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. | — | 744B |
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 | 200k |
| 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 | $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. | $15.00 | $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. | $0.30 | $0.20 |
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 | $0.60GMICloud |
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 | $1.92GMICloud |
| $1,350Anthropic list price | $216GMICloud · fp8 | |
| 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. | 79% | 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. | 77.2% | 77.8% |
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. | 51% | 56.2% |
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. | 33.6% | 50.4% |
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% | 86% |
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% |
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. | 36% | — |
MCP Atlas · original harnessMulti-step tool use — pass rate under the original tool-use harness, before Scale changed the judge and retry handling in April 2026 | 43.8% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 75.9% |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | 17.7% | — |
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% | — |
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% | — |
GDPval-AAKnowledge work — Measures how well the AI does economically valuable knowledge work, judged against human experts. Shown as a rating (like a chess Elo) — higher is better. | 1276 | — |
MMMU-ProMultimodal reasoning — A tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | 63.4% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 77.8% | — |
| Overview | ||
| Company | Anthropic | Z.ai |
| Release date | Sep 29 2025 | Feb 11 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Sonnet 4.5vsGPT-6.1 TerraGLM-5vsGPT-6.1 TerraClaude Sonnet 4.5vsGemini 4 HydrogenGLM-5vsGemini 4 HydrogenClaude Sonnet 4.5vsMuse EmberGLM-5vsMuse EmberClaude Sonnet 4.5vsGrok 4.7GLM-5vsGrok 4.7Claude Sonnet 4.5vsDeepSeek-V4.1-Flash-1017GLM-5vsDeepSeek-V4.1-Flash-1017Claude Sonnet 4.5vsMistral Large 4GLM-5vsMistral Large 4Frequently asked questions
GLM-5 leads Claude Sonnet 4.5 on 4 of the 5 benchmarks they both report. GLM-5 is cheaper on both input and output: $1.00 vs $3.00 per million input tokens, and $3.20 vs $15.00 per million output tokens. Claude Sonnet 4.5 shipped 135 days before GLM-5, so benchmark comparisons should account for the intervening progress.
Context windows are 200k (Claude Sonnet 4.5) vs 200k (GLM-5). Claude Sonnet 4.5 is closed, while GLM-5 is open weight.
On BullshitBench v2, Claude Sonnet 4.5 leads at 79% vs GLM-5 at 28%. On SWE-Bench Verified, GLM-5 leads at 77.8% vs Claude Sonnet 4.5 at 77.2%. On Terminal-Bench 2.0, GLM-5 leads at 56.2% vs Claude Sonnet 4.5 at 51%. On Humanity's Last Exam · with tools, GLM-5 leads at 50.4% vs Claude Sonnet 4.5 at 33.6%. On GPQA Diamond, GLM-5 leads at 86% vs Claude Sonnet 4.5 at 83.4%.