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Together AI vs Morph
Morph is a specialist fast-apply model for coding agents, not a general host. Together can serve the main coding model while Morph merges its edits.
By The Subconscious Team · Updated
Together AI vs Morph: key differences
These are not substitutes. Morph builds small specialist models, led by Fast Apply: a frontier model writes only the changed lines, and a 7B merge model folds them into the full file at 10,500+ tokens per second with up to 98% accuracy. Morph says this cuts tokens about 40% against full-file rewrites. Its lineup also includes WarpGrep for repository search, Compact for context compression and Reflex for classification. Together is a general open-model platform. It hosts the big models that would write those edits, such as Kimi K3, DeepSeek V4 or GLM 5.2, plus fine-tuning and GPU clusters.
In a coding agent they slot together. The planner and editor model runs on Together, emits lazy edit snippets, and Morph applies them fast, trimming the most expensive output tokens from the main model's bill. Morph's 2 to 4% merge error rate means tests or linting still need to gate edits. Morph does serve general chat endpoints and offers fine-tuning, but it is not built to replace a broad host. Evaluate them as layers of the same stack.
What Together AI and Morph do
Together AI
Together AI is the broadest open-model platform in the category. One bill covers per-token serverless inference, batch at up to 50% off, provisioned throughput with a 99% SLA, dedicated deployments, raw GPU clusters, managed fine-tuning and code sandboxes for agents. The text catalog runs past thirty open models, including DeepSeek V4, Kimi K3, GLM 5.2, Qwen 3.8 and MiniMax M3, plus image, video, speech and embedding models. Token prices sit at parity with Fireworks and Baseten.
Example models: Kimi K3, DeepSeek V4 Pro
Full Together AI 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 Together AI or Morph?
Together AI
Choose Together AI for
- Hosting the main open coding model, like Kimi K3
- Fine-tuning a coding model on your own repositories
- Agent code sandboxes on the same bill
Morph
Choose Morph for
- Applying agent edits to large files in near real time
- Cutting output tokens versus full-file rewrites
- Fast repository search with WarpGrep
Together AI vs Morph at a glance
| Attribute | Together AI | Morph |
|---|---|---|
| Model access | Open weights | Specialist models |
| Flagship models | Kimi K3, DeepSeek V4, GLM 5.2, Qwen 3.8 | morph-v3-fast, morph-v3-large |
| Speed | 0.99s TTFT on DeepSeek V4 Pro | 10,500+ tok/s Fast Apply |
| Price | Parity with Fireworks and Baseten | ~40% fewer tokens than full rewrites |
| Customization | LoRA and full SFT; RL in beta | Fine-tuning offered |
| Deployment | Serverless, dedicated, GPU clusters | OpenAI-compatible API |
| Long context | 512K on DeepSeek V4 Pro | Unknown |
Frequently asked questions
What is the difference between Together AI and Morph?
Morph is a specialist fast-apply model for coding agents, not a general host. Together can serve the main coding model while Morph merges its edits.
When should I choose Together AI over Morph?
Hosting the main open coding model, like Kimi K3; Fine-tuning a coding model on your own repositories; Agent code sandboxes on the same bill.
When should I choose Morph over Together AI?
Applying agent edits to large files in near real time; Cutting output tokens versus full-file rewrites; Fast repository search with WarpGrep.
Is Together AI or Morph cheaper?
Together AI: Parity with Fireworks and Baseten. 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.