5.3-Codex, so benchmark comparisons should account for the intervening progress.
Context windows are 400k (GPT-5.3-Codex) vs 256k (Qwen3-Coder). GPT-5.3-Codex is closed, while Qwen3-Coder is open weight.
On BullshitBench v2, GPT-5.3-Codex leads at 24% vs Qwen3-Coder at 20%.