Prism ML Bonsai 2 9B
Prism ML's 9B vision model trained for ternary weights (-1, 0 or +1), about half the size of a standard 4-bit model at close to full-precision quality, with tool calling, thinking and a 256K context.
Bonsai 2 9B is an open-weight text model from Prism ML. It is multimodal: alongside a text prompt it accepts images, then replies with generated text. It runs entirely on your own hardware through Ollama: no API key and no per-use fees.
- Released
- Oct 2026
- Pricing
- Free, runs on your hardware
- Inputs
- Text, Images
Samples
Generated with Bonsai 2 9B via Ollama. The same three prompts run against every text model in the catalog, shown verbatim, so the only thing that changes between two models’ answers is the model.
Explain what an API is to someone who has never written code. Do not use a restaurant or waiter analogy. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 50 words.
An API lets separate software programs talk to each other. Instead of connecting directly, one requests specific data or actions from another. This interface allows applications to collaborate efficiently without needing to know how the other works. Systems exchange information smoothly while keeping private details hidden.
A two-person startup is deciding whether to build their own user authentication or use a hosted provider. Give them a clear recommendation, then the single strongest argument against your own recommendation. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 150 words.
I recommend using a hosted provider because implementing authentication from scratch creates unacceptable security risks for small teams, particularly regarding GDPR and breach liability. The development effort requires dedicated security engineering resources that two engineers cannot commit to at launch. You need immediate scalability without hiring extra staff for password resets and token management tasks. The strongest argument against this choice is vendor lock-in. Once you integrate their API, switching providers later becomes technically complex and expensive, forcing you into a long-term dependency on their pricing structure. If they raise rates or discontinue services, your user base has no easy path to migrate without recreating the system you avoided building initially.
Write a briefing for a non-technical executive explaining why their company's spending on AI is hard to predict, and what they can do about it. Cover at least three distinct causes. Plain prose only — no headings, bullet points, or markdown formatting. Write exactly 205 words.
Predicting artificial intelligence expenditures remains exceptionally difficult for many organizations facing constant market volatility. Three primary factors contribute significantly to this budgetary uncertainty. First, technological landscapes evolve rapidly, meaning today’s standard capabilities might become obsolete or exponentially more valuable within months. This unpredictability creates significant challenges for long-term financial planning regarding infrastructure and licensing fees. Second, the scarcity of qualified skilled professionals drives labor costs up unpredictably. Hiring top talent often requires aggressive compensation packages that fluctuate based on industry demand rather than fixed contracts. Third, many companies struggle to identify specific return on investment scenarios early on. Without clear business outcomes, initial projects frequently expand in scope or require additional resources during implementation. Executives can mitigate these risks by shifting budgets toward modular pilot programs instead of massive upfront commitments. This approach allows teams to test specific use cases without heavy long-term liability. Additionally, establishing flexible resource pools enables quicker pivots when models change performance metrics. Finally, maintaining open lines of communication with technical partners ensures early warning systems regarding emerging risks or new opportunities that justify additional spending elsewhere. By focusing on adaptable strategies rather than rigid forecasts, leadership can better navigate the evolving landscape of intelligent automation without jeopardizing core operational stability.
Prism ML
Prism ML builds Bonsai, open vision models trained for ternary weights that run at about half the size of 4-bit models.
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