As of August 30, 2026, LFM2.5-2.6B API pricing is $0.02 per million input tokens and $0.02 per million output tokens. A typical 100K-in / 20K-out request costs $0.002. Source: https://www.liquid.ai
Blended / 1M
$0.04
Input
$0.02
Output
$0.02
100K / 20K
$0.002
Specs
65.5K
Aug 4, 2026
Open source
Percentile against every model with a published score on that eval. Click a row for the full board.
LiveCodeBench v6 against blended input+output $/M. Log price. LFM2.5-2.6B is the green point.
Published scores
8 evals · 4 groups
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As of August 30, 2026, LFM2.5-2.6B Liquid AI API pricing is $0.02 per million input tokens and $0.02 per million output tokens. Input tokens are the prompt you send; output tokens are the completion. Long answers therefore cost more than short ones even when the prompt stays the same. Source: https://www.liquid.ai
On this page the blended LFM2.5-2.6B API cost is $0.04 for 1 million input tokens plus 1 million output tokens. That 1:1 mix is the comparison scale used across the LLM pricing table. A 3:1 mix (three million input tokens per one million output tokens), which some calculators use, would be about $0.08.
A typical LFM2.5-2.6B API request with 100K input tokens and 20K output tokens costs about $0.002 at current rates as of August 30, 2026. Multiply that by daily or monthly volume to estimate spend. Caching, batch APIs, and volume discounts from Liquid AI can bring the real invoice lower than the public list rate at https://www.liquid.ai.
LFM2.5-2.6B supports a 66K token context window. The context window is the total prompt plus conversation history the model can see in one call. Larger windows cost more when you fill them, because every input token is billed per million tokens.
Yes. LFM2.5-2.6B is an open-weight model by Liquid AI. You can download the weights and self-host, or pay Liquid AI's hosted API and skip GPU ops. Hosted API pricing on this page is the public per-million-token rate, not the electricity cost of running the weights yourself.
LFM2.5-2.6B currently reports LiveCodeBench v6 59.4%, IFBench 59.2%, AIME 2025 51.9%. See the performance table for the full eval set. Use those scores with the token prices above when you are choosing between a cheaper LLM and a frontier model. GPQA, SWE-bench, and HLE are the evals people usually compare first.
Use the Compare picker on this page to start an LLM comparison. Search by model or provider, pick a second model, and AnotherWrapper opens a dedicated LFM2.5-2.6B vs page with per-million-token prices, blended 1M-in / 1M-out cost, context window, throughput, and benchmarks. You can also open any model from the sidebar, or go back to the LLM pricing table to sort the full index.
The cheapest LLM alternative depends on whether you optimize for blended token cost, coding evals, or context window. Start with the popular matchups on this page, then sort the LLM pricing table by blended cost to see which hosted APIs undercut LFM2.5-2.6B. Open-weight models can be cheaper still if you self-host, but that is not the same as Liquid AI API pricing on this page.
LFM2.5-2.6B is called through Liquid AI's API with an API key. Bill against input and output tokens separately; cache or shorten prompts if spend is high. If you are shipping a full product, AnotherWrapper includes pre-built API routes and templates for Liquid AI and 5+ other providers with auth, payments, and deployment already configured.
This LFM2.5-2.6B page tracks the latest published Liquid AI API pricing from Liquid AI, quoted per million tokens. List rates can differ from invoices because of prompt caching, batch discounts, committed-use tiers, and private contracts. Official rate sheet: https://www.liquid.ai. Benchmark scores come from official publications and independent evals, not from a private lab run by AnotherWrapper.
LFM2.5-2.6B is a open-source model from Liquid AI. Current API pricing is $0.02 per million input tokens and $0.02 per million output tokens, with a 65.5K token context window. A blended 1M-in / 1M-out workload costs $0.04.
Source: Liquid AI pricing (https://www.liquid.ai)
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