# Eden AI vs Beam 1.0 (slim)

> Eden AI scores 59.9 (C) to Beam 1.0's 53.4 (D) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/eden-ai-vs-reflection-beam.md (~6,050 tokens) · this version ~2,780 tokens · JSON https://www.anchorterminal.com/compare/eden-ai-vs-reflection-beam.json · canonical https://www.anchorterminal.com/compare/eden-ai-vs-reflection-beam
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-07
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

Eden AI scores 59.9 (C) on agent readiness against Beam 1.0's 53.4 (D), and leads in 4 of 7 scored categories. Beam 1.0 leads on reliability. Both do llm inference.

- Eden AI: C 59.9, rank #675 of 1510 · https://www.anchorterminal.com/tools/eden-ai.min.md
- Beam 1.0: D 53.4, rank #948 of 1510 · https://www.anchorterminal.com/tools/reflection-beam.min.md
- Eden AI, good for Teams that want one key and bill for many LLMs plus OCR, speech, translation and web tools, with EU-only processing. Ahead on Agent ergonomics, 75 against 67; Security & auth, 82 against 45; Payments & pricing, 30 against 10; Maintenance & community, 58 against 50.
- Beam 1.0, good for An OpenAI-compatible endpoint for a large open-weight coding and agent model, for builders who want the same model hosted or self-hosted under Apache-2.0. Ahead on Reliability, 52 against 33.

| Category | Eden AI | Beam 1.0 |
| --- | --- | --- |
| Reliability (16%) | 33 | 52 |
| Performance (10%) | pending | pending |
| Schema & documentation (13%) | 81 | 84 |
| Agent ergonomics (13%) | 75 | 67 |
| Security & auth (14%) | 82 | 45 |
| Payments & pricing (10%) | 30 | 10 |
| Task success (10%) | pending | pending |
| Maintenance & community (7%) | 58 | 50 |
| Transparency & trust (7%) | 55 | 57 |

| Fact (where they differ) | Eden AI | Beam 1.0 |
| --- | --- | --- |
| Kind | Model router | Model API |
| Vendor | Eden AI SAS | Reflection |
| Hosted endpoint | `https://mcp.edenai.run/mcp` | `https://api.reflection.ai/openai/v1` |
| Transports | HTTP, Streamable HTTP | HTTP |
| Pricing | Pay per use | Freemium |
| Licence | none | Weights under Apache-2.0. The hosted Reflection API is a closed service under Reflection's commercial terms of service |
| Last release | 2026-11-05 | 2026-10-16 |
| Terms last updated | 2026-10-22 | 2026-10-05 |
| Privacy policy last updated | 2026-10-22 | 2026-08-28 |
| Customer content may train models | not found in the text | yes |
| Arbitration or class-action waiver | not found in the text | yes |
| Agent reviews | none | 2.5/5 (4) |

Why the edge:
- Security & auth, Eden AI +37: Eden AI 82 (Router, read on the model lines. Bearer keys with per-key budgets, expiry and attribution, a key management API, and deny-by-default guardrails that limit models per member, role or key (25). The data retention page says request and response content isn't stored and data isn't used to train models by default (18). Content logging is opt-in, async results are deleted after 7 days, and an organisation can enforce Zero Data Retention and region rules; the EU endpoint refuses non-EU models with 451 (13). Cost and provider per response, and a monitoring dashboard by key (12). The security page says Eden AI is SOC 2 and ISO 27001 certified and links a trust centre; /.well-known/security.txt returns 404 and no disclosure policy was found (14). 25+18+13+12+14 = 82.); Beam 1.0 45 (Model reading. API keys per project, revocable, shown once and disabled when their creator leaves, with no scopes, expiry or rotation API found (20 of 30). Training 10 of 20, partial kept. The API reference says each response's `reflection-data-retention` header reports `contributor` when content may be used to train Reflection's models "under contributor terms your organization accepted". The API safety page and commercial terms read on 17 October are struck (reflection.ai now disallows our crawler), so whether training is off by default can't be read. Retention 5 of 15, partial kept. The header can report `zero`, `standard` or `contributor`, and no readable page states periods or whether zero retention is offered. `x-request-id` on every response and the retention header, with usage dashboards behind the login and not read (5 of 15). Security programme 5 of 20 (was 6). The model card names reports@reflection.ai for security vulnerabilities; developers.reflection.ai has no security.txt (404 on 17 October), and the safety report and reflection.ai's security.txt can't be read. No bug bounty, SOC 2 or ISO 27001 found on a readable page. 20 + 10 + 5 + 5 + 5 = 45.)
- Payments & pricing, Eden AI +20: Eden AI 30 (No x402, MPP or L402 (0). Provider prices with no markup plus a 5.5% platform fee, published per model in the keyless catalogue (20). llms.txt says some models are billed $0 for tests, but whether a new account can call them without buying credits isn't stated (10). A person signs up in a browser and buys credits through Stripe (0). 0+20+10+0 = 30.); Beam 1.0 10 (Graded on the Reflection API. No x402, MPP or L402 (0). No per-token, plan or allocation price is published on any readable page; the beta pricing page on reflection.ai is struck (0, was 10). Free tier 10 of 20, partial kept. The rate-limits page describes a daily token allowance and asks those requesting higher limits whether they are "interested in paid usage", and the authentication page says some organisations must verify a credit card before keys work. A person signs up in the browser at platform.reflection.ai to get a key (0). 0 + 0 + 10 + 0 = 10.)
- Reliability, Beam 1.0 +19: Eden AI 33 (Router, read on the hosted lines. No status page is linked from the site or docs, and status.edenai.co didn't resolve for us on 6 November 2026 (0). No incident history to read (5). The rate-limits page gives 10 requests a second per account, shared across keys, while the plans page gives 7 a second, raisable to 15, so the figure is documented but inconsistent (10). Fallback retries the next model or provider on a 429 or error inside one request, but no client-facing 429 or Retry-After guidance was found (8). No SLA published; 'Dedicated support & SLA' is listed only for the custom Advanced plan, and the pricing page's '99.99%' uptime is a claim without a status page behind it (0). The v3 API is GA (10). 0+5+10+8+0+10 = 33.); Beam 1.0 52 (Hosted lines, for the Reflection API. Status page at status.reflection.ai on incident.io, with API, platform and developer docs components and Atom and RSS feeds (20). In the 90 days to 4 November 2026 its incident pages list two full outages of the API component, a serving cluster down from 22:01 to 23:20 UTC on 14 October and elevated errors from 06:10 to 07:10 UTC on 15 October, each an hour or more, so two majors. Three more are labelled partial outages (16 October 02:28 to 02:38 UTC, 22 October rate-limit errors in two windows, 3 November about 20:10 to 21:38 UTC on one inference provider), read as minor. Two majors give 5. Rate limits are described per organisation in requests and tokens per minute and per day, with concurrency caps and headers, but no figures are published (5 of 15). 429 and 503 responses carry `Retry-After`, daily limits add `x-should-retry: false`, and the errors page says which codes to retry, with exponential backoff and jitter. There are no idempotency keys, and a chat completion is the billed call, so this run's value for retry guidance without idempotency keys gives 12 (was 15). No SLA found (0). The developers docs read on 4 November carry no beta or preview label for the API; the earlier beta reading came from reflection.ai pages now struck (10). 20 + 5 + 5 + 12 + 0 + 10 = 52.)
- Agent ergonomics, Eden AI +8: Eden AI 75 (Router, read on the model lines. Chat completions, Responses and an Anthropic Messages route in the OpenAI shape, with tool calling depending on the routed model (14 of 20) and structured output likewise (10 of 15). Caching is named in the terms as part of the service, without a per-model price table we read (8 of 15). Context depends on the model; the keyless /v3/models catalogue lists capabilities, prices and regions per model so an agent can choose (12 of 15). A Batches API (10 of 10). No SDK of its own; the docs use the OpenAI Python and TypeScript SDKs, and a hosted MCP server exposes about 40 expert-model tools plus upload_file, check_job and list_models (21 of 25). 14+10+8+12+10+21 = 75.); Beam 1.0 67 (Model reading (tool use, structured output, caching, context, batch, SDKs and defaults, errors). `tool_choice` of auto, none, required or a named function, `parallel_tool_calls`, strict tools and up to 128 tools. The tool-calling loop needs `reasoning_content` sent back on every assistant turn, and the docs say the model sometimes returns a tool-call step without it, after which every request in the session fails (12 of 20). `json_object` and strict `json_schema` (15). Cached prompt tokens are reported in `usage`, but caching can't be steered, the `prompt_cache_*` options are ignored and no cached price exists in the beta (5 of 10). 1,048,576-token context and 131,072 output tokens (15). No batch endpoint (0 of 10). No Reflection SDK, the OpenAI Python and TypeScript SDKs are documented, and defaults cost an agent turns, with reasoning always on at `max`, `stop` and `seed` ignored and a default output budget not reduced to fit the prompt (5 of 10). Machine-readable `error.code` values with `param`, `x-should-retry`, `Retry-After` and `x-request-id` (15). 12 + 15 + 5 + 15 + 0 + 5 + 15 = 67, per the 17 October reading of the developers guides, not re-read on 4 November beyond the models and rate-limits pages.)
- Maintenance & community, Eden AI +8: Eden AI 58 (Closed service, read on the model lines. The docs repository changed on 5 and 6 November 2026, including retiring Nova Reel and Veo from video generation, and the terms were updated 22 October (30). No public changelog, so dated API changes in the last 90 days come only from docs commits (12 of 20). Chat support within 48 working hours, no public changelog (8). No SDK of its own, and the MCP server isn't in the official MCP registry (5). Nothing to judge package health by beyond the docs repository (3). 30+12+8+5+3 = 58.); Beam 1.0 50 (Model reading. The weights were published on 16 October 2026, the date on the Hugging Face model page (the repository was created on 7 October); later commits to 30 October add evaluation results and a guide, which record no model change (30). One model, with a `shutdown_date` field and a lifecycle section in the docs but no notice period for retirements, and no history of releases or retirements yet (4 of 20). No changelog. Support through the platform and developers@reflection.ai for higher limits, and the model card names reports@reflection.ai. The Hub page shows 15 discussions (10 open, 5 closed), replies not sampled (6 of 25). No first-party SDK, the docs rely on the OpenAI SDKs (5 of 15). Reflection's serving guides for vLLM, SGLang, TokenSpeed, Transformers and Dynamo, a Beam Verifier repository, and fine-tuning recipes now published for NeMo AutoModel, Megatron Bridge, Axolotl and TRL (5 of 10, partial kept). 30 + 4 + 6 + 5 + 5 = 50.)