Olmo 3 7B Instruct

Allen AI chattool_use

API ID: allenai/olmo-3-7b-instruct-20251121

Input Price
$0.10
/1M tokens
Output Price
$0.20
/1M tokens

About Olmo 3 7B Instruct

OLMo is Allen Institute for AI's open language model, designed for research transparency and reproducibility. The model is fully open-source including training data, code, and weights, enabling unprecedented research access. OLMo excels at general language tasks while providing complete visibility into its development. It's particularly valuable for AI research, academic study, and applications requiring full model transparency. The model demonstrates that capable AI can be developed openly. For researchers and developers seeking fully transparent AI, OLMo offers unique openness that proprietary models can't match.

๐Ÿ’ฐ
Price Ranking
#628 lowest price among 950 Chat models

Model Specifications

Context Length
66k
Max Output
66k
Release Date
2025-11-21
Capabilities
chat tool_use
Input Modalities
text
Output Modalities
text

Best For

  • Conversations, content writing, general assistance

Consider Alternatives For

  • Image understanding (needs vision capability)

๐Ÿ’ฐ Real-World Cost Examples

Estimated monthly costs for common use cases

Personal AI Assistant
$0.10
/month
50 conversations/day, ~500 tokens each
Customer Service Bot
$3.30
/month
1000 tickets/day, ~800 tokens each

Allen AI Model Lineup

Compare all models from Allen AI to find the best fit

Model Input Output Context Capabilities
Olmo 3 7B Instruct Current $0.10 $0.20 66k chat tool_use
Llama 3.1 Tulu 3 405B Free Free - chat
Llama 3.1 Tulu 3 405B Free Free - chat
OLMo 7B Instruct Free Free 2k chat
OLMo 7B Instruct Free Free 2k chat
Olmo 2 32B Instruct $0.05 $0.20 128k chat tool_use

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๐Ÿš€ Quick Start

Get started with Olmo 3 7B Instruct API

OpenAI-compatible SDK
from openai import OpenAI

client = OpenAI(
    base_url="https://api.provider.com/v1",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="allenai/olmo-3-7b-instruct-20251121",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)
print(response.choices[0].message.content)