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Cohere vs Morph
Morph is a narrow tool: a 7B model that applies coding-agent edits at 10,500+ tokens per second. Cohere is a full model vendor for enterprise RAG and agents.
By The Subconscious Team · Updated
Cohere vs Morph: key differences
Morph does not replace a general model; it sits next to one. A frontier 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, according to Morph. The lineup also includes WarpGrep for repository search, Compact for context compression and Reflex for classification, all behind an OpenAI-compatible API with fine-tuning offered. Cohere sells the main models: Command A at $2.50 in and $10 out with 256K context, Command A+ under Apache 2.0, the 30B North Mini Code model, and the Embed 4 and Rerank 4 retrieval stack.
The practical comparison is scope and deployment. Morph's merges still carry a 2 to 4% error rate, so edits need tests or linting before they ship, and it serves coding agents, IDEs and CI pipelines specifically. Cohere covers broader enterprise work such as RAG, multilingual assistants and internal agents through North, and it deploys into a VPC or on-prem with fine-tuning. Command A+ trails the latest DeepSeek and GLM models on agentic coding, so a coding agent might pair a stronger generator with Morph. An enterprise search or document assistant has little use for Morph and fits Cohere directly.
What Cohere and Morph do
Cohere
Cohere is a Toronto-based lab that sells models and platforms to banks, governments and large enterprises rather than consumers. Its generative line is the Command family. Command A+, released May 20, 2026, is a 218B-parameter mixture-of-experts model with 25B active, published under Apache 2.0 with a 128K context window, and it combines reasoning, vision, translation and tool use in one set of weights. Command A has a 256K window and lists at $2.50 in and $10 out per million tokens, while Command R7B costs $0.0375 in. June 2026 added North Mini Code, a 30B Apache 2.0 coding model, and the lineup also includes Aya multilingual models and Transcribe for speech.
Example models: Command A+, Command A, Embed 4, Rerank 4
Full Cohere 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 Cohere or Morph?
Cohere vs Morph at a glance
| Attribute | Cohere | Morph |
|---|---|---|
| Model access | Closed, plus open Command A+ | Specialist models |
| Flagship models | Command A+, Command A, Embed 4, Rerank 4 | morph-v3-fast, morph-v3-large |
| Speed | 375 tok/s on Command A+ W4A4, per Cohere | 10,500+ tok/s Fast Apply |
| Price | $0.0375–$2.50 in, $0.15–$10 out per 1M | ~40% fewer tokens than full rewrites |
| Customization | Enterprise fine-tuning, incl. private | Fine-tuning offered |
| Deployment | API, Bedrock, Azure, OCI, VPC, on-prem | OpenAI-compatible API |
| Long context | 256K on Command A; 128K on A+ | Unknown |
Frequently asked questions
What is the difference between Cohere and Morph?
Morph is a narrow tool: a 7B model that applies coding-agent edits at 10,500+ tokens per second. Cohere is a full model vendor for enterprise RAG and agents.
When should I choose Cohere over Morph?
Enterprise RAG over internal documents; Internal agents built on North; Private deployment with fine-tuning.
When should I choose Morph over Cohere?
Applying model edits to large code files; Fast repository search inside coding agents; Cutting frontier-model output tokens.
Is Cohere or Morph cheaper?
Cohere: $0.0375–$2.50 in, $0.15–$10 out per 1M. 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.