Medlm Medium
Google Vertex AI chatMedlm Medium is a Google Vertex AI chat model.It supports a 32,768-token context windowwith up to 8,192 output tokens. Route Medlm Medium via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
We don't have verified per-token pricing for Medlm Medium yet. If you have a source from Google Vertex AI's documentation, help us add it — your submission gets reviewed within 48 hours.
Pricing
Per-token rates, expressed in USD per 1M tokens. Verified May 12, 2026.
| Input | — | |
| Output | — |
Limits
- Context window
- 32,768 tokens
- Max input
- 32,768 tokens
- Max output
- 8,192 tokens
- Modalities
- text
Capabilities
- Function calling — not advertised
- 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
Where it's strong
Watch out for
- !limited context — 32,768-token window is in the bottom quartile; not ideal for long documents or large RAG
- !agentic workflows — no advertised function-calling; use a tool-capable model and route via Agent Command Center for fallback
- !strict structured output — no JSON-schema enforcement, expect retry loops
Benchmarks pending
We haven't logged public benchmark scores for Medlm Medium yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Medlm Medium 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.
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.# Medlm Medium 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/medlm-medium",
messages=[{"role": "user", "content": "Hello, Medlm Medium!"}],
)
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/medlm-medium",
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"))AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗Compare with similar models
Medlm Medium doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark.
- Claude Opus 4.7Google Vertex AI · $5.00/M in · $25.00/M out · 1,000,000 ctx
- Claude Opus 4.6Google Vertex AI · $5.00/M in · $25.00/M out · 1,000,000 ctx
- Gemini 3.1 Pro previewGoogle Vertex AI · $2.00/M in · $12.00/M out · 1,048,576 ctx
- Gemini 3 Pro PreviewGoogle Vertex AI · $2.00/M in · $12.00/M out · 1,048,576 ctx
FAQ
How much does Medlm Medium cost?
Public per-token pricing for Medlm Medium is not yet published. Submit a source on this page to help us add it.
What is the context window of Medlm Medium?
Medlm Medium supports a 32,768-token context window with up to 8,192 output tokens.
Does Medlm Medium support function calling?
Medlm Medium does not currently advertise function-calling support. For agentic workloads, prefer a tool-calling-capable model and route via Agent Command Center for fallback.
Is Medlm Medium good for production?
Medlm Medium is best evaluated against your own production traces. Pipe traffic through Agent Command Center to compare it head-to-head against alternatives in shadow mode.
How can I route to Medlm Medium 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 Medlm Medium
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.