Allenai Olmo 3.7B Think
PublicAI chatAllenai Olmo 3.7B Think is a PublicAI chat model.It supports a 32,768-token context windowwith up to 4,096 output tokens. Capabilities include function calling, reasoning. Route Allenai Olmo 3.7B Think 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 Allenai Olmo 3.7B Think yet. If you have a source from PublicAI'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
- 4,096 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 ✓ supported
Where it's strong
- +multi-step reasoning and analysis tasks
- +agentic workflows that depend on reliable tool calls
Watch out for
- !limited context — 32,768-token window is in the bottom quartile; not ideal for long documents or large RAG
- !strict structured output — no JSON-schema enforcement, expect retry loops
Benchmarks pending
We haven't logged public benchmark scores for Allenai Olmo 3.7B Think yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Allenai Olmo 3.7B Think 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.# Allenai Olmo 3.7B Think 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="publicai/allenai-olmo-3-7b-think",
messages=[{"role": "user", "content": "Hello, Allenai Olmo 3.7B Think!"}],
)
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="publicai/allenai-olmo-3-7b-think",
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 ↗FAQ
How much does Allenai Olmo 3.7B Think cost?
Public per-token pricing for Allenai Olmo 3.7B Think is not yet published. Submit a source on this page to help us add it.
What is the context window of Allenai Olmo 3.7B Think?
Allenai Olmo 3.7B Think supports a 32,768-token context window with up to 4,096 output tokens.
Does Allenai Olmo 3.7B Think support function calling?
Yes — Allenai Olmo 3.7B Think supports function (tool) calling.
Is Allenai Olmo 3.7B Think good for production?
Allenai Olmo 3.7B Think is well-suited for multi-step reasoning and analysis tasks and agentic workflows that depend on reliable tool calls. Consider alternatives if you need limited context — 32,768-token window is in the bottom quartile; not ideal for long documents or large RAG.
How can I route to Allenai Olmo 3.7B Think 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 Allenai Olmo 3.7B Think
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.