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. | — | 109B |
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 | 10M |
| 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. | $1.50 | — |
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. | $7.50 | — |
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. | $0.075 | — |
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. | — | $0.08DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.24DeepInfra |
| $675Google list price | $35.00DeepInfra · fp8 | |
| 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% |
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. | — | 32.8% |
Terminal-Bench-Science 0.1Agentic scientific computing — The same command-line setup as Terminal-Bench, pointed at scientific work: the AI has to drive research tooling and computational workflows through to a result, rather than administer a machine. Version 0.1 is the first release of the task set, and scores run lower than on the general board. Higher is better. | 51.4% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 57.2% |
MMLU-ProKnowledge — accuracy on the ten-option MMLU-Pro benchmark | — | 74.3% |
MGSMMultilingual math — solving grade-school word problems translated into multiple languages | — | 90.6% |
OSWorld 2.1 · offlineAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Version 2.1, in its offline setting. Scores are not comparable with version 2.0. Higher is better. | 77.6% | — |
AutomationBench (Artificial Analysis)Business workflows — The same business-workflow test, run by Artificial Analysis rather than by the lab, and graded strict pass@1: a workflow counts only if the whole of it is right on the first attempt. Read it as a separate board from the lab-run AutomationBench scores. Higher is better. | 76.1% | — |
Vending-Bench 2 (USD)Business simulation — ending bank balance in US dollars in Andon Labs’ vending-business simulation; higher is better | 14549 | — |
Chartography · no toolsChart tasks — The same chart-centred test with no tools: the AI has to read each chart unaided. Scores run far lower than the with-tools version, so read the two as separate tests. Higher is better. | 66.6% | — |
MathVistaMultimodal reasoning — mathematical reasoning about visual information | — | 70.7% |
MMMU-Pro · standard and vision meanMultimodal reasoning — average across the standard and vision sets | — | 52.2% |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | — | 69.4% |
| Overview | ||
| Company | Meta | |
| Release date | Nov 12 2026 | Apr 5 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 4 HydrogenvsClaude Fable 5.2LLaMA 4 ScoutvsClaude Fable 5.2Gemini 4 HydrogenvsGPT-6.1 TerraLLaMA 4 ScoutvsGPT-6.1 TerraGemini 4 HydrogenvsGrok 4.7LLaMA 4 ScoutvsGrok 4.7Gemini 4 HydrogenvsDeepSeek-V4.1-Flash-1017LLaMA 4 ScoutvsDeepSeek-V4.1-Flash-1017Gemini 4 HydrogenvsMistral Large 4LLaMA 4 ScoutvsMistral Large 4Gemini 4 HydrogenvsKimi K3.1 ProLLaMA 4 ScoutvsKimi K3.1 ProFrequently asked questions
Gemini 4 Hydrogen and LLaMA 4 Scout don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Gemini 4 Hydrogen has a verified first-party price for an available API: $1.50 per million input tokens and $7.50 per million output tokens. No currently available pay-as-you-go API rate is tracked for LLaMA 4 Scout. LLaMA 4 Scout shipped 586 days before Gemini 4 Hydrogen, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Gemini 4 Hydrogen) vs 10M (LLaMA 4 Scout). Gemini 4 Hydrogen is closed, while LLaMA 4 Scout is open weight.
Direct benchmark comparisons are unavailable — Gemini 4 Hydrogen and LLaMA 4 Scout don't publish scores on any of the same benchmarks.