Qwen3 235B A22B Instruct 2507 Maas

Google Vertex AI chat Deprecated 14d ago
Heads-up: Google Vertex AI has scheduled Qwen3 235B A22B Instruct 2507 Maas for deprecation on Oct 21, 2026. Plan a migration. Use Agent Command Center's model fallback routing to swap models without code changes.

Qwen3 235B A22B Instruct 2507 Maas is a Google Vertex AI chat model.It supports a 262,144-token context windowwith up to 16,384 output tokens.Input is priced at $0.220/M tokens and output at $0.880/M tokens. Capabilities include function calling. Route Qwen3 235B A22B Instruct 2507 Maas via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: litellm Last verified: Oct 22, 2026 View source ↗
Cost calculator

Estimate Qwen3 235B A22B Instruct 2507 Maas spend

Pick a workload, fine-tune the sliders, and see the monthly bill.

~3K in / ~400 out · 5K req/day
3,000
0262,144
400
016,384
5,000
01,000,000
Per request
$0.001012
in $0.000660 · out $0.000352
Per day
$5.06
5,000 requests
Per month
$154
152,188 requests

Estimate uses $0.2200/M input · $0.8800/M output. Provider pricing changes. Production costs vary with retries, streaming overhead, and tool-call rounds.
Want this for free? Cache + route via Agent Command Center — first 100K requests and 100K cache hits free every month.

Pricing

Per-token rates, expressed in USD per 1M tokens. Verified Oct 22, 2026.

Input $0.220/M
Output $0.880/M
Blended (3:1 in:out) $0.385/M
Cross-model comparable price
Batch input $0.110/M
Batch output $0.440/M

Context & limits

Context window
262,144 tokens
Max input
262,144 tokens
Max output
16,384 tokens
Modalities
text

Capabilities

  • Function calling ✓ supported
  • Parallel tool calls — not advertised
  • Vision input — not advertised
  • Audio input — not advertised
  • Audio output — not advertised
  • PDF input — not advertised
  • Streaming ✓ supported
  • Structured output — not advertised
  • Prompt caching — not advertised
  • Reasoning — not advertised

Strengths & caveats — when to pick Qwen3 235B A22B Instruct 2507 Maas

Data-driven from Qwen3 235B A22B Instruct 2507 Maas's percentile within chat peers.

Where it's strong

  • +agentic workflows that depend on reliable tool calls

Watch out for

  • !strict structured output — no JSON-schema enforcement, expect retry loops
  • !already deprecated — provider stopped accepting new traffic 14 days ago

Benchmarks pending

We haven't logged public benchmark scores for Qwen3 235B A22B Instruct 2507 Maas yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Qwen3 235B A22B Instruct 2507 Maas via Agent Command Center

One OpenAI-compatible endpoint. Routing, fallback, semantic caching, guardrails, and cost tracking come along for the ride. First 100K requests + 100K cache hits free every month.

SDK
Native Future AGI client (agentcc / @agentcc/client). Per-call metadata — provider, cost, latency, cache hit, request id — is returned on x-agentcc-* response headers, so any HTTP client can read it.
# Qwen3 235B A22B Instruct 2507 Maas via the Agent Command Center Python SDK
# pip install agentcc
import os
from agentcc import AgentCC

client = AgentCC(
    api_key=os.environ["AGENTCC_API_KEY"],   # from app.futureagi.com → Settings → API Keys
    base_url="https://gateway.futureagi.com/v1",
)

resp = client.chat.completions.create(
    model="vertex-ai/qwen-qwen3-235b-a22b-instruct-2507-maas",
    messages=[{"role": "user", "content": "Hello, Qwen3 235B A22B Instruct 2507 Maas!"}],
)

print(resp.choices[0].message.content)
print(f"Tokens: {resp.usage.total_tokens}")

# Per-call gateway metadata is returned on x-agentcc-* response headers.
# When you need it programmatically, use .with_raw_response to get them:
raw = client.chat.completions.with_raw_response.create(
    model="vertex-ai/qwen-qwen3-235b-a22b-instruct-2507-maas",
    messages=[{"role": "user", "content": "Same call, but I want the headers."}],
)
print("Provider:", raw.headers.get("x-agentcc-provider"))
print("Latency:", raw.headers.get("x-agentcc-latency-ms"), "ms")
print("Cost:   ", raw.headers.get("x-agentcc-cost"), "USD")
print("Cache:  ", raw.headers.get("x-agentcc-cache"))
Set AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗

Explore similar models

Qwen3 235B A22B Instruct 2507 Maas doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark. Side-by-side links appear where a comparison page exists; otherwise, open the model profile.

FAQ

How much does Qwen3 235B A22B Instruct 2507 Maas cost?

Input is priced at $0.220 per 1M tokens and output at $0.880 per 1M tokens (Google Vertex AI, last verified Oct 22, 2026).

What is the context window of Qwen3 235B A22B Instruct 2507 Maas?

Qwen3 235B A22B Instruct 2507 Maas supports a 262,144-token context window with up to 16,384 output tokens.

Does Qwen3 235B A22B Instruct 2507 Maas support function calling?

Yes — Qwen3 235B A22B Instruct 2507 Maas supports function (tool) calling.

Is Qwen3 235B A22B Instruct 2507 Maas good for production?

Qwen3 235B A22B Instruct 2507 Maas is well-suited for agentic workflows that depend on reliable tool calls. Consider alternatives if you need strict structured output — no JSON-schema enforcement, expect retry loops.

How can I route to Qwen3 235B A22B Instruct 2507 Maas with fallback?

Use Agent Command Center: a single OpenAI-compatible endpoint that supports cost-optimized routing, latency-aware retries, model fallback, and shadow traffic. Configure once, swap models without app changes.

Useful links for Qwen3 235B A22B Instruct 2507 Maas

Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.