LLM Reference

Orca 2 7B

Released
2023-11-21
Last refreshed
2026-05-19
Status
Researched 106d ago
Open weightsCommercial use: non-commercialCodingClassification

Orca 2 7B is worth evaluating for coding and classification when its provider route and context window match the workload.

Use it for

  • Teams evaluating coding and classification
  • Workloads that can use a 4k context window
  • Buyers comparing 1 tracked provider route

Do not use it for

  • Vision or document-understanding workloads
  • Strict JSON or tool-calling flows
Specifications
Family
Orca 2
Released
2023-11-21
Context
4k
Parameters
7B
Architecture
Decoder Only
Specialization
general
Openness
Open weights
License
Microsoft Research(needs verification)Commercial use: non-commercial
Weights
Unknown
Code
Unknown
Training
Fine-tuned
Created by

Advancing the state-of-the-art in AI and computing.

Redmond, Washington, United States
Founded 1991
Website
Pricing
Output / 1M
$0.670
Input / 1M
$0.520

Cheapest of 1 route · Microsoft Foundry

About

Orca 2 7B is a large language model developed by Microsoft, focusing on reasoning tasks and providing precise single-turn responses. It is a fine-tuned version of the LLaMA-2 architecture, trained on a synthetic dataset with enhanced reasoning capabilities, moderated by Microsoft Azure content filters. While adept at handling reasoning over user-provided data, reading comprehension, math problem-solving, and text summarization, it is not optimized for chat applications without further fine-tuning. Orca 2 shows strong performance in zero-shot settings but shares some LLMs' common limitations, including biases and the potential for generating misleading content.

Top use-case fit: coding, agents, and build tasks

Coding

Q/$ C

1 relevant benchmark in the decision map.

Classification

Q/$ C

1 relevant benchmark in the decision map.

Capabilities

No model capability flags are currently sourced.

Benchmark peer barsfor Coding

Benchmark scores(2)

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
HumanEval28.4pass@1Observed 2026-03-06Source
Massive Multitask Language Understanding66.55-shotObserved 2026-03-06Source

Migration checks

No linked migration route is available for this model yet.