Research · Head-to-head

Inkling-Small vs Paperguide

Inkling-Small (free, AI Score 8.5/10) vs Paperguide (freemium, AI Score 7.5/10). Side-by-side pricing, features, pros and cons, and which to pick.

The verdict

Pick Inkling-Small if…

→budget is the constraint
→overall capability matters more than price (AI Score 8.5 vs 7.5)
→you want our editor's pick for this category
→your primary use case is ml research teams and infrastructure-heavy engineering orgs that need audio-plus-vision reasoning over long documents running on weights they host themselves, because the data can't go to a hosted api.

Try Inkling-Small →

Pick Paperguide if…

→your primary use case is grad students and thesis writers running a systematic review who want search, extraction, references, and drafting under one subscription rather than stitching elicit, zotero, and a separate chat assistant together.
→you need: education, content-creation

Try Paperguide →

Spec	Inkling-Small	Paperguide
Category	Research	Research
Pricing model	free	freemium
Headline pricing	Free open weights; Tinker hosted access — check website for current pricing	Free tier + Plus ~$12/mo, Pro ~$24/mo (billed annually) — check website for current pricing
Free tier	Yes — the weights themselves are free to download and run. Compute is the real cost.	Free plan with limited monthly AI credits and around 20 searches — enough to test discovery and chat-with-PDF before paying. Prices above were last confirmed 2026-06-27; check the pricing page for current rates.
AI Score	8.5/10	7.5/10
Best for	ML research teams and infrastructure-heavy engineering orgs that need audio-plus-vision reasoning over long documents running on weights they host themselves, because the data can't go to a hosted API.	Grad students and thesis writers running a systematic review who want search, extraction, references, and drafting under one subscription rather than stitching Elicit, Zotero, and a separate chat assistant together.
Editor's pick	✓ Yes	—
Use cases	research development	research education content-creation
Date added	2026-07-31	2026-06-27

Research · free

✓Open weights on Hugging Face, so it runs fully offline for audio, documents, or code that can't leave your infrastructure
✓12B active of 276B total keeps per-token compute near a mid-size dense model
✓Audio and vision are native to the model rather than a separate encoder stage — rarer on the open shelf than text-plus-image
✓1M-token context handles whole codebases or long document sets in a single pass
✓Fine-tuning path through Tinker comes from the lab that trained the model, not a third-party reimplementation

×Every performance claim is still first-party — the "beats the larger Inkling" result comes from the launch post and no independent evaluation has published since
×"Free weights" understates cost badly: all 276B parameters must be VRAM-resident, which puts local deployment out of reach for individuals and small teams
×Two weeks old, so quantizations, serving-stack support and community fine-tunes are thin next to DeepSeek or Llama
×A raw model, not a product — no chat app, no agent harness, and no documented computer-use or video generation; agentic tool-use behaviour is untested in public

Research · freemium

✓Combines search, literature review, reference manager, and AI writing in one workspace instead of three separate subscriptions
✓Search corpus claimed at 200M+ papers, broad enough to cover most fields
✓Writer pulls citations from your own saved library rather than open search, which keeps references checkable against papers you actually have
✓40% student discount and a usable free tier lower the barrier for the academics who are its core audience
✓Review matrices turn a pile of PDFs into a comparable table, which is the single biggest time saver for systematic reviews

×Every component competes with a sharper specialist — Elicit for extraction, Scite for citation context, Consensus for evidence claims
×Deep-research modes now bundled into general AI assistants cover much of the synthesis work for people already paying for one, squeezing the mid-tier academic tools hardest
×Credit-based metering makes real monthly cost hard to predict, and the free tier's ~20 searches runs out within a single serious review
×The company doesn't publicly document which underlying models drive its review and writing features, so you can't reason about quality changes when they swap them

Updated 2026-08-10. Spec data sourced from official product pages and tracked in our public directory at /tools.