// Available general modelsFast general models for agent loops

Frontier coding models,
served on custom kernels
Output speed
Codegen-specific optimizations and custom GPU kernels. Up to 150 tok/s on DeepSeek V4 Flash, measured on private deployments with custom speculators. Public endpoints run on shared capacity.
The lineup
Open-weight frontier models with long context, served and billed per token. No per-seat fees.
morph-kimik3 // 2.8T MoE, 1M contextmorph-kimik3-fast // same weights, latency-tunedmorph-glm52-744b // 744B MoE, 1M contextmorph-minimax3-428b // 428B MoE, 256k contextmorph-dsv4flash // 1M context, fastmorph-qwen38-27b // dense, low latencyBuilt for agent loops, not chat
Long tool calls expire your cache before the next turn arrives. Tag turns with run_id and the scheduler keeps it hot.
// One field. Every turn of a run shares it.await client.chat.completions.create({ model: "morph-kimik3", messages, run_id: "run-8f2c1a", // sticky worker + priority resume // parent_run_id: "run-root", // subagent fan-out lineage});// run_final: true releases the run when the loop ends (not billed)Standby: half price when nobody is waiting
service_tier: "standby" pays 50% of GLM-5.2's rate. Runs on spare capacity, sheds a fast 429 when busy — batch jobs, evals, and nightly runs skip interactive pricing.
// GLM-5.2 standby — $0.55/M in, $0.11/M cached, $2.05/M outawait client.chat.completions.create({ model: "morph-glm52-744b", messages, service_tier: "standby", // 50% off; 429 + Retry-After when busy});// Rejections generate nothing and bill nothing. Retry with// backoff, or resend as "default" if it can't wait.Built for production agent workloads
Every agent will write code. We bet the stack on it.
So we tune every layer for that one workload: custom GPU kernels, speculative decoding shaped around code, and serving built for the agent loop instead of general chat. Not general infrastructure with code bolted on.
The fastest endpoints are private deployments
The headline speeds on this page come from dedicated deployments, not the shared public endpoints. At scale we build the deployment around your traffic: speculators trained on it, caching tuned to it, and pricing well under public rates.
tokens per day served across private deployments
trained on your traffic for higher acceptance rates
prefix and KV caching tuned to your workload
large discounts over public per-token rates