The New Baseline: Multimodal by Default
“Multimodal” stops being a feature badge in 2026 and becomes the norm. OpenAI’s Sora 2 pushes toward more physically accurate video with synchronized dialog and sound—paired with storyboard controls that bring shot-planning into the model loop. Google’s Veo 3.x arrives in Gemini/Flow with finer object edits, camera moves, and audio, making short-form and ad creative less brittle and more editable. Expect studios and marketers to standardize on text+image+audio pipelines, not just text-to-video tricks. OpenAI+1

From Chatbots to Computer-Using Agents
A visible shift in 2026: top models don’t just “answer,” they act. OpenAI’s o-series (o3, o4-mini) formalizes longer-thinking reasoning with integrated tool use—web browsing, Python, file/image analysis, and more—so one model can chain steps and decide when to think vs. act. OpenAI’s Operator work shows the same direction for the browser: an agent that types, clicks, and navigates sites to complete tasks. Anthropic’s Claude line mirrors this with “computer use,” letting agents operate GUIs for procurement, onboarding, and research. Together, this sets the tone for product roadmaps: fewer narrow bots, more general agents with policy guardrails and observability. Anthropic+3OpenAI+3OpenAI+3

Long Context Becomes a Capability You Rely On
A year ago, “long context” was a demo. In 2026, it’s table stakes. OpenAI’s GPT-4.1 series introduced 1M-token inputs for agents that must read corpora or stitch long chains of steps. Google’s Gemini 2.5 ships with a 1M context window (and has signaled 2M), pairing that with “Deep Think” for math/coding. Anthropic’s Claude Sonnet 4 extends to a 1M-token window across its API and cloud partners. Net effect: codebases, archives, transcripts, and multi-hour videos become “one prompt” inputs instead of brittle chunking puzzles. Reuters+2blog.google+2

Roadmaps: What Changes in 2026
• Agent Workflows Everywhere. Expect first-party SDKs that orchestrate multi-step, multi-tool agents (responses APIs, built-in web/file search, computer use) to land in mainstream developer stacks. Enterprise rollouts will emphasize tracing, rate governance, and red-teaming baked into agent workflows.

• Video Gen Matures. Model releases focus less on “wow” clips and more on control: reference characters/styles, deterministic edits, and edit-friendly layers across Veo/Sora ecosystems. Studios will pilot “AI animatics → human polish” pipelines for cost and speed.

• On-Device Intelligence Rises. Apple’s on-device foundation model—now developer-accessible through its Foundation Models framework—pushes private, offline features into everyday apps. Expect companion SLMs (Microsoft’s Phi-4 multimodal) to proliferate in kiosks, wearables, and vehicles where latency, privacy, and power budgets matter.

• Open Models Double Down on Multimodal. Meta’s Llama 4 arrives as a multimodal, open release; Mistral’s Pixtral Large spreads via managed services (Bedrock, Snowflake), giving enterprises more options to keep IP control and tune costs.

Important Facts Shaping 2026 Plans

1. Agents need permissions and proof. Safety docs now spell out how reasoning models weigh policy in context and why “computer use” ships behind layered mitigations. Expect procurement to ask for operator system cards, audit trails, and kill-switches right alongside accuracy charts.

2. Context scale changes product design. With million-token windows common, teams can pass entire data rooms (or codebases) to a single run—reducing bespoke retrieval glue and lowering failure modes introduced by aggressive chunking. Roadmaps in BI, search, and dev-tools increasingly assume long-context availability.

3. Multimodal is not just video. The biggest daily gains come from text+vision+audio together: call-center QA on voice+screens, chart/table understanding, and screen-aware agents that read UIs. That’s why Pixtral-class models and o-/Gemini reasoning updates stress documents, charts, and GUIs as first-class citizens.

4. On-device becomes a strategic hedge. Apple Intelligence and small, efficient Phi-family models offer private inference for everyday tasks; they also blunt cloud latency/egress costs for simple automations—nudging hybrid designs where heavy training runs in cloud, but lots of assistive UX runs locally.

Who’s Likely to Lead Where
• Reasoning/agents: OpenAI (o-series, Operator) and Google (Gemini 2.5 + Deep Think, Astra/Mariner) are setting the pace on agentic tooling and “computer use.” Anthropic’s Claude keeps pushing reliability for enterprise workflows, especially browser automation.

• Multimodal media: OpenAI (Sora 2) and Google (Veo 3.x) focus on control and editability; Runway keeps pushing creator-centric features that plug into production.

• Open ecosystem: Meta’s Llama 4 and Mistral’s Pixtral Large broaden high-end, multimodal options under permissive licenses or managed services. 

• On-device/private: Apple’s on-device foundation model plus Microsoft’s Phi-4 multimodal lead the “small, capable, private” wave for apps and edge devices.

What to Do with This (If You’re Shipping in 2026)

• Design for agent handoffs: let a single model browse, run code, and operate UIs—with observable steps and human-in-the-loop gates.

• Treat long-context as a platform feature: collapse brittle retrieval glue where a single run suffices; reserve RAG for truly dynamic or huge corpora.

• Make multimodal I/O first-class: products that mix speech, screenshots, tables, and short clips will simply feel smarter.

• Plan a hybrid runtime: high-end cloud for training/bursty work; on-device or edge for private, frequent assistive tasks.

4. Closing Thoughts
   Generative AI’s center of gravity is shifting from “talking search box” to tool-using, multimodal, long-context agents. 2026 is the year those capabilities harden into product defaults. The roadmap is less about single-number benchmarks and more about controllability, safety proofs, and fit-for-purpose runtimes—from Sora-class editing controls to million-token reasoning to on-device assistants. Teams that build for this blend—cloud + edge, text + vision + audio, answers + actions—will feel the compounding gains first.

References

• OpenAI — “Introducing o3 and o4-mini.” https://openai.com/index/introducing-o3-and-o4-mini/

• Google DeepMind (The Keyword) — “Gemini 2.5: Our newest Gemini model with thinking.” https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025/

• Anthropic — “Claude Sonnet 4 now supports 1 million tokens of context.” https://www.anthropic.com/news/1m-context

• Reuters — “Meta releases new AI model Llama 4.” https://www.reuters.com/technology/meta-releases-new-ai-model-llama-4-2025-04-05/

• Apple Newsroom — “Apple’s Foundation Models framework unlocks new intelligent app experiences.” https://www.apple.com/newsroom/2025/09/apples-foundation-models-framework-unlocks-new-intelligent-app-experiences/

Authors
Serge Boudreaux — AI Hardware Technologies
Montreal, Quebec

Peter Jonathan Wilcheck — Co-Editor
Miami, Florida