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 | 355B |
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 | 128k |
| 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. | — | $0.60 |
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. | — | $2.20 |
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. | — | $0.11 |
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. | — | $0.60Z.AI |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $2.20Z.AI |
| — | $230Z.ai 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. | — | 8% |
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. | — | 64.2% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 79.1% |
| Overview | ||
| Company | Meta | Z.ai |
| Release date | Apr 18 2024 | Jul 28 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
LLaMA 3 (8B/70B)vsClaude Haiku 5.5GLM-4.5vsClaude Haiku 5.5LLaMA 3 (8B/70B)vsGPT-6.1 SolGLM-4.5vsGPT-6.1 SolLLaMA 3 (8B/70B)vsGemini 4 ArgonGLM-4.5vsGemini 4 ArgonLLaMA 3 (8B/70B)vsGrok 4.7GLM-4.5vsGrok 4.7LLaMA 3 (8B/70B)vsDeepSeek-V4.1-Flash-1017GLM-4.5vsDeepSeek-V4.1-Flash-1017LLaMA 3 (8B/70B)vsMistral Large 4GLM-4.5vsMistral Large 4Frequently asked questions
LLaMA 3 (8B/70B) and GLM-4.5 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GLM-4.5 has a verified first-party API price: $0.60 per million input tokens and $2.20 per million output tokens. No pay-as-you-go API rate is tracked for LLaMA 3 (8B/70B). LLaMA 3 (8B/70B) shipped 466 days before GLM-4.5, so benchmark comparisons should account for the intervening progress.
LLaMA 3 (8B/70B) has 8B/70B parameters, while GLM-4.5 has 355B. Context windows are 8k (LLaMA 3 (8B/70B)) vs 128k (GLM-4.5).
Direct benchmark comparisons are unavailable — LLaMA 3 (8B/70B) and GLM-4.5 don't publish scores on any of the same benchmarks.