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Open weights model

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Released November 2025

Olmo 3 7B Think Intelligence, Performance & Price Analysis

Name: Intelligence
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

API Provider Benchmarks

Intelligence

Artificial Analysis Intelligence Index

Speed

N/A

Output tokens per second

Input PriceUpdated

$0.00

USD per 1M tokens

Cache: $0.00 (NaN%)

Output Price

$0.00

USD per 1M tokens

Verbosity

N/A

Output tokens from Intelligence Index

Olmo 3 7B Think is among the least intelligent models, but well priced when comparing to other open weight models of similar size. The model supports text input, outputs text, and has a 66k tokens context window with knowledge up to December 2024.

Olmo 3 7B Think scores 9 on the Artificial Analysis Intelligence Index, placing it at the lower end among comparable models (averaging 15).

Pricing for Olmo 3 7B Think is $0.00 per 1M input tokens (competitively priced, average: $0.05) and $0.00 per 1M output tokens (competitively priced, average: $0.15).

Reasoning	Yes This page shows the reasoning version of this model. A non-reasoning variant may also exist.
Input modality	Supports: text
Output modality	Supports: text
Knowledge cutoff	Dec 1, 2024
Context window	66k ~98 A4 pages of size 12 Arial font
Total parameters	7B
License	Apache 2.0
Model weights	Hugging Face

Metrics are compared against models of the same class:

Non-reasoning models → compared only with other non-reasoning models
Reasoning models → compared across both reasoning and non-reasoning
Open weights models → compared only with other open weights models of the same size class:

Tiny: ≤4B parameters
Small: 4B–40B parameters
Medium: 40B–150B parameters
Large: >150B parameters

Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:

<$0.15 per 1M tokens
$0.15–$1 per 1M tokens
>$1 per 1M tokens

Highlights

Intelligence

Artificial Analysis Intelligence Index · Higher is better

Speed

Output tokens per second · Higher is better

Updated

Price

USD per 1M tokens (blended) · Lower is better

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.0 incorporates 10 evaluations: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt

Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Reasoning models are indicated by a lightbulb icon

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if the weights are available but commercial use is limited (typically requires obtaining a paid license).

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

Results claimed by AI Lab (not yet independently verified)

GDPval-AA

Agentic real-world work tasks, (ELO-500)/2000

Terminal-Bench Hard

Agentic coding & terminal use

𝜏²-Bench Telecom

Agentic tool use

AA-LCR

Long context reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

Humanity's Last Exam

Reasoning & knowledge

GPQA Diamond

Scientific reasoning

SciCode

Coding

IFBench

Instruction following

CritPt

Physics reasoning

APEX-Agents-AA

Long-horizon agentic tasks

ITBench-AA

Kubernetes incident root-cause analysis

No data available

MMMU-Pro

Visual reasoning

Reasoning models are indicated by a lightbulb icon.

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

Openness

Artificial Analysis Openness Index: Results

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)

Reasoning models are indicated by a lightbulb icon

Intelligence Index Comparisons

Intelligence vs. Price

Blended at 7:2:1 (cache-input-output) · USD per 1M tokens (blended)

Most attractive quadrant

Alibaba

Anthropic

DeepSeek

Google

Kimi

NVIDIA

OpenAI

xAI

Xiaomi

Z AI

Reasoning models are indicated by a lightbulb icon.

While higher intelligence models are typically more expensive, they do not all follow the same price-quality curve.

Price per token, shown in USD per million tokens. Price is a blend of cache hit, input, and output token prices using the selected ratio (default 7:2:1 cache-input-output).

The blended cache price shown here uses cache hit price only. Other caching costs differ by provider:

Anthropic: charges a separate cache write fee, with different rates for 5-minute and 1-hour TTLs (1-hour TTL is more expensive). Blended price charts use Anthropic cache write price for the input leg.
Google (Vertex/Gemini): charges a per-hour cache storage fee in addition to cache hit pricing. Some providers also use tiered pricing for prompts above 200K tokens.
OpenAI, DeepSeek, others: typically charge only cache hit pricing with no write or storage fee.

See Prompt Caching for the full breakdown.

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Intelligence Index Token Use & Cost

Output Tokens Used to Run Artificial Analysis Intelligence Index

Tokens used to run all evaluations in the Artificial Analysis Intelligence Index

Reasoning models are indicated by a lightbulb icon

The number of tokens required to run all evaluations in the Artificial Analysis Intelligence Index (excluding repeats).

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index

Reasoning models are indicated by a lightbulb icon

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input and output token pricing and the number of tokens used across evaluations (excluding repeats).

Context Window

Context window: tokens limit · Higher is better

Reasoning models are indicated by a lightbulb icon

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

PricingUpdated

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens)

Reasoning models are indicated by a lightbulb icon

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Price per token included in the request/message sent to the API, represented as USD per million Tokens.

The blended cache price shown here uses cache hit price only. Other caching costs differ by provider:

Anthropic: charges a separate cache write fee, with different rates for 5-minute and 1-hour TTLs (1-hour TTL is more expensive). Blended price charts use Anthropic cache write price for the input leg.
Google (Vertex/Gemini): charges a per-hour cache storage fee in addition to cache hit pricing. Some providers also use tiered pricing for prompts above 200K tokens.
OpenAI, DeepSeek, others: typically charge only cache hit pricing with no write or storage fee.

See Prompt Caching for the full breakdown.

Price per token generated by the model (received from the API), represented as USD per million Tokens.

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Speed

Measured by Output Speed (tokens per second)

Output Speed

Output tokens per second · Higher is better

Reasoning models are indicated by a lightbulb icon

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Output Speed vs. Price

Output speed: output tokens per second · USD per 1M tokens (blended)

Most attractive quadrant

Alibaba

Anthropic

DeepSeek

Google

Kimi

NVIDIA

OpenAI

xAI

Xiaomi

Z AI

Reasoning models are indicated by a lightbulb icon.

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Price per token, shown in USD per million tokens. Price is a blend of cache hit, input, and output token prices using the selected ratio (default 7:2:1 cache-input-output).

Latency

Measured by Time (seconds) to First Token

Latency: Time To First Answer Token

Seconds to first answer token received · Accounts for reasoning model 'thinking' time

Reasoning models are indicated by a lightbulb icon

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

End-to-End Response Time

Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed

End-to-End Response Time

Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better

Reasoning models are indicated by a lightbulb icon

Seconds to receive a 500 token response. Key components:

Input time: Time to receive the first response token
Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
Answer time: Time to generate 500 output tokens, based on output speed

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference

Reasoning models are indicated by a lightbulb icon

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

The number of parameters actually executed during each inference forward pass, expressed in billions. For Mixture of Experts (MoE) models, a routing mechanism selects a subset of experts per token, resulting in fewer active than total parameters. Dense models use all parameters, so active equals total.

Frequently Asked Questions

Common questions about Olmo 3 7B Think

Olmo 3 7B Think was released on November 20, 2025.

Olmo 3 7B Think was created by Allen Institute for AI.

Olmo 3 7B Think scores 9 on the Artificial Analysis Intelligence Index, placing it at the lower end among other open weight models of similar size (median: 15).

Yes, Olmo 3 7B Think is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.

Olmo 3 7B Think supports text input.

Olmo 3 7B Think supports text output.

No, Olmo 3 7B Think does not support image input. It can only process text.

No, Olmo 3 7B Think is not multimodal. It only supports text input.

Olmo 3 7B Think has a context window of 66k tokens. This determines how much text and conversation history the model can process in a single request.

Yes, Olmo 3 7B Think is open weights. The model weights are publicly available and can be downloaded for self-hosting.

Olmo 3 7B Think has 7 billion parameters.

Olmo 3 7B Think is released under the Apache 2.0 license. This license allows commercial use. View license

Olmo 3 7B Think achieves a score of 9 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.

Olmo 3 7B Think has a knowledge cutoff of December 2024. The model's training data includes information up to this date.

Olmo 3 7B Think is an open weights model that can be self-hosted. View providers

Olmo 3 7B Think is an open weights model that can be downloaded and self-hosted. Compare providers

Olmo 3 7B Think Intelligence, Performance & Price Analysis

Model summary

Intelligence

Speed

Input PriceUpdated

Output Price

Verbosity

Comparison Summary

Technical specifications

125 models in this class

Intelligence

Speed

Price

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index

Open Weights

Intelligence Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

Openness

Artificial Analysis Openness Index: Results

Intelligence Index Comparisons

Intelligence vs. Price

Price-Quality Variance

Artificial Analysis Intelligence Index

Price

Cache Pricing by Provider

Model Performance Representation

Intelligence Index Token Use & Cost

Output Tokens Used to Run Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index Output Tokens

Cost to Run Artificial Analysis Intelligence Index

Cost to Run Artificial Analysis Intelligence Index

Context Window

Context Window

Context Window for RAG

Context Window

PricingUpdated

Pricing: Cache Hit, Input, and Output

Cache Hit

Input Price

Cache Pricing by Provider

Output Price

Model Performance Representation

Speed

Output Speed

Output Speed

Model Performance Representation

Output Speed vs. Price

Output Speed

Price

Latency

Latency: Time To First Answer Token

Time to First Answer Token

End-to-End Response Time

End-to-End Response Time

End-to-End Response Time

Model Performance Representation

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Total Parameters

Active Parameters at Inference Time

Frequently Asked Questions

When was Olmo 3 7B Think released?

Who created Olmo 3 7B Think?

How intelligent is Olmo 3 7B Think?

Is Olmo 3 7B Think a reasoning model?

What input modalities does Olmo 3 7B Think support?

What output modalities does Olmo 3 7B Think support?

Can Olmo 3 7B Think process images?

Is Olmo 3 7B Think multimodal?

What is the context window of Olmo 3 7B Think?

Is Olmo 3 7B Think open source?

How many parameters does Olmo 3 7B Think have?

What is the license for Olmo 3 7B Think?

How does Olmo 3 7B Think perform on benchmarks?

What is the knowledge cutoff date for Olmo 3 7B Think?