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. | 14B | 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. | 256k | 1M |
| 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. | $0.20 | — |
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. | $0.20 | — |
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. | $0.20Mistral | $0.08DekaLLM |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.20Mistral | $0.40DeepInfra |
| $50.00Mistral list price | $37.00DeepInfra · bf16 | |
| 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. | — | 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% |
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% |
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% |
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% |
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 | Mistral | NVIDIA |
| Release date | Dec 2 2025 | Mar 11 2026 |
| Access | Open Weight | Open Source |
| Model details | View model | View model |
Other comparisons
Ministral 3 14B-25-12vsClaude Haiku 5.5Nemotron 3 SupervsClaude Haiku 5.5Ministral 3 14B-25-12vsGPT-6.1 SolNemotron 3 SupervsGPT-6.1 SolMinistral 3 14B-25-12vsGemini 4 ArgonNemotron 3 SupervsGemini 4 ArgonMinistral 3 14B-25-12vsMuse Spark OpenNemotron 3 SupervsMuse Spark OpenMinistral 3 14B-25-12vsGrok 4.7Nemotron 3 SupervsGrok 4.7Ministral 3 14B-25-12vsDeepSeek-V4.1-Flash-1017Nemotron 3 SupervsDeepSeek-V4.1-Flash-1017Frequently asked questions
Ministral 3 14B-25-12 and Nemotron 3 Super don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Ministral 3 14B-25-12 has a verified first-party API price: $0.20 per million input tokens and $0.20 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3 Super. Ministral 3 14B-25-12 shipped 99 days before Nemotron 3 Super, so benchmark comparisons should account for the intervening progress.
Ministral 3 14B-25-12 has 14B parameters, while Nemotron 3 Super has 120B. Context windows are 256k (Ministral 3 14B-25-12) vs 1M (Nemotron 3 Super). Ministral 3 14B-25-12 is open weight, while Nemotron 3 Super is open source.
Direct benchmark comparisons are unavailable — Ministral 3 14B-25-12 and Nemotron 3 Super don't publish scores on any of the same benchmarks.