The LLM Price Index
Every major large language model, normalized to dollars per million tokens. Scored on value, cheapness, and frontier capability. Independent, automated, refreshed daily.
What this is
The LLM Price Index is a live, independently maintained price comparison for every large language model offered through the OpenRouter catalog. Input and output pricing is normalized to dollars per million tokens, blended 3:1 to produce a single comparable figure, and scored 0–10 on three axes: value, cheapness, and frontier capability. The cheapest paid model right now is Gemma 3n 4B at $0.025 per million tokens (3:1 blended).
Mistral Nemo
7.2/10A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA. The model is multilingual, supporting…
Devstral Small 1.1
6.6/10Devstral Small 1.1 is a 24B parameter open-weight language model for software engineering agents, developed by Mistral AI in collaboration with All…
Devstral 2 2512
5.7/10Devstral 2 is a state-of-the-art open-source model by Mistral AI specializing in agentic coding. It is a 123B-parameter dense transformer model supporting…
Mistral Small Creative
5.6/10Mistral Small Creative is an experimental small model designed for creative writing, narrative generation, roleplay and character-driven dialogue, general-purpose instruction following, and…
Mistral Small 3
5.0/10Mistral Small 3 is a 24B-parameter language model optimized for low-latency performance across common AI tasks. Released under the Apache 2.0 license,…
Saba
4.7/10Mistral Saba is a 24B-parameter language model specifically designed for the Middle East and South Asia, delivering accurate and contextually relevant responses…
Mistral Large 2411
4.2/10Mistral Large 2 2411 is an update of [Mistral Large 2](/mistralai/mistral-large) released together with [Pixtral Large 2411](/mistralai/pixtral-large-2411) It provides a significant upgrade…
Mixtral 8x7B Instruct
3.9/10Mixtral 8x7B Instruct is a pretrained generative Sparse Mixture of Experts, by Mistral AI, for chat and instruction use. Incorporates 8 experts…
Mixtral 8x22B Instruct
3.6/10Mistral's official instruct fine-tuned version of [Mixtral 8x22B](/models/mistralai/mixtral-8x22b). It uses 39B active parameters out of 141B, offering unparalleled cost efficiency for its…
Mistral 7B Instruct v0.1
3.4/10A 7.3B parameter model that outperforms Llama 2 13B on all benchmarks, with optimizations for speed and context length.
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Price drops, new models, deprecations
Every Tuesday: which models got cheaper, which launched, which got pulled. Five minutes. No filler.
Three axes, one overall score
- Value (35%) — capability per dollar. Context length, vision, tools, and structured-output support divided by log-scaled blended price.
- Cheapness (35%) — raw affordability. Free models score 10. Paid models use an inverse log curve anchored at $0.01 / Mtok.
- Frontier (30%) — how close to the state of the art. Recent releases, long context windows, and premium pricing all contribute.
Blended price formula
- Most production workloads are input-heavy, so the index uses a 3:1 blended price:
(input × 0.75) + (output × 0.25). - All prices are normalized to dollars per million tokens. OpenRouter publishes per-token figures which we multiply by 1,000,000 before display.
Where does the pricing data come from?
Every model and price on this page is sourced from OpenRouter's public models API, which aggregates pricing directly from model providers including Anthropic, OpenAI, Google, Mistral, Meta, xAI, DeepSeek, and dozens of others. The pipeline re-fetches and re-scores the entire catalog once per day.
Why normalize to $/million tokens?
Model providers publish prices in inconsistent units — per 1K tokens, per million tokens, per character, sometimes per request. Comparing them directly is error-prone. Dollars per million tokens is the industry's most common reporting unit and makes cross-provider comparisons immediate and honest.
What does "3:1 blended" mean?
Most production LLM workloads are input-heavy — context, RAG retrievals, system prompts — while output is comparatively short. A 3:1 input:output ratio is the informal industry convention for producing a single number that reflects typical cost: (input × 0.75) + (output × 0.25). Your actual ratio may differ; always check both input and output columns for workloads with long generations.
What's the cheapest LLM right now?
The cheapest paid model as of the latest scan is Gemma 3n 4B from Google at $0.025 per million tokens (3:1 blended). Sort by "Cheapest" above for the full ranking. Many providers also offer free-tier variants of their models, which score a perfect 10 on the cheapness axis.
Is this affiliated with OpenRouter or any provider?
No. MegaOne AI is independent. OpenRouter is used as a public data source because their models API is the most complete and up-to-date LLM catalog available, but this directory is not operated by OpenRouter and we rate all models — including ones that compete with one another.
How often does the price index update?
A full re-fetch, re-score, and daily snapshot runs once per 24 hours. Snapshots are written to a history table so we can build price-over-time charts and detect drops. New models typically appear within 24 hours of being added to OpenRouter.
Is it free to use?
Yes. Browsing, filtering, sorting, and searching the entire price index is free. The weekly email briefing is free. There is no account required and no paywall.