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. | 4B | — |
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. | 256k | 1.05M |
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $30.00 |
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. | — | $180.00 |
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. | — | $15.00OpenAI |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $90.00OpenAI |
| — | $15,000OpenAI list price | |
| Benchmarks | ||
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 72.41% | 94.4% |
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. | 59.5% | — |
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | 18.1% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 89.3% |
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. | — | 42.7% |
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. | — | 58.7% |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | — | 83.3% |
FrontierMath · Tier 1–3Advanced math — Very hard, research-level math problems. Tiers 1–3 are the (still extremely difficult) lower tiers. Higher is better. | — | 50% |
FrontierMath · Tier 4Advanced math — Very hard, research-level math problems. Tier 4 is the hardest — close to what professional research mathematicians tackle. Higher is better. | — | 38% |
ARC-AGI-1Reasoning — accuracy on the first edition of the abstract grid reasoning benchmark | — | 94.5% |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | 77.94% | — |
FinanceAgent v1.1Finance — performance on financial research tasks in the v1.1 evaluation | — | 61.5% |
GDPval (win/tie rate)Knowledge work — How often the AI's work matches or beats a human expert's on real knowledge-work tasks. Higher is better. | — | 82% |
| Overview | ||
| Company | NVIDIA | OpenAI |
| Release date | Oct 16 2026 | Mar 5 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron Labs Dory 1.0 4BvsClaude Fable 5.2GPT-5.4-ProvsClaude Fable 5.2Nemotron Labs Dory 1.0 4BvsGemini 4 CarbonGPT-5.4-ProvsGemini 4 CarbonNemotron Labs Dory 1.0 4BvsMuse EmberGPT-5.4-ProvsMuse EmberNemotron Labs Dory 1.0 4BvsGrok 4.7GPT-5.4-ProvsGrok 4.7Nemotron Labs Dory 1.0 4BvsDeepSeek-V4.1-Flash-1017GPT-5.4-ProvsDeepSeek-V4.1-Flash-1017Nemotron Labs Dory 1.0 4BvsMistral Large 4GPT-5.4-ProvsMistral Large 4Frequently asked questions
GPT-5.4-Pro leads Nemotron Labs Dory 1.0 4B on 1 of the 1 benchmark they both report (GPQA Diamond). Only GPT-5.4-Pro has a verified first-party price for an available API: $30.00 per million input tokens and $180.00 per million output tokens. No currently available pay-as-you-go API rate is tracked for Nemotron Labs Dory 1.0 4B. GPT-5.4-Pro shipped 225 days before Nemotron Labs Dory 1.0 4B, so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Nemotron Labs Dory 1.0 4B) vs 1.05M (GPT-5.4-Pro). Nemotron Labs Dory 1.0 4B is open weight, while GPT-5.4-Pro is closed.
On GPQA Diamond, GPT-5.4-Pro leads at 94.4% vs Nemotron Labs Dory 1.0 4B at 72.41%.