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Morph vs Wafer
Two routes to faster coding agents. Wafer tunes the serving stack for big open models; Morph adds a small specialist model for the apply step.
By The Subconscious Team · Updated
Morph vs Wafer: key differences
Wafer and Morph both chase speed for coding agents, at different layers. Wafer serves big open models like Qwen 3.5 397B, GLM 5.1 and DeepSeek V4 Pro on stacks its own agents tune, and reports 2x to 2.8x speedups over stock vLLM or SGLang. Its Wafer Pass, from $10 a week, drops into Claude Code, Cline and OpenHands. Morph does not serve a main model at all. It merges that model's edits into files at 10,500+ tokens per second, and offers WarpGrep for search and Compact for context compression.
They stack cleanly. A team could run the main model on Wafer for faster reasoning and use Morph for apply, so the main model writes only changed lines, which Morph says cuts tokens about 40% against full rewrites. Both companies' numbers are self-reported, and Wafer's speedups are measured against stock baselines, not tuned hosts. Wafer is also very young with a small catalog. Morph's merge error rate of 2 to 4% means edits still need tests or linting.
What Morph and Wafer do
Morph
Morph builds small, very fast specialist models that sit beside a big coding model inside an agent. Its flagship is Fast Apply. The frontier model writes only the changed lines with // ... existing code ... markers, and Morph merges them into the full file at 10,500+ tokens per second with up to 98% accuracy. It is the same idea behind Cursor's instant apply, offered as an OpenAI-compatible API.
Example models: morph-v3-fast, morph-v3-large
Full Morph profileWafer
Wafer builds AI agents that act as GPU performance engineers, then sells inference on the stacks those agents tune. The company came out of Y Combinator's Summer 2025 batch as a "Cursor for CUDA" that turned slow PyTorch into custom kernels. Founders Emilio Andere and Steven Arellano are based in San Francisco. Its agents profile a workload, generate candidate configs across batching, decoding, quantization, engines, kernels and hardware, measure each one and deploy the winner.
Example models: Qwen 3.5 397B Turbo, GLM 5.1 Turbo
Full Wafer profileShould you choose Morph or Wafer?
Morph vs Wafer at a glance
| Attribute | Morph | Wafer |
|---|---|---|
| Model access | Specialist models | Open weights |
| Flagship models | morph-v3-fast, morph-v3-large | Qwen 3.5 397B Turbo, GLM 5.1 Turbo |
| Speed | 10,500+ tok/s Fast Apply | 2–2.8x vs stock vLLM or SGLang |
| Price | ~40% fewer tokens than full rewrites | Wafer Pass from $10 a week |
| Customization | Fine-tuning offered | Agent-tuned dedicated deployments |
| Deployment | OpenAI-compatible API | Serverless pass, dedicated |
| Long context | Unknown | Varies by model |
Frequently asked questions
What is the difference between Morph and Wafer?
Two routes to faster coding agents. Wafer tunes the serving stack for big open models; Morph adds a small specialist model for the apply step.
When should I choose Morph over Wafer?
Speeding up the file-edit step specifically; Cutting output tokens from any main model; Repository search and context compression.
When should I choose Wafer over Morph?
Faster big open models as the main agent model; Flat-rate access inside Claude Code or Cline; Dedicated endpoints tuned to a latency SLO.
Is Morph or Wafer cheaper?
Morph: ~40% fewer tokens than full rewrites. Wafer: Wafer Pass from $10 a week. The cheaper choice depends on the model and workload.
Related comparisons
Running long-horizon agents?
If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.