Models
How to choose, pin, and discover models on sference: open-weight families, BYOM, and the catalog API.
Models
Choosing a model
If you are experimenting, start with a small, fast open-weight checkpoint (for example GLM 5.3 Flash or DeepSeek V4 Flash in the catalog). If you are shipping to production, optimize for latency, cost per million tokens, and auditability. sference is built around pinned versions, so the model behind an id never changes underneath you.
Every text-generation model in the catalog runs in all three processing modes: realtime for user-facing traffic (where most usage is), flex for discounted latency-tolerant sync work, and async (24h window) for background pipelines: document extraction, synthetic data, embeddings backfills, and eval harnesses. Pick the smallest model that clears your quality bar, then scale horizontally. For classification and routing, a decisions model returns probabilities directly instead of generated text.
Always pin
Model ids in requests should include the exact catalog string you intend to run. Silent upgrades from “latest” are hostile to compliance reviews; we treat pinned versions as part of the audit trail.
Frontier vs efficient tiers
Think in three bands (names vary by catalog):
| Band | When to use |
|---|---|
| Frontier-class open weights | Hardest reasoning, codegen, or multimodal extraction where quality dominates unit cost. |
| Mid-tier | Production workloads where you still need headroom but not the largest context. |
| Efficient / small | High-volume async, classification, tagging, and preprocessing; optimize $/1M tokens first. |
Live catalog and serverless rates
The full catalog with exact ids and current serverless rates (USD per 1M tokens, realtime priority). Every model is addressed by its exact id string, and GET /v1/models returns the live list your key can reach, including per-model pricing and capabilities. The table below is generated from that endpoint, so it always matches the live catalog; the same catalog, search, and deployment-request routes live in API Reference under Model Catalog.
| Model | Context | Input | Output | Cached input | Vision |
|---|---|---|---|---|---|
moonshotai/Kimi-K3 | 1M | $3.00 | $15.00 | $0.45 | ✓ |
zai-org/GLM-5.3 | 1M | $1.20 | $4.20 | $0.26 | — |
deepseek-ai/DeepSeek-V4.1-Flash | 1M | $0.50 | $1.50 | $0.05 | ✓ |
zai-org/GLM-5.3-Flash | 1M | $0.20 | $0.60 | $0.07 | ✓ |
deepseek-ai/DeepSeek-V4-Flash-0731 | 1M | $0.28 | $0.56 | $0.07 | — |
Coming soon: DeepSeek V4 Pro, Gemma 4 31B, OpenAI GPT-OSS 120B, NVIDIA Nemotron 3 Super, and Qwen3.8. Rates are confirmed per model at account provisioning; the site shows USD with an EUR toggle.
Decisions models
Served on POST /v1/decisions (realtime only). It answers up to 16 choice, score, or yes/no questions about one input in a single call, generates no text, and is billed on input tokens only.
| Model | Context | Input | Output |
|---|---|---|---|
Cloudflare/clef | 64K | $0.24 | — |
Batch create validates each row’s model against the catalog and rejects batches that mix models. Use one model id per POST /v1/batches job.
Specialized paths
Batch inference
When you need JSONL-scale throughput and SLA windows instead of a single chat turn.
Responses & streams
Incremental output and event streams for long-running jobs.
Decisions
Realtime classification and routing: probabilities for choice, score, and yes/no questions in one call.
Catalog & deployment
Control plane routes for search, catalog, and requesting deployments.
Bring your own model (BYOM)
If it runs on vLLM or SGLang in your stack, we can usually serve it on sference, subject to onboarding and capacity. Start from the Model Catalog and request deployment flows in API Reference, or reach out at [email protected] if you are still evaluating fit.
Browse the full catalog
Use API Reference → Control Plane → Model Catalog for live schemas, or introspect from your integration tests. The marketing site also lists workload narratives under use cases.