Research · Head-to-head
Inkling-Small vs Fugu-Cyber
Inkling-Small (free, AI Score 8.8/10) vs Fugu-Cyber (paid, AI Score 8.2/10). Side-by-side pricing, features, pros and cons, and which to pick.
The verdict
Pick Inkling-Small if…
- →you need a genuinely free option
- →budget is the constraint
- →overall capability matters more than price (AI Score 8.8 vs 8.2)
- →you want our editor's pick for this category
Pick Fugu-Cyber if…
- →your primary use case is security engineers, red teams, and threat researchers who need an api model specialized for vulnerability discovery and threat-intelligence analysis.
- →you need: research, agents
Side-by-side specs
| Spec | Inkling-Small | Fugu-Cyber |
|---|---|---|
| Category | Research | Research |
| Pricing model | free | paid |
| Headline pricing | Free — open weights download; Tinker Playground access | Token plan only: $6–$12/M input, $36–$54/M output (application required, no free tier) |
| Free tier | Yes — the weights themselves are free to download and run. Compute is the real cost. | — |
| AI Score | 8.8/10 | 8.2/10 |
| Best for | — | Security engineers, red teams, and threat researchers who need an API model specialized for vulnerability discovery and threat-intelligence analysis. |
| Editor's pick | ✓ Yes | — |
| Use cases | — | research agents |
| Date added | 2026-07-31 | 2026-07-21 |
Pros and cons
Inkling-Small
Research · free
Pros
- ✓Open weights on Hugging Face, so it can run fully offline for sensitive audio, documents, or code
- ✓12B active parameters keeps per-token inference cost near a mid-size dense model despite the 276B total
- ✓Audio and vision are native to the model rather than a separate encoder stage
- ✓1M-token context handles whole codebases or long document sets in one pass
- ✓Hosted Tinker Playground path for teams that want to evaluate before committing hardware
Cons
- ×"Free weights" is misleading on cost — serving 276B parameters requires enough VRAM to put local deployment out of reach for individuals and small teams
- ×The claim that it outperforms the larger Inkling comes from the lab's own launch benchmarks; independent third-party evaluations aren't in yet
- ×Released July 30, 2026, so tooling, quantizations, and community fine-tunes are still thin compared to established open-weights families
- ×It's a raw model, not a product — no chat app, no agent harness, no support contract unless you build or buy one
Fugu-Cyber
Research · paid
Pros
- ✓Specialized for cybersecurity rather than a general model used off-label
- ✓Claimed SOTA on real-world benchmarks (CyberGym, CTI-REALM), not just synthetic tests
- ✓Orchestration design targets multi-step agentic security workflows
- ✓Application-gated rollout is a reasonable posture for offensive-security capability
Cons
- ×Access is gated behind application approval with no free tier or public playground
- ×Performance claims come almost entirely from Sakana's own release — little independent verification yet
- ×Output pricing is steep and scales higher for large-context requests
- ×Narrow specialization: not useful outside security work
Related comparisons
Updated 2026-07-31. Spec data sourced from official product pages and tracked in our public directory at /tools.