Deep Reinforce Ornith 1.5 122B
Deep Reinforce's 122B MoE self-improving model (MIT) with 256K context, image input, and native tool use, the largest Ornith 1.5 size that runs on a single high-memory machine.
Ornith 1.5 122B is an open-weight text model built by Deep Reinforce. 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 Ornith 1.5 122B 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 is a set of rules that lets one software program talk to another. It acts like a messenger: you request something through it, and it fetches the data or performs the action behind the scenes, returning the result without you needing to understand how it works internally.
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.
Use a hosted provider like Auth0, Clerk, or Firebase Auth. As a two-person team, your scarce resource is focus, and authentication is a solved problem you do not need to solve. Building secure auth means correctly handling password hashing, session management, token rotation, MFA, recovery flows, and evolving compliance requirements—work that distracts from the actual product and often gets done poorly under time pressure. A hosted service ships working login today, scales with you, and carries the security burden on the vendor. The strongest argument against this is cost and lock-in at scale. Hosted providers charge per active user, so once you have tens of thousands of engaged users your recurring fees can dwarf what an in-house system would cost, and migrating away later is painful because your entire product is woven around their APIs and data model.
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.
Your company's artificial intelligence spending resists neat prediction for several distinct reasons. First, artificial intelligence systems consume computing power on demand, meaning costs rise and fall with usage in ways that are nearly impossible to forecast months ahead. A single popular new feature can trigger steep, exponential increases in consumption, and the relationship between what you pay and what you use stays opaque. Second, the technology itself moves faster than any budget cycle. The tools you evaluate today may already be obsolete or dramatically cheaper by next quarter, and vendor pricing shifts constantly as competition intensifies. Third, hidden labor and integration costs repeatedly surprise finance teams. Beyond raw computing, you pay for skilled specialists whose salaries are high, for data preparation, for security and compliance review, and for reworking existing systems to accommodate new capabilities. These expenses surface gradually and often only after commitments are made. To regain control, treat your artificial intelligence spending as an ongoing experiment rather than a fixed line item. Establish clear usage monitoring from the very start, set flexible spending bands instead of rigid caps, and review spending projections quarterly. Negotiate pricing arrangements tied closely to measurable outputs, and build a modest reserve so unexpected surges never derail other strategic priorities.
Deep Reinforce
Deep Reinforce builds the Ornith family — self-improving open-weight (MIT) models for agentic coding, tuned with reinforcement learning on coding and terminal benchmarks.
deep-reinforce.com ↗Deep Reinforce's 9B self-improving agentic-coding model (MIT) with 256K context and native tool use.
Deep Reinforce's flagship 35B self-improving agentic-coding model (MIT) with 256K context — state-of-the-art open-source coding for its size.
Deep Reinforce's 9B self-improving model (MIT) with 256K context, image input, and native tool use, tuned for reasoning, coding, and agentic work.
Deep Reinforce's 35B MoE self-improving model (MIT) with 256K context, image input, and native tool use, strong across reasoning, coding, and agentic tasks.