Minimum context capacity as a hard filter instead of manually scanning model cards.
Large-context model selection
Need huge context? Make it a constraint, not the conclusion.
A large context window gets a model into the candidate set. It does not tell you whether that model is the right choice for coding, document synthesis, extraction, agents, or your budget.
The decision
The answer changes with the workload.
Long documents, repository-scale code, accumulated agent state, and multi-source synthesis can all require substantial context. ModelShortlist can enforce the minimum window you actually need, then let the host AI reason about cost, tools, benchmark evidence, and other workload requirements among the models that remain.
Input pricing, which becomes especially important when prompts routinely occupy large portions of the context window.
Tool/function-calling requirements for large-context agent workflows.
Quality and performance evidence so context size is not mistaken for task competence.
Independent performance evidence
Artificial Analysis
ModelShortlist uses Artificial Analysis benchmark and performance evidence when the model identity can be reconciled confidently. It does not create, relabel, or pretend ownership of those benchmarks.
How Artificial Analysis contributesCurrent operational facts
OpenRouter
The current catalog supplies model availability, context, supported parameters, pricing, and provider details. ZDR endpoint evidence is applied only when the workload explicitly requires ZDR.
Compare OpenRouter models by workloadModel selection is time-sensitive: new models launch, prices move, benchmark results change, tool support evolves, and providers add or remove endpoints. ModelShortlist surfaces freshness and degraded upstream state instead of silently presenting stale evidence as current.
Ask naturally
Prompts that work.
ModelShortlist sits behind the AI assistant you already use. Describe the job and hard constraints instead of translating them into a fixed ranking formula.
“I need at least 200k context and tool calling. What are my best current options?”
“Which models can handle a very large document corpus while keeping input cost reasonable?”
“I need 100k+ context for a coding agent. Prioritize agentic quality, but show me the cost tradeoffs.”
Why ModelShortlist
Current evidence, not a static leaderboard.
Reads current context and capability metadata from the OpenRouter catalog rather than relying on a static comparison table.
Can combine minimum-context filtering with output/input price ceilings and tool requirements.
Keeps models without an Artificial Analysis match eligible instead of silently dropping newer or unmatched options.
Lets the host AI decide whether a bigger window is actually worth a higher price for the specific workload.
Local BYOK architecture. No ModelShortlist account, hosted key vault, or telemetry in the MCP.
Put current model-selection evidence inside your assistant.
Install the local MCP, add your OpenRouter and Artificial Analysis keys, and ask the model-selection question in normal language.
Open the install configurator