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SambaNova vs Morph
SambaNova hosts large open LLMs with fast decode. Morph runs a small specialist model that merges code edits at 10,500+ tokens per second. A main model and an apply tool, not rivals.
By The Subconscious Team · Updated
SambaNova vs Morph: key differences
Morph does not compete with general hosts. Its Fast Apply model takes the changed lines a frontier model writes and merges them into the full file at 10,500+ tokens per second with up to 98% accuracy, using a 7B model trained only on code merging. It also offers WarpGrep for repo search, Compact for context compression and Reflex for classification. SambaNova is a general open-model host on custom silicon, serving models like MiniMax M2.7 and GPT-OSS 120B, and it targets interactive coding agents with fast decode.
In a coding agent they would sit side by side. The main model on SambaCloud plans and writes edit snippets, and Morph applies them, which Morph says cuts about 40% of tokens against full-file rewrites. That combination keeps both the reasoning step and the edit step fast. Morph's 2 to 4% merge error rate means tests or linting still matter. SambaNova's public catalog is smaller than GPU clouds, so check that the model you want is served before building on it.
What SambaNova and Morph do
SambaNova
SambaNova designs its own inference chip, the Reconfigurable Dataflow Unit, and sells fast tokens on large open models through SambaCloud. The RDU maps the model graph onto the chip to cut trips to off-chip memory. A three-tier memory design of SRAM, HBM and bulk DRAM lets one system host very large models and hot swap between several of them in milliseconds. SambaCloud serves models like MiniMax M2.7, DeepSeek, Gemma 4 31B and GPT-OSS 120B, with speeds reported by Artificial Analysis.
Example models: MiniMax M2.7, GPT-OSS 120B
Full SambaNova 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 SambaNova or Morph?
SambaNova
Choose SambaNova for
- The main reasoning model for a fast coding agent.
- Large open models with quick decode.
- Switching between models inside one agent.
Morph
Choose Morph for
- Merging model edits into large files in milliseconds.
- Cutting output tokens compared with full rewrites.
- High-volume code editing in CI or sandboxes.
SambaNova vs Morph at a glance
| Attribute | SambaNova | Morph |
|---|---|---|
| Model access | Open weights | Specialist models |
| Flagship models | MiniMax M2.7, GPT-OSS 120B, DeepSeek | morph-v3-fast, morph-v3-large |
| Speed | ~820 tok/s on MiniMax M2.7 (SN50) | 10,500+ tok/s Fast Apply |
| Price | $0.22 in, $0.59 out (GPT-OSS 120B) | ~40% fewer tokens than full rewrites |
| Customization | Unknown | Fine-tuning offered |
| Deployment | SambaCloud, racks for neoclouds | OpenAI-compatible API |
| Long context | Up to 192K (MiniMax M2.7) | Unknown |
Frequently asked questions
What is the difference between SambaNova and Morph?
SambaNova hosts large open LLMs with fast decode. Morph runs a small specialist model that merges code edits at 10,500+ tokens per second. A main model and an apply tool, not rivals.
When should I choose SambaNova over Morph?
The main reasoning model for a fast coding agent; Large open models with quick decode; Switching between models inside one agent.
When should I choose Morph over SambaNova?
Merging model edits into large files in milliseconds; Cutting output tokens compared with full rewrites; High-volume code editing in CI or sandboxes.
Is SambaNova or Morph cheaper?
SambaNova: $0.22 in, $0.59 out (GPT-OSS 120B). 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.