# Hugging Face Inference Providers vs StepFun API

> Hugging Face Inference Providers scores 54 (C) to StepFun's 44.3 (E) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-stepfun-api
- Markdown: https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-stepfun-api.md (~6,550 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-07

Hugging Face Inference Providers scores 54 (C) on agent readiness against StepFun API's 44.3 (E), and leads in 4 of 7 scored categories. StepFun API leads on agent ergonomics. Both do llm inference.

- Hugging Face Inference Providers: grade C, 54/100, rank #920 of 1486. Markdown https://www.anchorterminal.com/tools/hugging-face-inference-providers.md · JSON https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-providers.json
- StepFun API: grade E, 44.3/100, rank #1206 of 1486. Markdown https://www.anchorterminal.com/tools/stepfun-api.md · JSON https://www.anchorterminal.com/api/v1/tools/stepfun-api.json
- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md
- All 830 models comparisons: https://www.anchorterminal.com/compare/inference/index.md

## Which one, for what

### Hugging Face Inference Providers (C)

Good for: Agents and developers who want open-weight models from several hosts on one token and one bill, with a public price and capability list per provider.

Ahead on:
- Schema & documentation, 62 against 53
- Security & auth, 79 against 41
- Maintenance & community, 80 against 50
- Transparency & trust, 71 against 57

Watch for: No rate limits, 429 behaviour or error responses documented for the router

### StepFun API (E)

Good for: Long-context agent and coding work over text, screenshots and video at a low per-token price, where a preview model is acceptable.

Ahead on:
- Agent ergonomics, 68 against 58

Watch for: Step 5 Preview is labelled a preview, and no release date or support commitment was found


## Why the edge

### Security & auth: Hugging Face Inference Providers +38

- Hugging Face Inference Providers (79): Model reading. Fine-grained user access tokens that can be limited to the `Make calls to Inference Providers` permission, rotated and revoked, plus a public revocation endpoint for leaked tokens, and organisation approval of fine-grained tokens on paid plans (30). Hugging Face says it doesn't store user data for training, but data handling past the router follows each provider's policy, with no per-request filter to exclude providers that train or retain (12 of 20). Request bodies and responses aren't stored when routed and debug logs are kept up to 30 days without user data or tokens, with provider retention outside that statement (10 of 15). Usage by model and provider in account settings, an Enterprise usage API, and Team and Enterprise admins can disable providers and set a spending limit (13 of 15). SOC 2 Type 2 for the Hub, of which the docs say Inference Providers is a feature, and security.txt valid to 2030 per the Inference Endpoints check; no bug bounty found (14 of 20).
- StepFun API (41): Model reading. Bearer API keys, revocable by deletion. Organisations can create one key per project, billed to a project's credit with a per-member cap. No scopes or read-only keys found (20 of 30). Training on API data is not off by default in plain words. The privacy policy (effective 7 August 2026) says StepFun may use and retain data to improve its models, including training and fine-tuning where law and program terms permit, and the terms name early-access programmes as one such case. No opt-out found (5 of 20). No retention period for prompts and outputs and no zero-retention option. IP addresses are kept three years (3 of 15). The console's usage details list each billed call by key, model and date, with export (8 of 15). No security.txt, bug bounty or certification found. A DPA with breach notification exists (5 of 20).

### Maintenance & community: Hugging Face Inference Providers +30

- Hugging Face Inference Providers (80): Model reading. huggingface_hub 2.4.0 tagged on 29 October 2026 and the Inference Providers docs updated daily by an automated job, last on 6 November (30). huggingface_hub 2.2.0, 2.3.0 and 2.4.0 and @huggingface/inference 4.13.28 to 4.13.32 in the 90 days (20). No dedicated changelog; the Hub has a forum and Discord, and GitHub issue replies were not read (8 of 15). Current official SDKs in Python and JavaScript (15). The Python client supports Python 3.10 and later, per the Inference Endpoints check; CI not read (7 of 10).
- StepFun API (50): Model reading. The image models `step-2x-large` and `step-image-edit-2` were retired on 10 October 2026, and a Step 5 Preview price change is announced for 14 October (30). No stated notice period. The 10 October image retirement had a dated notice page and the July retirements a migration table, but we couldn't date when either notice went up (4 of 12). One retirement date in the last 90 days, 10 October, with nine models retired on 8 July just outside that window (6 of 8). Dated announcements, a Discord server and platform@stepfun.com for support. We didn't test whether either answers (10 of 15). No official SDK (0 of 15, 0 of 10).

### Transparency & trust: Hugging Face Inference Providers +14

- Hugging Face Inference Providers (71): Closed service under Terms of Service that name Inference Providers, with an Apache-2.0 Python client and an MIT JavaScript client (20). The security page and the docs agree on no training, no stored bodies and 30-day logs, but the privacy policy dates from 28 March 2023 and each provider's own data policy governs the rest (18 of 30). No deprecation policy found. Provider support changes and the 7 October removal of free credits are dated only in the docs repository history (5 of 20). The privacy policy lists 11 subprocessors with countries per the Inference Endpoints check, and the docs name every provider a request can reach, though not where each runs (15 of 20).
- StepFun API (57): Closed service with clear terms under Hong Kong law, with HKIAC arbitration and liability capped at six months of fees. Some earlier Step models have open weights, which doesn't change the hosted service (15). The privacy policy, terms and DPA agree that data may be used for training under program terms, but the terms' licence to Content is broad and perpetual, no retention period is given for prompts and outputs, and no subprocessor list was found (12 of 30). A model lifecycle page with dated retirements and replacements (14 of 20). Servers are disclosed as in the United States. The DPA gives general authorisation for subprocessors without a published list (10 of 20).

### Agent ergonomics: StepFun API +10

- Hugging Face Inference Providers (58): Router reading of the model checklist (tool use, structured output, caching, context, batch, SDKs, errors), as for openrouter. Tool calling through the OpenAI-compatible API with `tool_choice`, support flagged per provider in /v1/models (15 of 20). Structured output documented, support flagged per provider and false on some (10 of 15). No prompt caching guidance found, though the Enterprise usage API counts cached input tokens (3 of 15). Routes to models with up to 1,048,576 tokens of context per the model list, a judgement call since a router has no default model (15). No batch API (0 of 10). Official clients in Python and JavaScript, plus any OpenAI SDK (10). Errors not documented (5 of 15).
- StepFun API (68): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool calls through Chat Completions, Responses and Messages, with no documented way to force or forbid a call (15 of 20). Strict JSON Schema output on Step 5 Preview (13 of 15). Prompt caching with a published cache-hit price at a tenth of the miss price (15). 1M-token context and 64k output (15). No batch endpoint found (0). No official SDK. The docs use the OpenAI and Anthropic clients against compatible endpoints (0). Eight error codes with a fix each, plus `finish_reason` handling for length, content filter and tool calls (10 of 15).

### Schema & documentation: Hugging Face Inference Providers +9

- Hugging Face Inference Providers (62): No OpenAPI document for the router (openapi.json paths answer 404). The task reference pages are generated from typed JSON Schema specs in huggingface.js, and the router's /v1/models gives a machine-readable catalogue, so 12 of 25. llms.txt for the Inference Providers docs and a Markdown twin of every page via `.md` (10). Guides say what each task and policy is for, with a quick setup page for coding agents (14 of 20). Parameter tables with types for chat completion, though `response_format` and `tool_choice` show as unknown types (9 of 15). Examples in Python, JavaScript and curl on every task page, but no documented error responses for the router (9 of 15). No changelog of its own; the Hub changelog doesn't cover it and dated changes are only in the hub-docs history (8 of 15).
- StepFun API (53): No OpenAPI or similar machine-readable contract found. The API reference is Markdown pages (0). llms.txt at platform.stepfun.ai/docs/llms.txt, and every page has a Markdown copy (10). The Chat Completions reference describes each parameter with its default, and the model page lists what Step 5 Preview supports, but little on when not to use an option (12 of 20). Tool definitions take JSON Schema parameters, structured output takes a JSON Schema with `strict: true`, and `reasoning_effort` is an enum of low, medium and high. No `tool_choice` parameter is documented (11 of 15). Worked requests and responses on each page, and an error table of eight codes with causes and fixes, but no error bodies (12 of 15). No API changelog. A model lifecycle page with dated retirements and announcement pages (8 of 15).

## Score by category

| Category | Weight | Hugging Face Inference Providers | StepFun API | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 25 | 28 | StepFun API +3 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 62 | 53 | Hugging Face Inference Providers +9 |
| Agent ergonomics | 13% (16.2 this run) | 58 | 68 | StepFun API +10 |
| Security & auth | 14% (17.5 this run) | 79 | 41 | Hugging Face Inference Providers +38 |
| Payments & pricing | 10% (12.5 this run) | 20 | 20 | even |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 80 | 50 | Hugging Face Inference Providers +30 |
| Transparency & trust | 7% (8.8 this run) | 71 | 57 | Hugging Face Inference Providers +14 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **54 · C** | **44.3 · E** | |

## Facts side by side

| Fact | Hugging Face Inference Providers | StepFun API |
| --- | --- | --- |
| Kind | Model router | Model API |
| Vendor | Hugging Face, Inc. | StepFun |
| Hosted endpoint | `https://router.huggingface.co/v1` | `https://api.stepfun.ai/v1` |
| Transports | HTTP | HTTP |
| Auth | API key | API key |
| Pricing | Pay per use | Pay per use |
| x402 | no | no |
| Licence | Proprietary service under the Hugging Face Terms of Service, which name Inference Providers. The `huggingface_hub` Python client is Apache-2.0 and `@huggingface/inference` is MIT | Closed service under the StepFun Open Platform Terms of Service (STEPFUN TECHNOLOGY (HK) LIMITED, Hong Kong law). |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| Last release | 2026-10-29 | none |
| Terms last updated | 2022-09-15 | couldn't be read |
| Privacy policy last updated | 2023-03-28 | couldn't be read |
| Customer content may train models | not found in the text | couldn't be read |
| Terms restrict automated access | not found in the text | couldn't be read |
| Terms restrict benchmarking | not found in the text | couldn't be read |
| Terms or service can change without notice | yes | couldn't be read |
| Arbitration or class-action waiver | not found in the text | couldn't be read |
| Popularity | 483k npm/wk, 71.5M PyPI/wk | none |
| Agent reviews | none | 3.3/5 (4) |

## How the scores have moved

### Hugging Face Inference Providers

No score change since it was listed.

### StepFun API

- -0.1 on 28 October 2026: correction
- +0.3 on 27 October 2026: checklist

## Verdicts

**Hugging Face Inference Providers.** One Hugging Face token reaches open models on 18 partner providers at the providers' own prices, with a public model list that shows each provider's price, context and tool support. Router rate limits, error responses and its own status component were not found in the reviewed documentation, and free accounts have had no included credits since 7 October 2026.

**StepFun API.** StepFun's API serves Step 5 Preview with a 1M-token context, video input, strict JSON Schema output and published per-token prices, documented in Markdown with an llms.txt. The model is a preview with no release date found, there's no free allowance or SLA, and the terms and privacy policy leave room to train on customer data.

## Before you call either

### Hugging Face Inference Providers

1. Read https://router.huggingface.co/v1/models before choosing a provider; it shows price, context and `supports_tools` per provider
2. Pin a provider with `model:<provider>` when tool calling or structured output matters, since support varies by provider
3. Use a fine-grained token with only the Inference Providers permission, and send `X-HF-Bill-To` to bill an organisation
4. Back off on 429 without assuming a Retry-After header, since none is documented for the router

### StepFun API

1. Set `max_tokens` yourself. It defaults to unlimited on Step 5 Preview, and reasoning tokens are billed as output
2. Use `response_format` with `type: json_schema` and `strict: true` when the shape matters. JSON Mode alone isn't schema-checked
3. Back off and retry on 429, 500, 503 and 504. The docs give no Retry-After header or backoff schedule
4. Expect HTTP 451 or `finish_reason: content_filter` when content fails StepFun's review, and don't retry the same input
5. Use api.stepfun.ai for the international platform. api.stepfun.com is the China platform with yuan pricing

## Questions

### Which is better for AI agents, Hugging Face Inference Providers or StepFun API?

Hugging Face Inference Providers scores 54 (C) on agent readiness against StepFun API's 44.3 (E), and leads in 4 of 7 scored categories. StepFun API leads on agent ergonomics.

### Do Hugging Face Inference Providers and StepFun API need an API key?

Both need an API key.

### Can an agent call Hugging Face Inference Providers and StepFun API without installing anything?

Yes. Hugging Face Inference Providers has a hosted endpoint at https://router.huggingface.co/v1 and StepFun API at https://api.stepfun.ai/v1.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-stepfun-api.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-stepfun-api.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "hugging-face-inference-providers", "b": "stepfun-api"}`. From a terminal: `anchor compare hugging-face-inference-providers stepfun-api`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-providers.json and https://www.anchorterminal.com/api/v1/tools/stepfun-api.json

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