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Inference.net vs Particle.AI
Both sell cheap open-model tokens. Particle.AI offers low-priced Flash models with 1M context in real time; Inference.net offers batch and distillation.
By The Subconscious Team · Updated
Inference.net vs Particle.AI: key differences
Particle.AI and Inference.net both push down cost per token, through different channels. Particle serves a few Flash-class models on Vercel AI Gateway, such as GLM 5.3 Flash at $0.10 in and $0.40 out and DeepSeek V4 Flash 0731 at $0.14 in and $0.28 out, all with 1M context and cache reads at $0.03. Inference.net runs on aggregated spare GPU capacity and sells a Batch API for up to 1M requests per file with 24-hour to 7-day windows. Particle's prices are listed on the gateway, while Inference.net has few public pricing comparisons.
Particle is a synchronous API, though its DeepSeek V4.1 Flash listing shows 3.5 seconds of latency. Inference.net's batch model gives up immediacy for headroom and discount, and it adds a path from captured traffic to a fine-tuned task model on a dedicated GPU. Each has a limited public track record. Cheap real-time calls on Flash models fit Particle. Huge offline jobs and custom distillation fit Inference.net.
What Inference.net and Particle.AI do
Inference.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: open catalog models plus customer fine-tunes served on dedicated GPUs
Full Inference.net profile
Particle.AI
Particle AI is an early San Francisco infrastructure startup with a mission to make intelligence as cheap and abundant as electricity. The team works on post-training, inference optimization and distributed systems, all aimed at pushing down cost per unit of intelligence. It is still hiring its founding team and works fully in person. Public detail about funding and founders is thin as of this writing.
Example models: DeepSeek V4.1 Flash, GLM 5.3 Flash
Full Particle.AI profileShould you choose Inference.net or Particle.AI?
Inference.net
Choose Inference.net for
- Offline jobs that can wait a day or more
- Custom distilled models from captured traces
- Workloads that need models beyond a few Flash options
Particle.AI
Choose Particle.AI for
- Cheap synchronous calls on DeepSeek and GLM Flash
- 1M context with $0.03 cache reads
- Trying a low-cost route with no new contract
Inference.net vs Particle.AI at a glance
| Attribute | Inference.net |  Particle.AI |
|---|---|---|
| Model access | Open, closed and custom | Open weights |
| Flagship models | Customer fine-tunes | DeepSeek V4.1 Flash, GLM 5.3 Flash |
| Speed | Batch windows of 24h to 7 days | ~157 tok/s on DeepSeek V4.1 Flash |
| Price | Discounted spare GPU capacity | $0.10 in, $0.40 out (GLM 5.3 Flash) |
| Customization | Distill traces into custom models | Unknown |
| Deployment | Batch API, gateway, dedicated GPUs | Via Vercel AI Gateway |
| Long context | Varies by model | 1M |
Frequently asked questions
What is the difference between Inference.net and Particle.AI?
Both sell cheap open-model tokens. Particle.AI offers low-priced Flash models with 1M context in real time; Inference.net offers batch and distillation.
When should I choose Inference.net over Particle.AI?
Offline jobs that can wait a day or more; Custom distilled models from captured traces; Workloads that need models beyond a few Flash options.
When should I choose Particle.AI over Inference.net?
Cheap synchronous calls on DeepSeek and GLM Flash; 1M context with $0.03 cache reads; Trying a low-cost route with no new contract.
Is Inference.net or Particle.AI cheaper?
Inference.net: Discounted spare GPU capacity. Particle.AI: $0.10 in, $0.40 out (GLM 5.3 Flash). The cheaper choice depends on the model and workload.
Which has more context, Inference.net or Particle.AI?
Inference.net: Varies by model. Particle.AI: 1M.
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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.