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Meta vs Inference.net
Meta sells a new closed model API. Inference.net sells cheap batch on spare GPUs and a pipeline from production traces to a custom fine-tuned model.
By The Subconscious Team · Updated
Meta vs Inference.net: key differences
Inference.net is built for teams that want to own a model eventually. Its Inference Gateway routes traffic to open, closed or custom models under one key, records every request, turns it into eval and training data, and ends with a task-specific fine-tune on a dedicated GPU with a 99.99% uptime target. Its Batch API takes up to 1M requests per file on otherwise idle capacity. Meta sells a model to call, Muse Spark 1.3, at $1.25 in and $4.25 out, and ships Muse Glimmer weights for teams that want to self-host.
Both offer a data loop, pointed in opposite directions. Meta's Contributor tier gives you about 95% off if your prompts train Meta's models. Inference.net turns your traffic into training data for a model you control. For interactive agents, Meta is the more natural fit, since Inference.net's spare capacity suits batch better than strict real-time SLAs. For a narrow high-volume task that a smaller model could handle, Inference.net's loop may cut cost and latency, though it has few independent benchmarks to check against.
What Meta and Inference.net do
Meta
Meta has moved from open Llama releases toward its own closed API. Meta Superintelligence Labs builds the Muse family, and in July 2026 Meta launched the Meta Model API with Muse Spark 1.1, a multimodal reasoning model aimed at agentic coding, tool use and computer use. At Meta Connect in September, Meta made the API generally available globally. Muse Spark 1.3 has a 1M token context and costs $1.25 in and $4.25 out per million tokens, with cached input at $0.15. Muse Wonder, released October 12, 2026, sits above it for longer, harder agent work at $4 in and $20 out, also with 1M context. The endpoint speaks OpenAI Chat Completions, Anthropic Messages and a stateful agentic format.
Example models: Muse Wonder, Muse Spark 1.3, Muse Spark Open
Full Meta profileInference.net
Inference.net started as a buyer of last resort for idle GPU time. Its scheduler aggregates small unused chunks of capacity across data centers and runs models on them, and it passes the steep discounts it gets from those data centers on to customers. That origin still shows in its OpenAI compatible Batch API, which takes up to 1M requests per file with completion windows from 24 hours to 7 days and far higher headroom than synchronous limits.
Example models: GLM-5.2, Kimi K2.6, Schematron, customer fine-tunes
Full Inference.net profileShould you choose Meta or Inference.net?
Meta
Choose Meta for
- Interactive coding and tool-use agents
- Multimodal assistants with 1M context
- Cheap experiments on the Contributor tier
Inference.net
Choose Inference.net for
- Turning production traffic into your own fine-tuned model
- Large batch jobs on discounted spare capacity
- One gateway key across open, closed and custom models
Meta vs Inference.net at a glance
| Attribute | Meta | Inference.net |
|---|---|---|
| Model access | Closed API; open Muse Spark Open and Glimmer | Open, closed and custom |
| Flagship models | Muse Wonder, Muse Spark 1.3, Muse Spark Open | Schematron, ClipTagger, customer fine-tunes |
| Speed | ~145–233 tok/s on Muse Spark 1.3 | Batch windows of 24h to 7 days |
| Price | $1.25–$4 in, $4.25–$20 out; Contributor tier cheaper | Free pay-as-you-go; Growth $250/mo; B200 $9.98/hr |
| Customization | Open Muse weights to fine-tune | Distill traces into custom models |
| Deployment | Meta Model API, Oracle Cloud | Batch API, gateway, dedicated GPUs |
| Long context | 1M | Varies by model |
Frequently asked questions
What is the difference between Meta and Inference.net?
Meta sells a new closed model API. Inference.net sells cheap batch on spare GPUs and a pipeline from production traces to a custom fine-tuned model.
When should I choose Meta over Inference.net?
Interactive coding and tool-use agents; Multimodal assistants with 1M context; Cheap experiments on the Contributor tier.
When should I choose Inference.net over Meta?
Turning production traffic into your own fine-tuned model; Large batch jobs on discounted spare capacity; One gateway key across open, closed and custom models.
Is Meta or Inference.net cheaper?
Meta: $1.25–$4 in, $4.25–$20 out; Contributor tier cheaper. Inference.net: Free pay-as-you-go; Growth $250/mo; B200 $9.98/hr. The cheaper choice depends on the model and workload.
Which has more context, Meta or Inference.net?
Meta: 1M. Inference.net: Varies by model.
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.