Research · Head-to-head

Inkling-Small vs Undermind

Inkling-Small (free, AI Score 8.5/10) vs Undermind (freemium, AI Score 7.8/10). 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 7.8)
→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 Undermind if…

→your primary use case is researchers, r&d scientists and phd students sweeping a narrow technical literature where missing one key paper means a wasted experiment, not a slightly weaker intro section.
→you need: education

Try Undermind →

Spec	Inkling-Small	Undermind
Category	Research	Research
Pricing model	free	freemium
Headline pricing	Free open weights; Tinker hosted access — check website for current pricing	Free tier + Pro from ~$20/mo (verify current tiers on site)
Free tier	Yes — the weights themselves are free to download and run. Compute is the real cost.	Free tier includes a small monthly allowance of deep searches (roughly 3 as last verified) — enough to judge whether the agentic approach fits your workflow before paying.
AI Score	8.5/10	7.8/10
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.	Researchers, R&D scientists and PhD students sweeping a narrow technical literature where missing one key paper means a wasted experiment, not a slightly weaker intro section.
Editor's pick	✓ Yes	—
Use cases	research development	research education
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

✓Multi-round agentic search surfaces papers that keyword ranking buries, with a vendor-published recall benchmark behind the claim
✓Returns every paper it read with a relevance rating, so the search is auditable and not just the summary
✓Reports a coverage confidence estimate — an unusual honesty signal no major rival matches
✓Free tier is genuinely enough to test the workflow before committing

×Minutes per search and tight monthly quotas make it a considered-use tool, not a daily driver
×Does not disclose which underlying models power the agent loop, so 'more advanced models' on paid tiers is unverifiable
×Narrow by design: strong on technical scientific questions, near-useless for general or non-academic research
×Deep-research modes now bundled free into ChatGPT, Claude and Gemini cover the easier majority of literature sweeps at no extra cost

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