# Yi-Large API Rate Limits, Pricing & Performance (July 2026)

- Tool: AI API Limits & Performance Matrix
- Last updated: July 2026

## TL;DR

01.AI's international first-party API is closed — the platform.01.ai shutdown banner has been displayed since at least August 25, 2024 (year not definitively confirmed from primary source). Fireworks AI is the sole active third-party host, serving Yi-Large at $3.00 in / $3.00 out per million tokens with a 32K–33K token context window. For new projects, Qwen3-Max or Llama 4 Scout offer larger context and lower input costs.

## Frequently asked questions

### Does Yi-Large have an API?

01.AI's international first-party API at platform.01.ai is closed as of at least August 25, 2024 (year not definitively confirmed from a primary source). Fireworks AI is the sole confirmed active third-party host as of July 10, 2026, serving Yi-Large at $3.00 in / $3.00 out per million tokens. There is no official 01.AI endpoint you can sign up for directly.

### How do I access Yi-Large now that the 01.AI API is closed?

Use Fireworks AI: set your base URL to https://api.fireworks.ai/inference/v1 and model to accounts/fireworks/models/yi-large. The Fireworks API is OpenAI-SDK compatible, so you only need to change the base_url and model name in your existing code. Sign up at fireworks.ai and add a payment method for pay-as-you-go access.

### What are Yi-Large's rate limits?

Yi-Large's rate limits on Fireworks are not individually published — they follow Fireworks standard limits for pay-as-you-go accounts. Exact RPM and TPM are shown in your Fireworks console after signing up. The first-party 01.AI rate limits are not applicable as that API is closed.

### How much does Yi-Large cost per million tokens?

$3.00 per million input tokens and $3.00 per million output tokens on Fireworks AI — an unusually symmetric flat rate. There is no published caching or batch discount for Yi-Large on Fireworks. Verify the current rate on the Fireworks pricing page, as it may change without notice.

### Is Yi-Large free to use?

There is no permanent free tier for Yi-Large. Fireworks AI may offer trial credits for new accounts — check at sign-up. The former 01.AI free tier is not available as the international platform is closed. For free inference at scale, self-hosting Yi-34B or Yi-6B under the Apache 2.0 license is possible, though Yi-Large specific weights availability should be verified on Hugging Face.

### Yi-Large vs Qwen3-Max: which should I use?

For new projects: Qwen3-Max. It offers 262K context (vs Yi-Large's 32K–33K), an active first-party API on DashScope at $1.20/$6.00 (vs Yi-Large's $3.00/$3.00 flat on Fireworks), and a 50% batch discount. Yi-Large's only comparative advantage is its $3.00/MTok output rate, which is lower than Qwen3-Max's $6.00 output rate — but the context and development activity gap is decisive for most use cases.

### How do I increase Yi-Large rate limits on Fireworks?

Contact Fireworks sales for enterprise rate limit increases. For standard pay-as-you-go accounts, limits are set by Fireworks policy and shown in your console. If Yi-Large's limits are insufficient for your scale, RapidDev can help you evaluate multi-provider quota strategies and migration paths — reach out at rapidevelopers.com/contact.

### What should I do if I get a 429 error from Yi-Large on Fireworks?

Implement exponential backoff: start with a 1-second delay, double each retry, add random jitter, and cap at 60 seconds. Honor the Retry-After header if Fireworks includes one. If 429s are frequent, check your Fireworks console for your quota ceiling and consider contacting Fireworks for an increase, or evaluate migrating to Llama 4 Scout ($0.08/$0.30) or Qwen3-Max ($1.20/$6.00) for more cost-effective high-volume inference.

### Can I self-host Yi-Large?

Yi-34B and Yi-6B are available on Hugging Face under the Apache 2.0 license and can be self-hosted commercially. Yi-Large's specific full-model weights may not be fully publicly available — verify on huggingface.co/01-ai before planning a self-host deployment. For the open-weight variants, you need sufficient GPU memory: Yi-34B requires approximately 70GB at FP16 (2× A100 40GB).

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Source: https://www.rapidevelopers.com/ai-api-limits-performance-matrix/yi-large
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