Experiential Labs vs Gemini 3.1 Pro: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Experiential Labs and Gemini 3.1 Pro — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Experiential Labs
Experiential Labs
Open-source AI gateway that routes every model through one endpoint at provider cost, then improves that traffic with caching, routing and fine-tuning.
Key features
- Unified Model Endpoint: One OpenAI-compatible POST endpoint fronts every hosted provider, your own bring-your-own keys and your own GPUs, so switching models is a parameter change rather than an integration.
- Zero-Markup Routed Tokens: Routed traffic bills at the provider's list price with 0% added on top, with the company earning on hosted inference and the Pro plan instead of on your tokens.
- Model Recommendation from Traffic: The intelligence layer watches real request patterns and tells you when switching models would win, including newly released models on the day they ship, with optional per-prompt optimization.
- Caching Opportunity Detection: Identifies where cache hit rate could improve and shows the projected savings, with repeated tokens returning at 90% off once enabled.
- Traffic-Trained Custom Models: Fine-tunes a model on your own traffic and proves it in closed-loop simulation before it ever serves, then exposes it through the same endpoint you already call.
- Spend Attribution Console: Breaks requests and dollars down by agent, person, model, provider and day across the whole organization, alongside catalog, usage and limits.
- Live Request Logs and Metrics: Streams per-request time-to-first-token, token counts, provider, status and cost, with dashboard rollups for requests, spend, p50 TTFT and cache hit rate.
- Governance Controls: Budgets, provider allowlists and attribution are available from the free tier upward for controlling who can spend what on which models.
Best for
- Consolidating Multi-Provider Access: Replace separate SDKs and keys for OpenAI, Anthropic, Google and others with a single endpoint and key across every application.
- Cutting Inference Spend: Use caching recommendations and model-switch suggestions to lower the cost of an existing production workload without changing application code.
- Replacing a Frontier Model with a Small One: Distill or fine-tune a small model on your own traffic for a narrow repetitive task and serve it at a fraction of frontier-model cost and latency.
- Chargeback and Budgeting: Attribute AI spend to individual agents, teams or people for internal cost allocation and to enforce per-key budget caps.
- Evaluating New Model Releases: Compare a newly shipped model against your current one on your own traffic before committing to a migration.
- Hybrid Local and Hosted Serving: Route some workloads to self-hosted GPUs at zero marginal cost while sending the rest to hosted providers through the same interface.
- Self-Hosting the Gateway: Run the open-source gateway inside your own infrastructure when hosted routing is not an option.
Gemini 3.1 Pro
Google (Google Research / Google DeepMind)
High-capacity multimodal model optimized for complex reasoning and very long-context tasks when simple answers aren’t enough.
Key features
- 1M+ Token Context Window: Supports extremely long contexts (reported 1,048,576+ token capacity) enabling analysis, summarization, and reasoning over very large documents, codebases, or multi-file datasets.
- Enhanced Multi-step Reasoning: Improved capabilities for complex, multi-step problem solving and chain-of-thought style reasoning for planning, debugging, and research tasks.
- Multimodal Input Support: Accepts text, images, PDFs and video inputs, letting users combine modalities in a single session for richer understanding and cross-modal retrieval.
- API Accessibility and Model ID: Available through the Gemini API with the model identifier gemini-3.1-pro, enabling programmatic integration into applications and developer tooling (CLI, Vertex AI, Google Cloud).
- Large Output Support: Capable of producing very long outputs suitable for detailed reports, long-form generation, and exhaustive code or document revisions (community config cites output windows up to 65,536 tokens).
- Phased Rollout & Access Controls: Released via a staged rollout (initially to AI Ultra / AI Ultra for Business subscribers and via API keys with appropriate permissions) with session and quota behaviors managed per Google account or API key.
- Very large context window: 1M+ tokens (e.g., 1,048,576 context in provider configs)
- Multi-modal input support: text, image, PDF, video
- Text output modality (configurable large output limit noted in configs: 65536)
- Available via Gemini API and Gemini CLI (gemini tool)
- Model IDs: gemini-3.1-pro and gemini-3.1-pro-preview
- Enhanced reasoning and complex problem-solving capabilities compared with earlier Gemini releases
- Phased rollout with API-key immediate availability (if permissions enabled) and staged Google Login rollout (AI Ultra tiers prioritized)
- Integrates with Google platforms such as AI Studio and Vertex AI (as referenced in rollout guidance)
Best for
- Long-form Research Synthesis: Ingest and synthesize entire research papers, corpora, or legal collections (multi-file PDFs and documents) and produce structured summaries, literature reviews, or annotated bibliographies across 1M+ token contexts.
- Large-Scale Codebase Analysis: Perform architectural analysis, cross-file refactoring suggestions, and multi-step debugging for million-line codebases by maintaining context across many files and commits.
- Enterprise Knowledge Assistant: Index and query company knowledge (handbooks, contracts, PDFs, recorded meetings) to answer complex policy and compliance questions requiring multi-document reasoning.
- Multimodal Media Intelligence: Analyze and correlate video transcripts, images, and associated documents to produce investigative reports, scene summaries, or multimedia content plans.
- Strategic Planning and Simulation: Drive multi-step scenario planning, decision trees, and detailed stepwise recommendations for product, legal, or research strategies requiring deep reasoning over prolonged context.
- Long-form document understanding and summarization using 1M+ token context
- Multi-modal analysis combining text with images, PDFs, or video
- Complex reasoning and multi-step problem solving (research, technical analysis, legal/medical summarization)
- Large-codebase generation, review and debugging where sustained context is required
- Interactive agents and assistants that must maintain very large conversational state