Compare AI models
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | — | 355B |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | — | 128k |
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $1.00 | $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. | $2.00 | $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. | $0.20 | $0.11 |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. 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 third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $2.20Z.AI |
| Benchmarks | ||
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. | 70.8% | 64.2% |
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% |
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 | SpaceXAI | Z.ai |
| Release date | Aug 28 2025 | Jul 28 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
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Grok Code Fast 1 leads GLM-4.5 on 1 of the 1 benchmark they both report (SWE-Bench Verified). GLM-4.5 is cheaper on input: $0.60 vs $1.00 per million tokens. Grok Code Fast 1 is cheaper on output: $2.00 vs $2.20 per million tokens. Figures are base-tier rates. GLM-4.5 shipped 31 days before Grok Code Fast 1, so benchmark comparisons should account for the intervening progress.
Grok Code Fast 1 is closed, while GLM-4.5 is open weight.
On SWE-Bench Verified, Grok Code Fast 1 leads at 70.8% vs GLM-4.5 at 64.2%.