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. | 500k | 200k |
| 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. | $2.00 | $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. | $6.00 | $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. | $0.50 | $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. | $2.00xAI | $0.43Venice |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $6.00xAI | $1.75Venice |
These models have no shared benchmark scores. | ||
CheatBenchCheating rate — Measures how often AI agents try to cheat on difficult assignments, such as reading hidden answers, copying work or manipulating grading. The overall score gives equal weight to ten categories; the sycophancy category measures how far an agent shifts its beliefs toward a user's stated views. Scores describe each model in its tested agent setup, and task success is measured separately. Lower is better. | 78% | — |
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. | — | 68% |
CursorBench 4.0Agentic coding — Cursor's own test of coding agents on ambiguous, multi-file tasks taken from real Cursor sessions — editing, refactoring, investigating a codebase, understanding what the user meant, managing jobs and following a design. Cursor runs each model at several reasoning efforts; each release here carries the score of its best listed effort. Scores aren't comparable with earlier CursorBench versions. Higher is better. | 46.3% | — |
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | 71% | — |
FrontierSWE V2Ultra-long-horizon coding — Engineering problems that would occupy a person for days: systems implementation, performance work, scientific computing and AI research, with up to twenty hours per task. Partial credit is awarded, because finishing one outright is still rare. Higher is better. | 29% | — |
SWE-Marathon v1.1Multi-hour coding — Twenty multi-hour programming jobs — rebuilding a library, cloning a product, training a model, making something faster. A run counts only when every check passes, hidden tests and interface review included. Higher is better. | 46% | — |
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. | 94% | — |
Supabase Evals · with skillsSupabase coding — Supabase's own open benchmark: a coding agent is dropped into a real Supabase project and asked to do real work — set up a schema, fix a broken security policy, debug an Edge Function — and every run is checked against a live Supabase stack. This is the headline number, where the agent has Supabase's own skills loaded, as most people building on Supabase would. The score is the share of scenarios it got right. Higher is better. | 89.9% | — |
Supabase Evals · no skillsSupabase coding — The same Supabase scenarios, but with none of Supabase's skills loaded — so it measures what the model already knows about building on Supabase, rather than how well it follows Supabase's supplied instructions. Higher is better. | 78.3% | — |
CADGenBenchCAD generation — Can the AI turn a written design brief into a real CAD model? What it produces has to run as a program and come out the right shape. Higher is better. | 44.4% | — |
Terminal-Bench 4.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Version 4.0 recalibrated how much time, CPU and memory each task gets, removed eight tasks and fixed nineteen, so fewer runs fail for reasons that have nothing to do with the model. Scores are not comparable with earlier versions. Higher is better. | 38% | — |
CyberGymCybersecurity — Tests the AI on cybersecurity challenges — finding and exploiting software weaknesses inside a safe sandbox. Higher is better. | 80.3% | — |
CVE-BenchCybersecurity — Can the AI exploit real, publicly known security holes in web applications, in a sandbox built to resemble a live service? A measure of raw capability: it is run without the safeguards a deployed model carries. Higher is better. | 37.7% | — |
CathedralBenchCybersecurity — An outside red team's test: chaining several exploits together to get through a hardened system, scored on the hardest tasks only and run without safeguards, to show what the underlying model can do. Higher is better. | 29% | — |
LatchBio Capabilities v1.0Biology — Can the AI do a working biologist's analysis — opening real experimental files and reaching a defensible conclusion across genomics, drug discovery and pathogen surveillance? Averaged over eleven separate tests. Higher is better. | 44.5% | — |
EEBenchElectrical engineering — Can the AI design electronics? It is asked for circuits and chip layouts, and the designs are checked for whether they would physically work — not whether the answer reads well. Higher is better. | 66% | — |
Harvey's Legal Agent BenchmarkAgentic legal work — Harvey's test of whether an AI agent can complete real legal work — drafting and reviewing documents, working with spreadsheets and presentations, and navigating files the way a lawyer's assistant would. Higher is better. | 19.6% | — |
HealthBench ProfessionalHealth — Realistic health conversations graded against detailed rubrics written by physicians — can the AI respond the way a careful medical professional would? Higher is better. | 56.7% | — |
GDPval-AA v2.1Knowledge work — economically valuable knowledge work (v2.1, Crowd-BT Elo fit) | 1695 | — |
AA-Briefcase v1.1Knowledge work — Artificial Analysis agentic office-work eval (Elo, v1.1 rating fit) | 1657 | — |
threejsevalCommunity preference (Three.js) — Every model gets the same prompt — "the Eiffel Tower", "a glass fishbowl", "a robot arm picking toys into a box" — and builds a 3D scene in Three.js. Real people then see two scenes side by side, names hidden, and vote for the one they prefer. The votes become a chess-style Elo rating on threejseval.com, averaged across all the prompts. It measures whether the scene looks and moves right to a human eye, not whether the code passes a test. Higher is better. | 1621 | — |
| Overview | ||
| Company | SpaceXAI | Z.ai |
| Release date | Sep 21 2026 | Sep 30 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Grok 4.7vsClaude Sonnet 5.5GLM-4.6vsClaude Sonnet 5.5Grok 4.7vsGPT-6.1 SolGLM-4.6vsGPT-6.1 SolGrok 4.7vsGemini 4 ArgonGLM-4.6vsGemini 4 ArgonGrok 4.7vsMuse Spark 1.3GLM-4.6vsMuse Spark 1.3Grok 4.7vsDeepSeek-V4.1-FlashGLM-4.6vsDeepSeek-V4.1-FlashGrok 4.7vsMistral Large 4GLM-4.6vsMistral Large 4Frequently asked questions
Grok 4.7 and GLM-4.6 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. GLM-4.6 is cheaper on both input and output: $0.60 vs $2.00 per million input tokens, and $2.20 vs $6.00 per million output tokens. Figures are base-tier rates. GLM-4.6 shipped 356 days before Grok 4.7, so benchmark comparisons should account for the intervening progress.
Context windows are 500k (Grok 4.7) vs 200k (GLM-4.6). Grok 4.7 is closed, while GLM-4.6 is open weight.
Direct benchmark comparisons are unavailable — Grok 4.7 and GLM-4.6 don't publish scores on any of the same benchmarks.