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. | 120B | — |
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. | 1M | — |
| API pricingUSD per 1M tokens · lower wins | ||
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. | $0.08DekaLLM | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.40DeepInfra | — |
| $37.00DeepInfra · bf16 | — | |
| Benchmarks | ||
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. | 45.78% | 73.7% |
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. | 31% | 61.7% |
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. | 54% | — |
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. | 60.5% | — |
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. | — | 58% |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | 81.19% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | 31.28% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 79.2% | — |
AA Intelligence IndexOverall intelligence — Artificial Analysis composite intelligence index across evals | 36 | — |
| Overview | ||
| Company | NVIDIA | SpaceXSI |
| Release date | Mar 11 2026 | Mar 19 2026 |
| Access | Open Source | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron 3 SupervsClaude Haiku 5.5Composer 2vsClaude Haiku 5.5Nemotron 3 SupervsGPT-6.1 SolComposer 2vsGPT-6.1 SolNemotron 3 SupervsGemini 4 ArgonComposer 2vsGemini 4 ArgonNemotron 3 SupervsMuse Spark OpenComposer 2vsMuse Spark OpenNemotron 3 SupervsDeepSeek-V4.1-Flash-1017Composer 2vsDeepSeek-V4.1-Flash-1017Nemotron 3 SupervsMistral Large 4Composer 2vsMistral Large 4Frequently asked questions
Composer 2 leads Nemotron 3 Super on 2 of the 2 benchmarks they both report (SWE-Bench Multilingual, Terminal-Bench 2.0). Nemotron 3 Super shipped 8 days before Composer 2, so benchmark comparisons should account for the intervening progress.
Nemotron 3 Super is open source, while Composer 2 is closed.
On SWE-Bench Multilingual, Composer 2 leads at 73.7% vs Nemotron 3 Super at 45.78%. On Terminal-Bench 2.0, Composer 2 leads at 61.7% vs Nemotron 3 Super at 31%.