Pricing
How sference bills inference: per-token rates for every catalog model across the realtime, flex, and batch windows, cached input, and what counts as a token.
Pricing
sference bills per token. There is no platform fee, no seat cost, and no minimum spend on serverless: you pay for prompt tokens and completion tokens. Dedicated capacity is billed per GPU-hour instead — see Deployments.
All rates are USD per 1M tokens. The site shows the same numbers with a EUR toggle.
Three windows, three prices
Every request runs on one of three scheduling windows. The window decides how long the request may wait behind other work, and therefore the rate.
| Window | How you select it | Wait budget | Rate |
|---|---|---|---|
| realtime | default; sync /v1/chat/completions, /v1/messages, or blocking /v1/responses | none — served as soon as capacity allows | list |
| flex | service_tier: "flex" on /v1/chat/completions or /v1/responses | up to 30 minutes, best effort | 0.7 × list |
| batch | background: true, batches, or streams (completion_window: "24h") | up to 24 hours | 0.5 × list |
Realtime, flex, and batch are scheduling priorities on the same fleet, not separate deployments. Interactive traffic never queues behind backfill, which is what makes the discounted windows possible without degrading realtime.
Flex is per-account
service_tier: "flex" must be enabled on your account. Ask your account team to turn it on. A flex request that cannot complete inside its window fails with 408, and the OpenAI SDKs retry it automatically.
Flex and batch are independent dimensions and they stack: a batch request submitted with service_tier: "flex" bills at 0.7 × 0.5 = 0.35 × list.
Catalog rates
USD per 1M tokens. Cached input is the rate for prompt tokens served from the prefix cache — see Prompt caching.
| Model | Window | Input | Cached input | Output |
|---|---|---|---|---|
moonshotai/Kimi-K3 | realtime | 3.00 | 0.45 | 15.00 |
| flex | 2.10 | 0.315 | 10.50 | |
| batch | 1.50 | 0.225 | 7.50 | |
zai-org/GLM-5.3 | realtime | 1.20 | 0.26 | 4.20 |
| flex | 0.84 | 0.182 | 2.94 | |
| batch | 0.60 | 0.13 | 2.10 | |
zai-org/GLM-5.2 | realtime | 1.20 | 0.26 | 4.20 |
| flex | 0.84 | 0.182 | 2.94 | |
| batch | 0.60 | 0.13 | 2.10 | |
deepseek-ai/DeepSeek-V4.1-Flash | realtime | 0.50 | 0.05 | 1.50 |
| flex | 0.35 | 0.035 | 1.05 | |
| batch | 0.25 | 0.025 | 0.75 | |
deepseek-ai/DeepSeek-V4-Flash-0731 | realtime | 0.28 | 0.07 | 0.56 |
| flex | 0.196 | 0.049 | 0.392 | |
| batch | 0.14 | 0.035 | 0.28 | |
deepseek-ai/DeepSeek-V4-Flash | realtime | 0.28 | 0.07 | 0.56 |
| flex | 0.196 | 0.049 | 0.392 | |
| batch | 0.14 | 0.035 | 0.28 | |
Cloudflare/clef | realtime | 0.24 | — | 0.00 |
| flex | 0.168 | — | 0.00 | |
| batch | 0.12 | — | 0.00 | |
zai-org/GLM-5.3-Flash | realtime | 0.20 | 0.07 | 0.60 |
| flex | 0.14 | 0.049 | 0.42 | |
| batch | 0.10 | 0.035 | 0.30 |
Cloudflare/clef has no output charge — it is a decisions model and bills on input only.
This table is a snapshot
GET https://api.sference.com/v1/models is authoritative and reflects your entitlements, including custom models. Rates for models agreed in your contract are per contract. Read the API at runtime rather than hardcoding this table.
What counts as a token
- Input — every token in the prompt, including system messages, tool definitions, and images converted to tokens. Tokens served from the prefix cache bill at the cached input rate instead.
- Output — every generated token, including reasoning tokens. A model that thinks for 2,000 tokens before answering 100 bills 2,100 output tokens. This is why
max_output_tokensmatters on reasoning models: the cap covers the thinking too. - No cache-write charge. Unlike some providers, sference does not bill a premium for writing into the prefix cache, and no TTL choice changes the price. Cached tokens are simply cheaper.
- Rejected requests are not billed. A
max_queue_wait_exceededrejection, a408on flex, or an upstream failure before any output costs nothing. A request that produced output and then failed bills for the tokens it produced.
Checking what you spent
| Surface | What it shows |
|---|---|
GET /control/v1/billing/summary | Current period spend, balance, and burn rate |
GET /control/v1/usage/summary | Token and cost totals, optionally grouped by model, key, or member |
GET /control/v1/activity | Per-request rows: model, tokens, cost, latency, outcome |
See Usage & analytics for the full endpoint list, CSV exports, and attribution.
Dedicated and enterprise
- Dedicated deployments — reserved GPU capacity with private endpoints, billed per GPU-hour on monthly commitments. Predictable latency and no per-token metering. See Deployments.
- Custom models — fine-tunes and distilled models are hosted under an enterprise agreement; pricing is per contract. See Custom models.
- Enterprise — annual agreements, committed-spend discounts, volume pricing, and custom SLAs. Contact
[email protected].
Why per-token beats self-managed
The per-token rate is only comparable to self-hosting if your GPUs are busy. Our scheduler keeps utilization high around the clock by packing realtime traffic and batch backfill onto the same fleet; on self-managed instances or an on-prem cluster, idle capacity is paid for whether it serves tokens or not. The rate you see is the cost of the tokens, not the cost of the reservation.