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. | 8B/49B | — |
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. | 128k | 400k |
| 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. | — | $0.875OpenAI |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $7.00OpenAI |
| — | $1,050OpenAI list price | |
| These models have no shared benchmark scores. | ||
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. | — | 38% |
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. | — | 80% |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 65.8% |
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. | — | 52.9% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 92.4% |
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. | — | 47.3% |
| Overview | ||
| Company | NVIDIA | OpenAI |
| Release date | Mar 18 2025 | Dec 11 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Llama Nemotron (Nano/Super)vsClaude Haiku 5.5GPT-5.2vsClaude Haiku 5.5Llama Nemotron (Nano/Super)vsGemini 4 ArgonGPT-5.2vsGemini 4 ArgonLlama Nemotron (Nano/Super)vsMuse Spark OpenGPT-5.2vsMuse Spark OpenLlama Nemotron (Nano/Super)vsGrok 4.7GPT-5.2vsGrok 4.7Llama Nemotron (Nano/Super)vsDeepSeek-V4.1-Flash-1017GPT-5.2vsDeepSeek-V4.1-Flash-1017Llama Nemotron (Nano/Super)vsMistral Large 4GPT-5.2vsMistral Large 4Frequently asked questions
Llama Nemotron (Nano/Super) and GPT-5.2 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-5.2 has a verified first-party API price: $1.75 per million input tokens and $14.00 per million output tokens. No pay-as-you-go API rate is tracked for Llama Nemotron (Nano/Super). Llama Nemotron (Nano/Super) shipped 268 days before GPT-5.2, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (Llama Nemotron (Nano/Super)) vs 400k (GPT-5.2). Llama Nemotron (Nano/Super) is open weight, while GPT-5.2 is closed.
Direct benchmark comparisons are unavailable — Llama Nemotron (Nano/Super) and GPT-5.2 don't publish scores on any of the same benchmarks.