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 | 480B |
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 | 256k |
| 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. | — | $0.30DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $1.00DeepInfra |
| — | $110DeepInfra · turbo · fp4 | |
| 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. | — | 20% |
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% | — |
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 | Qwen |
| Release date | Oct 16 2026 | Jul 22 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Nemotron Labs Dory 1.0 4BvsClaude Fable 5.2Qwen3-CodervsClaude Fable 5.2Nemotron Labs Dory 1.0 4BvsGPT-6.1 TerraQwen3-CodervsGPT-6.1 TerraNemotron Labs Dory 1.0 4BvsGemini 4 CarbonQwen3-CodervsGemini 4 CarbonNemotron Labs Dory 1.0 4BvsMuse EmberQwen3-CodervsMuse EmberNemotron Labs Dory 1.0 4BvsGrok 4.7Qwen3-CodervsGrok 4.7Nemotron Labs Dory 1.0 4BvsDeepSeek-V4.1-Flash-1017Qwen3-CodervsDeepSeek-V4.1-Flash-1017Frequently asked questions
Nemotron Labs Dory 1.0 4B and Qwen3-Coder don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Qwen3-Coder shipped 451 days before Nemotron Labs Dory 1.0 4B, so benchmark comparisons should account for the intervening progress.
Nemotron Labs Dory 1.0 4B has 4B parameters, while Qwen3-Coder has 480B. Context windows are 256k (Nemotron Labs Dory 1.0 4B) vs 256k (Qwen3-Coder).
Direct benchmark comparisons are unavailable — Nemotron Labs Dory 1.0 4B and Qwen3-Coder don't publish scores on any of the same benchmarks.