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Relace vs Runware
Relace speeds up coding agents with apply and search models. Runware generates images, video, audio and 3D cheaply. They serve unrelated jobs.
By The Subconscious Team · Updated
Relace vs Runware: key differences
There is almost no overlap here. Relace trains small models that act as tools for coding agents, such as relace-apply-3, which merges lazy edits at about 10,000 tokens per second, plus agentic codebase search and a 50,000 tokens per second compaction model. Runware sells media generation across 300+ priced models behind one request schema, with images from fractions of a cent and video billed per second, like Seedance 2.5 at about $0.10 a second at 480p. Runware lists text as a modality but calls LLM hosting a side line.
The only realistic shared product is an app builder that generates both code and media. Relace would apply AI edits to the user's codebase, and Runware would generate images or clips for the app itself, batching many tasks in one call. Each has its own gotcha: Relace returns an error past 128K tokens, and Runware's output URLs expire after seven days by default. Pick by the job, since neither can stand in for the other.
What Relace and Runware do
Relace
Relace started out training small, fast models that act as tools for coding agents, and by October 2026 its homepage led with open-model inference instead. It says it served 30 trillion tokens in the 30 days to late October and ranks among the top providers on OpenRouter. The hosted catalog covers DeepSeek V4 Pro, V4 Flash and V4.1 Flash, Kimi K3, GLM 5.3, GLM 5.3 Flash and GLM 5.2 over OpenAI and Anthropic compatible APIs, billed per token with no subscription. Prices run from $0.07 in and $0.14 out per million tokens on DeepSeek V4 Flash to $2.00 in and $14.00 out on Kimi K3, and Relace says it reprices often.
Example models: DeepSeek V4.1 Flash, Kimi K3, GLM 5.3, relace-apply-3
Full Relace profileRunware
Runware sells what it calls the lowest-cost API for media generation, and it claims more than 1M developers. One endpoint covers image, video, audio, 3D and text. Every request is a task with the same shape, so switching from a Kling video to a Seedream image mostly means changing the model ID. The published rate sheet lists 300+ priced models, with images from fractions of a cent to a few cents each and video billed per second, like Seedance 2.5 at about $0.10 a second at 480p.
Example models: Seedance 2.5, Qwen-Image-3.0
Full Runware profileShould you choose Relace or Runware?
Relace vs Runware at a glance
| Attribute | Relace | Runware |
|---|---|---|
| Model access | Open weights, plus specialist coding models | Hosted media models |
| Flagship models | DeepSeek V4.1 Flash, Kimi K3, GLM 5.3, relace-apply-3 | Seedance 2.5, Qwen-Image-3.0 |
| Speed | ~10,000 tok/s apply | Unknown |
| Price | $0.07 in, $0.50 out (DeepSeek V4.1 Flash) | Images from fractions of a cent |
| Customization | Unknown | Fine-tuned diffusion checkpoints |
| Deployment | Hosted API, OpenRouter or self-hosted | Unified API, serverless GPUs, bare metal |
| Long context | 128K on apply; varies by open model | Not applicable |
Frequently asked questions
What is the difference between Relace and Runware?
Relace speeds up coding agents with apply and search models. Runware generates images, video, audio and 3D cheaply. They serve unrelated jobs.
When should I choose Relace over Runware?
Applying AI edits in app builders and coding agents; Searching large repos for review and fixes; In-house deployment for private code.
When should I choose Runware over Relace?
Cheap image and short video generation at volume; Fine-tuned diffusion checkpoints; One schema across media types.
Is Relace or Runware cheaper?
Relace: $0.07 in, $0.50 out (DeepSeek V4.1 Flash). Runware: Images from fractions of a cent. The cheaper choice depends on the model and workload.
Which has more context, Relace or Runware?
Relace: 128K on apply; varies by open model. Runware: Not applicable.
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.