Which LLM is the cheapest for our workload?

The best-for pages rank the live catalogue per task by the index that measures it, and the router picks the cheapest model that meets your quality rule for each request.

How much can we save on our LLM bill?

On the public benchmark the router saved 89% against sending everything to the reference model. Import last month's provider bill on the bill page and the estimate is computed on your own traffic before you change a line of code.

How often do model prices change?

The catalogue is refreshed from the vendors' price lists; each model page shows the date of its data.

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OpenSmartRoute

Mercury 2.5 by Inception: pricing, context window and specs - OpenSmartRoute

Model catalogue

Inception

Mercury 2.5

inception/mercury-2.5Catalogue: Indexed in the reference catalogue; nothing on this deployment answers as this model yet.

JSON

Specification

Published by the vendor; the platform bills at the target's declared price.

Mercury 2.5

reasoning tools

Inception inception/mercury-2.5

Mercury 2.5 is the fastest reasoning LLM, and the latest diffusion LLM (dLLM) from Inception. Instead of generating tokens sequentially, Mercury 2.5 produces and refines multiple tokens in parallel, achieving...

Input $/1M: $0.04
Output $/1M: $0.15
Context: 260K
Max output

Alternatives, and where the router puts Mercury 2.5

Mercury 2.5 earns a request when it needs step-by-step reasoning, tool calls, very long inputs; a short, general request would go to Nemotron 3.5 Super VL (free) instead - free per million input tokens against $0.04 here; and the hardest requests, where the quality floor rules it out, to Claude Opus 5.5 (batch). Your rules decide the floor; the trace shows the comparison for every request.

Cheaper alternatives

Lower input price and an intelligence index within 5 points.

On this deployment

Nothing on this deployment answers as this model yet. Connect your own provider under Model providers, or add a target to your catalogue - the router picks it up without retraining.

Not available here yet

Signed in, add the model under Model providers; self-hosting, add a target with metadata.model set to this id.

targets.yamlyaml

# targets.yaml - declare a target that runs Mercury 2.5
targets:
  - id: mercury-2-5
    kind: llm
    name: Mercury 2.5
    metadata:
      model: inception/mercury-2.5          # links the target to this catalogue entry
    capabilities:
      domains: [general]
      supports_tools: true
      context_window: 260000
    cost:
      usd_per_1k_tokens: 0.000095

Price among Inception models

Input list price per million tokens; Mercury 2.5 is the cheapest of 3 listed.

1
Mercury 2.5
$0.04
2
Mercury 3
$0.10
3
Mercury 2
$0.25

Mercury 2.5 earns a request when it beats the field on your objective, not on the vendor's page.

The specification above is what Inception publishes. What the router uses is the price you actually pay here, the latency measured on this deployment and the outcomes people reported - and it compares those against every other model that could take the request. Price a workload on the estimator, or send one live in the playground.

Photo: RDNE Stock project

For the business

What it means for which models you pay for.

Catalogue of every vendor
The right model, not the famous one
Price, context, abilities and benchmark indexes side by side, with the model that fits each task named.
The rankings
89% less
Lower cost per request
On the 150-prompt benchmark the router spent 89% less than sending every prompt to the reference model, and the judged quality went up, not down.
Read the benchmark
Per million tokens, every model
Prices you can compare

Mercury 2.5

Specification

Mercury 2.5

Alternatives, and where the router puts Mercury 2.5

Cheaper alternatives

On this deployment

Price among Inception models

More from Inception

Mercury 2.5 earns a request when it beats the field on your objective, not on the vendor's page.

What it means for which models you pay for.

The right model, not the famous one

Lower cost per request

Prices you can compare

Better alternatives