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. | — | 744B |
| 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. | $1.00 | $1.00 |
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. | $5.00 | $3.20 |
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. | $0.10 | $0.20 |
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. | $1.00Amazon Bedrock | $0.60GMICloud |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $5.00Amazon Bedrock | $1.92GMICloud |
| Benchmarks | ||
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. | 77% | 28% |
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. | 73.3% | 77.8% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 73% | 86% |
ProgramBenchProgram reconstruction — The AI receives a working program and its documentation, then builds a replacement from scratch without the original source code, internet access or decompilation. The score is the percentage of 200 programs that pass every behavioral test. We record each model's best published mini-SWE-agent result, including higher reasoning efforts where available. Partial test-pass rates and almost-solved programs do not count toward this score. Equal scores share a rank here; the official board also uses partial progress to break ties. Higher is better. | 0% | — |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | — | 73.3% |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | — | 56.2% |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 75.9% |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 50.4% |
| Overview | ||
| Company | Anthropic | Z.ai |
| Release date | Oct 15 2025 | Feb 12 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Haiku 4.5vsGPT-6 SolGLM-5vsGPT-6 SolClaude Haiku 4.5vsGemini 3.8 FlashGLM-5vsGemini 3.8 FlashClaude Haiku 4.5vsMuse Spark 1.3GLM-5vsMuse Spark 1.3Claude Haiku 4.5vsGrok 4.7GLM-5vsGrok 4.7Claude Haiku 4.5vsDeepSeek-V4.1-FlashGLM-5vsDeepSeek-V4.1-FlashClaude Haiku 4.5vsMistral Medium 3.5GLM-5vsMistral Medium 3.5Frequently asked questions
GLM-5 leads Claude Haiku 4.5 on 2 of the 3 benchmarks they both report (BullshitBench v2, SWE-Bench Verified, GPQA Diamond). Both charge $1.00 per million input tokens. GLM-5 is cheaper on output: $3.20 vs $5.00 per million tokens. Claude Haiku 4.5 shipped 120 days before GLM-5, so benchmark comparisons should account for the intervening progress.
Claude Haiku 4.5 is closed, while GLM-5 is open weight.
On BullshitBench v2, Claude Haiku 4.5 leads at 77% vs GLM-5 at 28%. On SWE-Bench Verified, GLM-5 leads at 77.8% vs Claude Haiku 4.5 at 73.3%. On GPQA Diamond, GLM-5 leads at 86% vs Claude Haiku 4.5 at 73%.