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Baseten vs Morph
Morph is a specialist model that merges code edits at 10,500+ tokens per second. Baseten hosts the main model. Coding agents can use both in one pipeline.
By The Subconscious Team · Updated
Baseten vs Morph: key differences
Morph does one job inside a coding agent. The main model writes only the changed lines, and Morph's Fast Apply merges them into the full file at 10,500+ tokens per second with up to 98% accuracy. Morph says this cuts token usage about 40% against full-file rewrites. Baseten does not compete on that step. It serves the main model, such as GLM 5.2, Kimi K3 or DeepSeek V4, and its KV cache-aware routing is tuned for agentic coding traffic. The natural setup puts the reasoning model on Baseten and the apply step on Morph.
Picking between them only makes sense for the file-editing step itself. Baseten could host a small merge model through Truss, but Morph already runs a 7B model trained only on code merging on custom CUDA kernels. Morph's 2 to 4% merge error rate means edits still need tests or linting before they ship. Morph also offers WarpGrep for repo search, Compact for context compression and its own fine-tuning. Baseten covers what Morph does not: general model serving, HIPAA and a 99.99% SLA.
What Baseten and Morph do
Baseten
Baseten runs two products. Model APIs serve a curated set of 13 open models, including DeepSeek V4, GLM 5.2, Kimi K3 and gpt-oss 120B, over endpoints that speak both the OpenAI Chat Completions shape and the Anthropic Messages shape. That dual compatibility means an existing OpenAI or Claude SDK, or a coding agent, points at Baseten with a base URL change. Dedicated deployments take any model you package with the open-source Truss CLI and bill per GPU minute, with an H100 at about $6.50 an hour.
Example models: GLM 5.2, gpt-oss 120B
Full Baseten profileMorph
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 profileShould you choose Baseten or Morph?
Baseten
Choose Baseten for
- Hosting the coding agent's main reasoning model
- Low first-token latency on each agent turn
- Compliance-bound serving of open models
Morph
Choose Morph for
- Applying lazy edits to large files at high speed
- Cutting output tokens from the frontier model
- Fast repo search and context compression in agents
Baseten vs Morph at a glance
| Attribute | Baseten | Morph |
|---|---|---|
| Model access | Open weights, 13 curated | Specialist models |
| Flagship models | GLM 5.2, DeepSeek V4, Kimi K3, gpt-oss 120B | morph-v3-fast, morph-v3-large |
| Speed | 0.49s TTFT, lowest measured | 10,500+ tok/s Fast Apply |
| Price | H100 about $6.50/hr dedicated | ~40% fewer tokens than full rewrites |
| Customization | Deploy any model with Truss | Fine-tuning offered |
| Deployment | Model APIs, dedicated, self-host | OpenAI-compatible API |
| Long context | Varies by model | Unknown |
Frequently asked questions
What is the difference between Baseten and Morph?
Morph is a specialist model that merges code edits at 10,500+ tokens per second. Baseten hosts the main model. Coding agents can use both in one pipeline.
When should I choose Baseten over Morph?
Hosting the coding agent's main reasoning model; Low first-token latency on each agent turn; Compliance-bound serving of open models.
When should I choose Morph over Baseten?
Applying lazy edits to large files at high speed; Cutting output tokens from the frontier model; Fast repo search and context compression in agents.
Is Baseten or Morph cheaper?
Baseten: H100 about $6.50/hr dedicated. Morph: ~40% fewer tokens than full rewrites. 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.