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 | — |
| 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. | — | $1.25 |
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. | — | $2.50 |
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. | — | $0.20 |
| — | $375SpaceXSI list price | |
| 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. | 59.5% | 76.7% |
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. | — | 56% |
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% | — |
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. | — | 53.3% |
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% | — |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | 77.94% | — |
| Overview | ||
| Company | NVIDIA | SpaceXSI |
| Release date | Oct 16 2026 | Feb 17 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron Labs Dory 1.0 4BvsClaude Fable 5.2Grok 4.20 BetavsClaude Fable 5.2Nemotron Labs Dory 1.0 4BvsGPT-6.1 TerraGrok 4.20 BetavsGPT-6.1 TerraNemotron Labs Dory 1.0 4BvsGemini 4 CarbonGrok 4.20 BetavsGemini 4 CarbonNemotron Labs Dory 1.0 4BvsMuse EmberGrok 4.20 BetavsMuse EmberNemotron Labs Dory 1.0 4BvsDeepSeek-V4.1-Flash-1017Grok 4.20 BetavsDeepSeek-V4.1-Flash-1017Nemotron Labs Dory 1.0 4BvsMistral Large 4Grok 4.20 BetavsMistral Large 4Frequently asked questions
Grok 4.20 Beta leads Nemotron Labs Dory 1.0 4B on 1 of the 1 benchmark they both report (SWE-Bench Verified). Only Grok 4.20 Beta has a verified first-party price for an available API: $1.25 per million input tokens and $2.50 per million output tokens. No currently available pay-as-you-go API rate is tracked for Nemotron Labs Dory 1.0 4B. Grok 4.20 Beta shipped 241 days before Nemotron Labs Dory 1.0 4B, so benchmark comparisons should account for the intervening progress.
Nemotron Labs Dory 1.0 4B is open weight, while Grok 4.20 Beta is closed.
On SWE-Bench Verified, Grok 4.20 Beta leads at 76.7% vs Nemotron Labs Dory 1.0 4B at 59.5%.