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 | — |
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. | 8k | 400k |
| 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. | — | $1.25 |
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. | — | $10.00 |
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. | — | $0.125 |
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. | — | $0.625OpenAI |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $5.00OpenAI |
| — | $750OpenAI 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. | — | 25% |
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. | — | 76.3% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 88.1% |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | — | 76% |
| Overview | ||
| Company | Meta | OpenAI |
| Release date | Apr 18 2024 | Nov 12 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
LLaMA 3 (8B/70B)vsClaude Haiku 5.5GPT-5.1vsClaude Haiku 5.5LLaMA 3 (8B/70B)vsGemini 4 ArgonGPT-5.1vsGemini 4 ArgonLLaMA 3 (8B/70B)vsGrok 4.7GPT-5.1vsGrok 4.7LLaMA 3 (8B/70B)vsDeepSeek-V4.1-Flash-1017GPT-5.1vsDeepSeek-V4.1-Flash-1017LLaMA 3 (8B/70B)vsMistral Large 4GPT-5.1vsMistral Large 4LLaMA 3 (8B/70B)vsKimi K3GPT-5.1vsKimi K3Frequently asked questions
LLaMA 3 (8B/70B) and GPT-5.1 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-5.1 has a verified first-party API price: $1.25 per million input tokens and $10.00 per million output tokens. No pay-as-you-go API rate is tracked for LLaMA 3 (8B/70B). LLaMA 3 (8B/70B) shipped 573 days before GPT-5.1, so benchmark comparisons should account for the intervening progress.
Context windows are 8k (LLaMA 3 (8B/70B)) vs 400k (GPT-5.1). LLaMA 3 (8B/70B) is open weight, while GPT-5.1 is closed.
Direct benchmark comparisons are unavailable — LLaMA 3 (8B/70B) and GPT-5.1 don't publish scores on any of the same benchmarks.