AI Model Intelligence Platform

Every AI Model.
One Clear Map.

Hundreds of models. Dozens of providers. Shifting prices and capabilities every week. aimodels.ms cuts through the noise — mapping the entire landscape so you choose the right model before you write a single line of code.

0
Models indexed
0
Providers tracked
0
Weekly freshness rate
<1
Avg. decision time
TRUSTED BY ENGINEERS AT Fortune 500 AI Teams Series A Startups MLOps Platforms Research Labs
The Challenge

The Model Landscape is a Noise Problem

Hundreds of models. Dozens of providers. Shifting prices, limits, and capabilities every week. The information is scattered across blogs, release notes, and forums — and half of it is stale the moment it's published.

🗂️
Scattered Information
Capabilities, pricing, and benchmarks are spread across dozens of sources with no single source of truth.
📉
Instant Staleness
Release cycles are weekly. By the time you read a comparison, the pricing has changed and a new model has shipped.
💸
Wrong Model = Real Cost
Choosing without a current map means rework, overspend, and missed quality benchmarks that compound over time.
🔄
Deprecation Surprises
Models get deprecated with little warning. Migrations are expensive when you don't see them coming.
DATA FRESHNESS ACROSS SOURCES
Provider blogs
32%
Community forums
48%
Release notes
61%
Benchmark sites
54%
aimodels.ms
99%
Platform Features

Built for Builders, Not Browsers

Every feature is designed around a single outcome: the fastest, most confident model selection decision possible.

🎯
Decision Support
Use-Case Guidance
Recommendations for common workloads so you start from a curated shortlist, not a blank page with 400 options.
⚖️
Analysis
Honest Trade-Offs
Each model's strengths and limitations summarized in plain language. No vendor spin, no marketing copy.
🔄
Lifecycle
Migration Notes
Clear guidance when a model is deprecated or superseded. Plan migrations before they become emergencies.
🔗
Evidence
Linked Sources
Docs, benchmarks, and community feedback gathered in one place. Validate before you build, not after.
API-First
Programmatic Access
Pull model metadata via API to keep your own tooling and internal dashboards automatically current.
📡
Tracking
Release Intelligence
Follow new models as they ship — without trawling a dozen newsletters, X threads, and provider blogs every morning.
How It Works

From Question to Confident Choice

A four-step process that turns a recurring research burden into a fast, deliberate decision.

Define Your Requirements

Tell aimodels.ms what you're building — your task type, budget ceiling, deployment target, and latency requirements. This becomes your filter configuration, not a search bar.

  • Specify task modality (text, vision, code, audio)
  • Set budget and context-window floor
  • Choose deployment target (cloud, edge, on-prem)
  • Flag licensing constraints (commercial / open)
task: "document summarization"
budget_max: $5 / 1M tokens
context_min: 128 K tokens
deploy: cloud API
license: commercial OK
→ filtering 400+ models...

Filter to the Shortlist

The catalog filters instantly to the models that genuinely fit your constraints — not a long ranked list, but a focused shortlist of viable options worth evaluating.

  • Hard filters eliminate mismatches immediately
  • Soft rankings surface best-fit candidates
  • Deprecated models flagged with migration paths
  • New entrants highlighted for consideration
Models matching criteria12 of 400+
Eliminated by budget147 models
Eliminated by context89 models
Deprecated / EOL23 flagged

Compare on What Matters

The shortlist is your working surface. Compare models on the dimensions that actually drive the decision for your use case — not generic benchmarks that may not apply.

  • Side-by-side pricing at your expected volume
  • Context window and throughput comparison
  • Licensing, SLA, and compliance review
  • Shareable comparison link for team alignment
Claude Sonnet 4.6 @ 10M tok/mo$30/mo
GPT-4.1 @ 10M tok/mo$20/mo
Gemini 2.0 Flash @ 10M tok/mo$1/mo

Validate, Then Build

Jump straight from the catalog to docs, benchmark reports, and community feedback. Validate assumptions before writing code, not after discovering a mismatch in production.

  • Direct links to official API documentation
  • Curated benchmark results for your task type
  • Community signal on reliability and quirks
  • Version history to anticipate deprecation windows
Official API docs✓ Linked
MMLU benchmark89.4%
HumanEval (code)92.1%
Model age / versionv3 — 6 mo old
Use Cases

Where aimodels.ms Fits

From first model selection to organization-wide governance — four scenarios where the map pays for itself.

Choosing a Model for a New Feature

Starting a new AI feature means navigating 400+ models on day one. aimodels.ms gets you to a validated shortlist in minutes instead of days of scattered research.

  • Filter to task-relevant models immediately
  • Price-check at your expected token volume
  • Verify context window fits your payload
  • Check licensing before committing to an architecture
Research time saved~2–4 days
Models considered400+ → 8
Decision confidenceHigh ↑
Rework riskReduced ↓

Standardizing an Approved Model List

Organizations scaling AI usage need approved lists — curated, maintained, and aligned to budget and compliance requirements. aimodels.ms is the working surface for building and updating that list.

  • Export curated shortlists for policy review
  • Track licensing status across all approved models
  • Flag deprecation risks before they become incidents
  • Share comparisons with stakeholders and legal
Approved models trackedEnterprise-scale
License review cyclesAutomated alerts
Stakeholder sharingOne-click links

Planning a Migration Off a Deprecated Model

Models get deprecated with little warning. aimodels.ms surfaces deprecation timelines and recommends successors so you plan migrations before they become production emergencies.

  • Deprecation dates tracked and surfaced proactively
  • Successor model recommendations ready to review
  • Migration notes on behavior changes and API diffs
  • Cost-impact comparison before committing
Deprecation lead timeEarly warning
Successor mappingAuto-suggested
Migration urgencyPrioritized

Staying Oriented Month to Month

The landscape shifts weekly. aimodels.ms is the instrument panel that keeps you oriented as new models ship, prices change, and the competitive picture evolves — without full-time research overhead.

  • New model alerts as they enter the catalog
  • Price change notifications for tracked models
  • Monthly digest of significant capability shifts
  • API feed for automated internal updates
Update frequencyWeekly cadence
New models per month20–40 tracked
Research overhead saved~8 hrs/month
Data & Trust

Built on Verifiable Data

Every data point in the catalog is sourced, linked, and reviewed. No estimates, no inferred pricing, no vendor-supplied-only specs.

All pricing verified against official provider pages

Context window specs linked to official documentation

Benchmark scores from published, reproducible runs

Licensing status verified and flagged for commercial use

Deprecation alerts sourced from official release channels

🔗

Source-Linked

Every data point links back to its origin

🔄

Weekly Refresh

Catalog updated as releases ship

🧪

Benchmark-Backed

Reproduced results only

🚫

No Vendor Bias

Neutral, independent coverage

What Teams Say

Trusted by Engineers Choosing Deliberately

Before aimodels.ms, every model decision meant two days of research. Now I get to a shortlist in under an hour. That's not a minor improvement — it's a workflow change.
AK
Anika Karan
Staff ML Engineer, Series B SaaS
The deprecation tracking alone is worth it. We got early warning on GPT-4 going end-of-life and migrated on our timeline instead of scrambling.
MR
Marcus Reyes
Head of AI Infrastructure, Fintech
I use aimodels.ms to build the approved-model list for our org. It gives compliance the licensing evidence they need and gives engineering the comparison they actually want.
SL
Sophia Lin
AI Platform Lead, Enterprise
FAQ

Common Questions

How often is the catalog updated?+
The catalog is refreshed on a weekly cadence, with high-velocity providers (OpenAI, Anthropic, Google) monitored more frequently. When a major model ships or pricing changes significantly, the entry is updated within 24–48 hours of the official announcement.
What modalities does aimodels.ms cover?+
The catalog covers text, vision, audio, code, and multimodal models. Specialized models (embedding, speech-to-text, image generation) are indexed separately. You can filter by any combination of modalities using the filter interface.
How is pricing data verified?+
All pricing is sourced directly from official provider pricing pages and is linked for independent verification. When providers update pricing, entries are flagged and updated in the next weekly pass or sooner for significant changes. We do not interpolate or estimate pricing.
Can I access the catalog via API?+
Yes. The catalog exposes a REST API that returns structured model metadata including specs, pricing, context window, licensing, and benchmark scores. SDKs are available for Python and JavaScript. API access is available on paid plans.
Does aimodels.ms cover open-source models?+
Yes — open-source and open-weight models are a first-class part of the catalog. Coverage includes Llama, Mistral, Qwen, DeepSeek, Phi, Gemma, and other significant open models. Deployment guidance for self-hosting and edge inference is included where applicable.
How does aimodels.ms stay vendor-neutral?+
aimodels.ms has no commercial relationship with any model provider. There are no sponsored placements, affiliate arrangements, or preferential rankings. The catalog is funded by user subscriptions. The goal is your best decision, not any provider's conversion metric.
Get Started

Map the Model Landscape

Stop researching. Start deciding. aimodels.ms turns a recurring research burden into a fast, confident choice — every time the landscape shifts.