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. | 1M | 128k |
| 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. | $1.25Meta | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $4.25Meta | — |
| $463Meta | — | |
| 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. | 50% | — |
ProgramBenchProgram reconstruction — The AI receives a working program and its documentation, then builds a replacement from scratch without the original source code, internet access or decompilation. The score is the percentage of 200 programs that pass every behavioral test. We record each model's best published mini-SWE-agent result, including higher reasoning efforts where available. Partial test-pass rates and almost-solved programs do not count toward this score. Equal scores share a rank here; the official board also uses partial progress to break ties. Higher is better. | 0% | — |
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | 59.3% | — |
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. | 82.9% | — |
| Overview | ||
| Company | Meta | NVIDIA |
| Release date | Aug 5 2026 | Mar 18 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Muse Spark 1.2vsClaude Haiku 5.5Llama Nemotron (Nano/Super)vsClaude Haiku 5.5Muse Spark 1.2vsGPT-6.1 SolLlama Nemotron (Nano/Super)vsGPT-6.1 SolMuse Spark 1.2vsGemini 4 ArgonLlama Nemotron (Nano/Super)vsGemini 4 ArgonMuse Spark 1.2vsGrok 4.7Llama Nemotron (Nano/Super)vsGrok 4.7Muse Spark 1.2vsDeepSeek-V4.1-Flash-1017Llama Nemotron (Nano/Super)vsDeepSeek-V4.1-Flash-1017Muse Spark 1.2vsMistral Large 4Llama Nemotron (Nano/Super)vsMistral Large 4Frequently asked questions
Muse Spark 1.2 and Llama Nemotron (Nano/Super) don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Llama Nemotron (Nano/Super) shipped 505 days before Muse Spark 1.2, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Muse Spark 1.2) vs 128k (Llama Nemotron (Nano/Super)). Muse Spark 1.2 is closed, while Llama Nemotron (Nano/Super) is open weight.
Direct benchmark comparisons are unavailable — Muse Spark 1.2 and Llama Nemotron (Nano/Super) don't publish scores on any of the same benchmarks.