OpenRouter Models Analyzer#

Pulls the live model catalog from https://openrouter.ai/api/v1/models, normalizes prices to $ / 1M tokens, and lets you filter / sort / compare models across providers.

Quick start#

bash

# 1. Fetch the catalog (no API key needed — endpoint is public)
python3 scripts/fetch_models.py
# -> writes models_raw.json next to the script

# 2. Analyze
python3 scripts/analyze.py --family anthropic/
python3 scripts/analyze.py --free --has-image
python3 scripts/analyze.py --has-cache --sort input_cache_read --limit 10
python3 scripts/analyze.py --has-image --min-ctx 1000000 --sort prompt --limit 20

The analyze.py script does not need network access — it works off the cached models_raw.json. Re-run fetch_models.py to refresh.

Tip: Always run fetch_models.py first to ensure the data is current.

What you get per model#

• id, name, context_length
• modality (legacy string) plus input_modalities / output_modalities lists
• tokenizer
• 8 normalized prices in $ / 1M tokens (or $ / 1M calls for web_search): prompt, completion, input_cache_read, input_cache_write, web_search, internal_reasoning, image, audio

See references/pricing_fields.md for the full schema and unit gotchas (notably that web_search is per call, not per token, and -1 means variable pricing on router models).

Filter flags#

FlagEffect--family PREFIXid starts with PREFIX, e.g. anthropic/, openai/, qwen/``--keyword Kcase-insensitive substring match on id or name--min-ctx / --max-ctxcontext-length window--has-image / --has-audio / --has-video / --has-fileinput modality--outputs-imageoutput modality contains image``--free``prompt == 0 AND completion == 0``--has-cache``input_cache_read is priced--has-reasoning-price``internal_reasoning is priced

Sort keys#

id (default), prompt, completion, input_cache_read, input_cache_write, web_search, internal_reasoning, ctx.

Missing values sort to the end regardless of direction.

Use --desc to sort in descending order (e.g. most expensive first).

Output formats#

• Default: aligned text table with all 8 pricing columns
• --json: structured JSON output (ideal for programmatic consumption)
• --stats: print coverage statistics (how many models have each pricing field, modality breakdown)

Typical analyses#

bash

# Cheapest cache_read among vision-capable 1M-context models
python3 scripts/analyze.py --has-image --min-ctx 1000000 \
    --has-cache --sort input_cache_read --limit 10

# All free models that take image input
python3 scripts/analyze.py --free --has-image

# Most expensive models (per prompt)
python3 scripts/analyze.py --sort prompt --desc --limit 15

# Reasoning-priced models
python3 scripts/analyze.py --has-reasoning-price

# Models with image output (image generators)
python3 scripts/analyze.py --outputs-image

# JSON output for programmatic use
python3 scripts/analyze.py --family anthropic/ --json

# Coverage statistics
python3 scripts/analyze.py --stats

Provider cheat sheet#

See references/model_families.md for the full list of provider prefixes and naming conventions ( -thinking, -vl, :free, etc.).

Common ones:

• anthropic/, openai/, google/, x-ai/ — frontier
• meta-llama/, deepseek/, qwen/, mistralai/, nvidia/, microsoft/, amazon/
• openrouter/auto, openrouter/fusion — variable-price routers ( -1)

Notes & caveats#

• web_search is per call, not per token — the script displays it as $/1M calls (i.e. the value is dollars per 1M invocations).
• -1 for prompt / completion means variable pricing (router models). The script filters these out of numeric columns and renders -.
• Not all models expose every pricing field. Use --stats to see current coverage for each field.
• modality is the legacy compact string; the input_modalities / output_modalities lists are canonical.
• top_provider.context_length may be smaller than the model's own context_length for a specific provider endpoint.