API model string
accounts/fireworks/models/yi-largeContext window
32K–33K tokens
Max output not published
- Knowledge cutoff
- not published
- Released
- June 2024
- Modalities
- text in, text out
Last verified July 10, 2026
Rate limits by tier
Yi-Large has no active first-party API — 01.AI's international platform is closed. Fireworks AI is the sole confirmed active third-party host as of July 10, 2026; all rate limits below refer to Fireworks standard policies, which are not individually published for this model.
| Tier | Requirements | RPM | TPM | RPD | Notes |
|---|---|---|---|---|---|
| 01.AI First-Party International API | CLOSED — not available | n/a | n/a | n/a | platform.01.ai shows shutdown banner: "API services will be temporarily unavailable starting from August 25th due to business adjustments." The platform redirects to the Chinese product (platform.lingyiwanwu.com / 零一万物). International API closed, assessed August 25, 2024 — year not definitively confirmed from primary source (verify). |
| Fireworks AI (sole active third-party host) | Fireworks account; payment method for pay-as-you-go | not published | not published | not published | Yi-Large available at $3.00 in / $3.00 out per MTok with 32.8K–33K context. Fireworks standard rate limits apply; exact RPM/TPM not published for this specific model. Verify current availability and limits in the Fireworks console. |
| OpenRouter | OpenRouter account | not published | not published | not published | Yi-Large model page exists on OpenRouter (released Jun 25, 2024, 33K context listed) but shows no active provider or price as of July 10, 2026. Do not use as a primary path without verifying current availability. |
| Self-host (open weights) | GPU hardware; Yi-34B or Yi-6B open weights (Apache 2.0 on Hugging Face) | self-managed | self-managed | self-managed | Yi-34B and Yi-6B are released under Apache 2.0 and commercially usable. Yi-Large specific full-model weights availability on Hugging Face is not fully confirmed — verify on huggingface.co/01-ai before planning a self-host path. No caching or batch API applicable. |
Swipe the table sideways to see every limit column.
- 1.01.AI pivoted away from LLM pre-training in March 2025, focusing on enterprise/DeepSeek-based solutions; the international API was not reinstated.
- 2.Last 01.AI model release listed November 29, 2024.
- 3.No caching or batch API from Fireworks for Yi-Large specifically — verify on Fireworks pricing page before assuming these features are available.
Limits verified against the 01.AI (first-party closed; hosted by Fireworks AI) docs, July 10, 2026.
Token pricing
What you pay per million tokens (USD). Input and output are billed separately.
Input
$3.00
per 1M tokens
Output
$3.00
per 1M tokens
- Pricing is for Fireworks AI — the sole active host. First-party 01.AI pricing is not published (API closed).
- Yi-Large uses an unusually symmetric flat rate: same $3.00/MTok for both input and output. Verify current rate on the Fireworks AI pricing page; sourced from CloudPrice.net as of July 10, 2026.
- No context caching or batch discount confirmed for Yi-Large on Fireworks — the 70%-cached scenario is not applicable.
- No free tier or trial credits specific to Yi-Large; Fireworks general trial credits may apply — confirm on sign-up.
Side-project chatbot
$18.00
per month
Assumptions
5M input / 1M output tokens per month via Fireworks at $3.00/$3.00
5 × $3.00 + 1 × $3.00 = $15.00 + $3.00 = $18.00
Mid-scale app
$216.00
per month
Assumptions
60M input / 12M output per month; no caching available
60 × $3.00 + 12 × $3.00 = $180.00 + $36.00 = $216.00 (no cache discount — not available for Yi-Large on Fireworks)
High-volume pipeline
$1,440.00
per month
Assumptions
400M input / 80M output per month via Fireworks at $3.00/$3.00
400 × $3.00 + 80 × $3.00 = $1,200.00 + $240.00 = $1,440.00
Run your own numbers
Drag your real monthly token volumes and watch the bill update live — priced against rival models at the same usage.
Estimated accounts/fireworks/models/yi-large spend
$105/mo
Input: $90
Output: $15
30M in × $3.00 + 5M out × $3.00 = $105
Same volume, priced across models
- Llama 4 ScoutCheapest$3.90
- Kimi K2$49
- Qwen3-Max$66
- accounts/fireworks/models/yi-largeThis model$105
Rivals priced at their published input/output rates for the same monthly volumes. Prompt caching is model-specific, so it is applied to accounts/fireworks/models/yi-large only. Estimates for comparison; real bills vary with request shape and long-context surcharges.
accounts/fireworks/models/yi-large vs the alternatives
Yi-Large on Fireworks AI compared to live alternatives with first-party APIs — all data verified July 10, 2026.
| Aspect | accounts/fireworks/models/yi-large | Qwen3-Max | Llama 4 Scout | Kimi K2 |
|---|---|---|---|---|
| First-party API | closed (01.AI international) | live (DashScope) | third-party hosts | legacy migrated to K2.6 |
| Context window | 32K–33K tokens | 262K tokens | up to 1M tokens (hosted) | 262K tokens |
| Input $/MTok | $3.00 (Fireworks) | $1.20 (DashScope) | $0.08 (DeepInfra) | $0.95 (K2.6) |
| Output $/MTok | $3.00 (Fireworks) | $6.00 | $0.30 | $4.00 |
| Multilingual support | EN / ZH / ES / JA / DE / FR | multilingual | multilingual | multilingual |
| License (open weights) | Apache 2.0 (Yi-6B / Yi-34B) | Qwen3 license | Llama 4 Community | Modified MIT |
| Active first-party development | halted (01.AI pivot March 2025) | active | active | active |
| Host diversity | 1 active host (Fireworks only) | DashScope + aggregators | many inference hosts | Fireworks + others |
Swipe the table sideways to see every model.
Hitting a 429? The playbook
The exact errors you'll see
HTTP 429 Too Many Requests (Fireworks AI)Fireworks standard rate limit exceeded (exact error string not published — verify in Fireworks documentation)401 Unauthorized (when attempting to use the closed 01.AI first-party endpoint)Why it happens & how to fix it
Calling the closed 01.AI first-party endpoint (api.01.ai or platform.01.ai)
The 01.AI international API is closed. Switch your base URL to Fireworks: https://api.fireworks.ai/inference/v1 with model accounts/fireworks/models/yi-large.
Fireworks RPM or TPM quota exceeded
Implement exponential backoff with jitter; honor any Retry-After header. Check your Fireworks console for current quota. Contact Fireworks support for quota increases.
Input exceeds Yi-Large's 32K–33K token context window
Truncate or summarize input before sending. Yi-Large has one of the shortest context windows in its class — plan for this hard limit. Consider Qwen3-Max (262K) for long-context use cases.
Fireworks account credits depleted
Add credits or a payment method to your Fireworks account. For high-volume workloads, evaluate migrating to Llama 4 Scout ($0.08/$0.30) to reduce costs significantly.
Retry strategy
For Fireworks AI: use exponential backoff with jitter on HTTP 429 responses. Honor the Retry-After header when present. Fireworks is OpenAI-SDK compatible — use the standard retry pattern with base_url override. Ramp request rate gradually after backoff to avoid burst re-triggering rate limits. Start with a 1-second base delay, doubling each attempt with a random jitter of 0–500ms, capped at 60 seconds.
1import OpenAI from "openai";23const client = new OpenAI({4 apiKey: process.env.FIREWORKS_API_KEY,5 baseURL: "https://api.fireworks.ai/inference/v1",6});78async function callYiLarge(9 messages: OpenAI.Chat.ChatCompletionMessageParam[],10 maxRetries = 511): Promise<string> {12 for (let attempt = 0; attempt < maxRetries; attempt++) {13 try {14 const response = await client.chat.completions.create({15 model: "accounts/fireworks/models/yi-large",16 messages,17 max_tokens: 1024,18 });19 return response.choices[0].message.content ?? "";20 } catch (err: unknown) {21 const error = err as { status?: number; headers?: Record<string, string> };22 if (error?.status === 429) {23 const retryAfter = parseInt(error.headers?.["retry-after"] ?? "0", 10);24 const base = retryAfter > 0 ? retryAfter * 1000 : Math.min(60000, 1000 * 2 ** attempt);25 const jitter = Math.random() * 500;26 await new Promise((r) => setTimeout(r, base + jitter));27 continue;28 }29 throw err;30 }31 }32 throw new Error("Max retries exceeded for Yi-Large on Fireworks");33}How to raise your limits
The ladder from the starter tier to enterprise — what each rung takes, and what it unlocks.
Fireworks trial
ImmediateSign up at fireworks.ai — trial credits may be available on new accounts
Unlocks: Limited free inference for evaluation; verify current trial credit offer on Fireworks sign-up
Fireworks pay-as-you-go
ImmediateAdd a payment method on fireworks.ai; Yi-Large is available at $3.00/$3.00 per MTok
Unlocks: Full Yi-Large access; Fireworks standard rate limits (exact RPM/TPM not published — shown in Fireworks console)
Fireworks enterprise
Sales cycle (days to weeks)Contact Fireworks sales at fireworks.ai
Unlocks: Higher rate limits, SLA guarantees, priority support
Migrate to a live alternative (recommended for new projects)
ImmediateSwitch model string to qwen3-max on DashScope intl, or llama-4-scout on Fireworks/Groq — no code changes beyond base_url and model name
Unlocks: Active first-party development, larger context (262K or 1M), lower input cost ($0.08–$1.20 vs $3.00)
Cut your token spend
Migrate to Qwen3-Max or Llama 4 Scout for new projects
2.5–37× lower input token costYi-Large on Fireworks charges $3.00/MTok input — Llama 4 Scout is $0.08/MTok (37× cheaper) and Qwen3-Max is $1.20/MTok (2.5× cheaper). Only keep Yi-Large if existing integrations depend on its specific behavior.
Plan around the 32K–33K context hard limit
Prevents silent truncation errors in productionYi-Large has one of the shortest context windows in the 2024 model generation. For conversations, RAG pipelines, or document tasks that can exceed 32K tokens, migrate to Qwen3-Max (262K) or Llama 4 Scout (up to 1M) before hitting production.
Monitor Fireworks for Yi-Large availability
Risk mitigation — sole active hostFireworks is currently the only active third-party host for Yi-Large. If Fireworks deprioritizes or removes the model, there is no confirmed fallback. Set up alerts on your API response codes and maintain a migration plan to an alternative model.
Use Yi-Large only for existing tuned integrations
Avoids unnecessary migration risk if already deployedIf your application has been fine-tuned or prompt-engineered specifically for Yi-Large's response style, staying on Fireworks for now is lower risk than re-tuning. Set a migration milestone for Q3 2026.
For multilingual CJK workloads, evaluate Qwen3-Max
Potentially better ZH/JA language quality from Alibaba's training dataQwen3-Max is trained by the same provider ecosystem (China-based) with deep CJK corpus coverage. If Yi-Large was selected for Chinese or Japanese language quality, Qwen3-Max is a natural migration target with active support.
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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