LLM Reference

OLMo 7B

Released
2024-02-01
Last refreshed
2026-06-15
Status
Researched 136d ago
Open sourceCommercial use: permittedClassificationJSON / Tool use

OLMo 7B is worth evaluating for classification and json / tool use when its provider route and context window match the workload.

Use it for

  • Teams evaluating classification and json / tool use
  • Buyers comparing 1 tracked provider route

Do not use it for

  • Vision or document-understanding workloads
Specifications
Family
OLMo
Released
2024-02-01
Parameters
7B
Architecture
Decoder Only
Knowledge cutoff
2023-03
Specialization
general
Openness
Open source
License
Apache 2.0OSI-approvedCommercial use: permitted
Weights
Unknown
Code
Unknown
Training
Fine-tuned
Created by

Advocating for open science and source

Seattle, Washington, United States
Founded 2014
Website
Pricing
Output / 1M
-
Input / 1M
-

Cheapest of 2 routes · Replicate API

About

OLMo 7B is a large language model created by the Allen Institute for Artificial Intelligence (AI2), characterized by its open-source nature where model weights, training data, code, and evaluation tools have been publicly released. It utilizes a decoder-only transformer architecture, featuring 32 layers, a hidden size of 4096, and 32 attention heads, among other features. Trained on 2.5 trillion tokens from the Dolma dataset, this model excels in text generation, question answering, and language understanding, with performance metrics often comparable to or exceeding those of similar-sized models.

Top use-case fit

Classification

1 relevant benchmark in the decision map.

JSON / Tool use

Included by capability and metadata signals in the decision map.

Provider price ladder

Compare all 2

Compare API pricing across 1 providers for input and output tokens, batch, and cached reads when available.

ProviderInput / 1MOutput / 1MRoute
Replicate API--
ServerlessPartial

Available via routers & gateways(1)

Capabilities

Structured Outputs

Benchmark peer barsfor Classification

Benchmark scores(1)

Scores are benchmark-specific and are direction-aware: the same numeric gap can mean very different outcomes across suites. Use the leaderboard context and this model's provider route to decide whether the winning margin is meaningful for your workload.
BenchmarkScoreVersionEvaluationSource
Massive Multitask Language Understanding62.35-shotObserved 2026-03-07Source

Migration checks

No linked migration route is available for this model yet.