GPT-5.3-CodexvsQwen3.5
GPT-5.3-Codex | Qwen3.5 | |
|---|---|---|
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | — | 397B |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | — | 1M |
| 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 | $0.60 |
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 | $3.60 |
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. | $0.175 | — |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $1.75Azure | $0.10DeepInfra |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $14.00Azure | $0.15DeepInfra |
| Benchmarks | ||
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% | 76.4% |
| BenchmarksPublished by one model only | ||
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% | — |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | — | 69.3% |
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. | 83% | — |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | — | 52.5% |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 69% |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | — | 28.7% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 88.4% |
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. | — | 62.2% |
CharXiv ReasoningChart reasoning — Can the AI read and reason about complex charts and figures, not just text? Higher is better. | — | 80.8% |
MMMU-ProMultimodal reasoning — A tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | — | 79% |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | — | 85% |
Arena Elo (Code)Community preference (code) — Like the text arena, but people vote on which AI writes better code. The votes become a chess-style Elo rating on arena.ai. Higher is better. | 1406 | — |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Feb 5 2026 | Feb 16 2026 |
| Access | Proprietary | Open Weight |
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Frequently asked questions
GPT-5.3-Codex leads Qwen3.5 on 1 of the 1 benchmark they both report (SWE-Bench Verified). Qwen3.5 is cheaper on both input and output: $0.60 vs $1.75 per million input tokens, and $3.60 vs $14.00 per million output tokens. GPT-5.3-Codex shipped 11 days before Qwen3.5, so benchmark comparisons should account for the intervening progress.
GPT-5.3-Codex is proprietary, while Qwen3.5 is open weight.
On SWE-Bench Verified, GPT-5.3-Codex leads at 85% vs Qwen3.5 at 76.4%.
GPT-5.3-Codex was released by OpenAI on Feb 5 2026.
Qwen3.5 was released by Qwen on Feb 16 2026.
GPT-5.3-Codex leads on SWE-Bench Verified — GPT-5.3-Codex 85% vs Qwen3.5 76.4%.
Qwen3.5 is cheaper on both input and output: $0.60 vs $1.75 per million input tokens, and $3.60 vs $14.00 per million output tokens. Rates are pay-as-you-go API prices verified on August 18, 2026.
GPT-5.3-Codex is a proprietary model released by OpenAI. Qwen3.5 is an open weight model released by Qwen.