Last refreshed 2026-06-19. Next refresh: weekly.
Why use Qwen3-Coder-480B-A35B-Instruct on GCP Vertex AI?
GCP Vertex AI offers Qwen3-Coder-480B-A35B-Instruct with pay-as-you-go pricing at $0.22/1M input tokens. Vertex AI is Google Cloud's managed AI platform, offering access to Gemini models and hundreds of partner models alongside tools for fine-tuning, grounding, vector search, and end-to-end MLOps pipelines.
Compare Qwen3-Coder-480B-A35B-Instruct across 7 providers to find the best fit for your use caseSetup recipe
Python + curlpip install google-cloud-aiplatformexport GOOGLE_CLOUD_PROJECT=...import os
import vertexai
from vertexai.generative_models import GenerativeModel
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")qwen3-coder-480b-a35b-instructRequest example
import os
import vertexai
from vertexai.generative_models import GenerativeModel
# Reads GOOGLE_CLOUD_PROJECT from env; authenticates via Application Default Credentials
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")
model = GenerativeModel("qwen3-coder-480b-a35b-instruct")
response = model.generate_content("Hello")
print(response.text)Gotchas
- For Google-published models use the model name directly, e.g. "gemini-2.0-flash-001". For third-party publishers (Anthropic, Meta, etc.) use the full publisher path, e.g. "publishers/anthropic/models/claude-3-5-sonnet-v2@20241022".
- The examples expect GOOGLE_CLOUD_PROJECT; rename it only if your application config maps the new variable.
Compare Qwen3-Coder-480B-A35B-Instruct Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Fireworks AI | — | — |
| GCP Vertex AI | $0.22 | $1.80 |
| NVIDIA NIM | — | — |
| AWS Bedrock | — | — |
| Vercel AI Gateway | $1.50 | $7.50 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.22 |
| Output tokens | $1.80 |
Capabilities
About Qwen3-Coder-480B-A35B-Instruct
Qwen3-Coder-480B-A35B-Instruct is Alibaba's flagship open-source code generation and agentic model, released July 22, 2025 under the Apache 2.0 license. The model has 480 billion total parameters with 35 billion active parameters per token, organized across 62 transformer layers with 160 specialized expert networks and 8 experts activated per token. It uses Grouped Query Attention (GQA) with 96 query heads and 8 key-value heads and supports a native context window of 262,144 tokens, extendable to 1 million tokens via YaRN position scaling.