m2m100-1.2b
meta
Multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation
- Context window
- — tokens
- Input cost
- $0.34 / 1M
- Output cost
- $0.34 / 1M
- Latency (p50)
- 83 ms
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Run side-by-side checks for pricing, context window, and latency.
Price (lower is better), Speed/Latency (lower is better), Performance (higher is better)
Google: Gemma 2 9B is 1040% cheaper
💰Google: Gemma 2 9B is 91% cheaper than m2m100-1.2b
⚡Google: Gemma 2 9B is 83ms faster than m2m100-1.2b
raghav-singhal
Open-source 1pp-0.58b-v2-asst-seed1234-sft model from raghav-singhal — available for download and self-hosting on Hugging Face.
raghav-singhal
Open-source 1pp-0.98b-v2-raw-seed42-sft model from raghav-singhal — available for download and self-hosting on Hugging Face.
raghav-singhal
Open-source 1pp-0.58b-v2-raw-seed1234-sft model from raghav-singhal — available for download and self-hosting on Hugging Face.
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meta
Multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation
Gemma 2 9B by Google is an advanced, open-source language model that sets a new standard for efficiency and performance in its size class. Designed for a wide variety of...