Hyperresearch vs OpenLLM
Hyperresearch and OpenLLM are both AI assistants tools. Hyperresearch is aimed at developers and enterprise, while OpenLLM is aimed at developers and freelancers. Both have a free plan.
Deep research reports with every citation checked against source
Routes coding agents through your AI plans, API keys and local models
| Pricing | Open source | Freemium · $30 |
| Starting price | Not published | $30/mo |
| Free plan | Yes | Yes |
| Free trial | — | 3 days |
| Built for | Developers, Enterprise, Small businesses | Developers, Freelancers |
| Platforms | web | macos, linux |
| Country | — | — |
| Rating | — | — |
Choose Hyperresearch if
Analysts, researchers and agent builders who need long reports or short answers where every citation can be verified.
- Every cited claim is checked against its source text
- Pay per run with no monthly subscription
- Open-source CLI for teams that want to self-run the pipeline
Look elsewhere if: People who want instant conversational answers or a general AI agent workspace (Perplexity, Genspark).
Choose OpenLLM if
Individual developers who pay for several AI plans and want every coding agent to use them through one endpoint with automatic fallback.
- Free tier is generous at 50M non-cached tokens a month
- Existing config such as ~/.claude is left unmodified
- Keeps agents running through rate limits with fallback chains
Look elsewhere if: Teams that need a self-hosted, governed gateway or pay-per-token access to many models without accounts (LiteLLM, OpenRouter).
Hyperresearch vs OpenLLM pricing
Hyperresearch
- Open-source CLI$0MIT license, runs inside your own Claude Code or Codex
- Light run$9About 30-40 minutes, 2,500-4,000 word report
- Deep run$493-5 hours, 150-200 sources; first run free with a card
- Answers MCP/APIFrom $0.05Per call; $5 one-time free credit, no card
- EnterpriseCustomContact sales
OpenLLM
- Free$050M non-cached tokens/mo included, 2 devices, no card
- Pro$30/moUnlimited tokens, 5 devices, hosted gateway; 3-day trial
Hyperresearch · Key features
- Deep research runs across roughly 150-200 sources with cited reports
- Per-citation verdicts, quote checks and retraction sweeps
- Searchable source vault with semantic search and tar.gz export
- Direct calls to 40+ data APIs such as SEC EDGAR, BLS and PubMed
- Answers MCP with ask and search tools for AI agents
- REST API with scoped keys, async runs and webhooks
Cons
- Deep runs take 3 to 5 hours to finish
- $49 per Deep run adds up for frequent research
- Benchmark results are run and reported by the vendor
OpenLLM · Key features
- One OpenAI- and Anthropic-compatible endpoint and key for every connected model
- Use Claude, ChatGPT, Kimi Code and Grok subscriptions from other agents
- Fallback model chains when a model is rate-limited or unavailable
- Auto-detection of local Ollama and LM Studio model servers
- Shared MCP memory across Claude Code, Codex, Grok Build and OpenCode
- Browser-side AES-256-GCM key vault with a user-held recovery phrase
Cons
- No Windows support for the local daemon
- Single-user today, with no team features
- Losing the recovery phrase means re-entering every key
Hyperresearch vs OpenLLM: FAQ
Do Hyperresearch and OpenLLM have a free plan?
Yes, both Hyperresearch and OpenLLM have a free plan; paid plans add more usage and features.
Which is better for small businesses, Hyperresearch or OpenLLM?
Hyperresearch lists small businesses as a core audience, while OpenLLM is aimed more at developers and freelancers.