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/70B/405B | — |
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. | 128k | 200k |
| 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. | — | $1.10 |
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. | — | $4.40 |
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. | — | $0.55 |
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. | $0.02DeepInfra | $1.10OpenAI |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.04DeepInfra | $4.40OpenAI |
| $6.00DeepInfra · fp8 | $440OpenAI list price | |
| 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. | 14% | — |
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. | — | 49.3% |
| Overview | ||
| Company | Meta | OpenAI |
| Release date | Jul 23 2024 | Jan 31 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
LLaMA 3.1vsClaude Haiku 5.5o3-minivsClaude Haiku 5.5LLaMA 3.1vsGemini 4 Argono3-minivsGemini 4 ArgonLLaMA 3.1vsGrok 4.7o3-minivsGrok 4.7LLaMA 3.1vsDeepSeek-V4.1-Flash-1017o3-minivsDeepSeek-V4.1-Flash-1017LLaMA 3.1vsMistral Large 4o3-minivsMistral Large 4LLaMA 3.1vsKimi K3o3-minivsKimi K3Frequently asked questions
LLaMA 3.1 and o3-mini don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only o3-mini has a verified first-party API price: $1.10 per million input tokens and $4.40 per million output tokens. No pay-as-you-go API rate is tracked for LLaMA 3.1. LLaMA 3.1 shipped 192 days before o3-mini, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (LLaMA 3.1) vs 200k (o3-mini). LLaMA 3.1 is open weight, while o3-mini is closed.
Direct benchmark comparisons are unavailable — LLaMA 3.1 and o3-mini don't publish scores on any of the same benchmarks.