Last refreshed 2026-09-22. Next refresh: weekly.
Why use Yi 34B 200K on Replicate API?
Replicate API offers Yi 34B 200K with pay-as-you-go pricing at $0.20/1M input tokens. Replicate is a cloud-based platform that enables users to run machine learning models easily and efficiently.
Compare Yi 34B 200K across 3 providers to find the best fit for your use caseSetup recipe
Python + curlpip install replicateexport REPLICATE_API_TOKEN=...import replicate
output = replicate.run(
"01-ai/yi-34b-200k",
input={"prompt": "Hello"}01-ai/yi-34b-200kRequest example
import replicate
# reads REPLICATE_API_TOKEN from env
# 01-ai/yi-34b-200k format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"01-ai/yi-34b-200k",
input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))Gotchas
- Use provider model ID "01-ai/yi-34b-200k", not the LLMReference slug "yi-34b-200k".
- Replicate uses "owner/model-name" format (e.g. "meta/meta-llama-3-8b-instruct") for the latest version, or "owner/model-name:version-sha" to pin to a specific version. The REST endpoint splits owner and model-name into the path: /v1/models/{owner}/{model-name}/predictions.
- The examples expect REPLICATE_API_TOKEN; rename it only if your application config maps the new variable.
Compare Yi 34B 200K Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Alibaba Cloud PAI-EAS | — | — |
| Fireworks AI | $0.90 | $0.90 |
| Replicate API | $0.20 | $1.00 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.20 |
| Output tokens | $1.00 |
Capabilities
No model capability flags are currently sourced.
About Yi 34B 200K
The Yi 34B 200K is a sophisticated large language model by 01.AI that excels in varied NLP tasks, featuring an impressive 34 billion parameters and a 200,000-token context window to handle extensive text inputs. Built on a Transformer architecture, it differentiates itself from models like Llama by employing unique training methods such as Grouped-Query Attention and RoPE with adjusted base frequency. It showcases strengths in language comprehension, commonsense reasoning, and bilingual support for English and Chinese. Despite its advanced capabilities, it shares common LLM limitations like hallucination and non-determinism.