GPT-5.4 nanovsQwen3.5
GPT-5.4 nano | 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. | $0.20 | $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. | $1.25 | $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. | $0.02 | — |
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. | $0.20Azure | $0.08Darkbloom |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $1.25Azure | $0.15DeepInfra |
| Benchmarks | ||
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. | 39% | 62.2% |
| 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. | 14% | — |
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. | — | 76.4% |
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% |
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% |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | 37.7% | — |
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% |
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% |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Mar 17 2026 | Feb 16 2026 |
| Access | Proprietary | Open Weight |
Other comparisons
GPT-5.4 nanovsClaude Opus 5Qwen3.5vsClaude Opus 5GPT-5.4 nanovsGemini 3.7 FlashQwen3.5vsGemini 3.7 FlashGPT-5.4 nanovsMuse GlimmerQwen3.5vsMuse GlimmerGPT-5.4 nanovsGrok 4.6Qwen3.5vsGrok 4.6GPT-5.4 nanovsDeepSeek-V4-Pro-0813Qwen3.5vsDeepSeek-V4-Pro-0813GPT-5.4 nanovsMistral Medium 3.5Qwen3.5vsMistral Medium 3.5
Frequently asked questions
Qwen3.5 leads GPT-5.4 nano on 1 of the 1 benchmark they both report (OSWorld-Verified). GPT-5.4 nano is cheaper on both input and output: $0.20 vs $0.60 per million input tokens, and $1.25 vs $3.60 per million output tokens. Qwen3.5 shipped 29 days before GPT-5.4 nano, so benchmark comparisons should account for the intervening progress.
GPT-5.4 nano is proprietary, while Qwen3.5 is open weight.
On OSWorld-Verified, Qwen3.5 leads at 62.2% vs GPT-5.4 nano at 39%.
GPT-5.4 nano was released by OpenAI on Mar 17 2026.
Qwen3.5 was released by Qwen on Feb 16 2026.
GPT-5.4 nano is cheaper on both input and output: $0.20 vs $0.60 per million input tokens, and $1.25 vs $3.60 per million output tokens. Rates are pay-as-you-go API prices verified on August 18, 2026.
GPT-5.4 nano is a proprietary model released by OpenAI. Qwen3.5 is an open weight model released by Qwen.