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. | 200k | 8k |
| 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. | $3.00 | — |
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. | $15.00 | — |
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. | $0.30 | — |
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. | $3.00Amazon Bedrock | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $15.00Amazon Bedrock | — |
| $1,350Anthropic 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. | 79% | — |
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. | 77.2% | — |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | 39% | — |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | 13.6% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 83.4% | — |
OSWorld-VerifiedAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | 61.4% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 68% | — |
| Overview | ||
| Company | Anthropic | Meta |
| Release date | Sep 29 2025 | Apr 18 2024 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Sonnet 4.5vsGPT-6.1 SolLLaMA 3 (8B/70B)vsGPT-6.1 SolClaude Sonnet 4.5vsGemini 4 ArgonLLaMA 3 (8B/70B)vsGemini 4 ArgonClaude Sonnet 4.5vsGrok 4.7LLaMA 3 (8B/70B)vsGrok 4.7Claude Sonnet 4.5vsDeepSeek-V4.1-Flash-1017LLaMA 3 (8B/70B)vsDeepSeek-V4.1-Flash-1017Claude Sonnet 4.5vsMistral Large 4LLaMA 3 (8B/70B)vsMistral Large 4Claude Sonnet 4.5vsKimi K3LLaMA 3 (8B/70B)vsKimi K3Frequently asked questions
Claude Sonnet 4.5 and LLaMA 3 (8B/70B) don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Claude Sonnet 4.5 has a verified first-party API price: $3.00 per million input tokens and $15.00 per million output tokens. No pay-as-you-go API rate is tracked for LLaMA 3 (8B/70B). LLaMA 3 (8B/70B) shipped 529 days before Claude Sonnet 4.5, so benchmark comparisons should account for the intervening progress.
Context windows are 200k (Claude Sonnet 4.5) vs 8k (LLaMA 3 (8B/70B)). Claude Sonnet 4.5 is closed, while LLaMA 3 (8B/70B) is open weight.
Direct benchmark comparisons are unavailable — Claude Sonnet 4.5 and LLaMA 3 (8B/70B) don't publish scores on any of the same benchmarks.