Model profile
allenai

AllenAI: Olmo 3 7B Instruct

AllenAI: Olmo 3 7B Instruct is a budget text-first model from allenai with a fast runtime profile, standard context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalFast latencyStandard contextBudget pricing
Intelligence
8.2

Benchmark blend

Coding
3.4

Dev workflow signal

Context
66K Tokens

Standard

Input Price
$0.10

Budget tier

Decision snapshot
45

AllenAI: Olmo 3 7B Instruct currently reads as a budget text-first option with standard context and a fast runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Multimodal
Latency tier
Fast
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
8.2
8

General reasoning and benchmark headroom.

Limited
Speed
156 tok/s
100

TTFT 0.36s

Above average
Context
66K Tokens
64

How much prompt and task state can stay in view.

Competitive
Price
$0.10
86

$0.20 output / 1M

Efficient

Editorial Profile

AllenAI: Olmo 3 7B Instruct in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Use-case specificCoding score 3Math score 41

Olmo 3 7B Instruct is a supervised instruction-fine-tuned variant of the Olmo 3 7B base model, optimized for instruction-following, question-answering, and natural conversational dialogue. By leveraging high-quality instruction data and an open training pipeline, it delivers strong performance across everyday NLP tasks while remaining accessible and easy to integrate. Developed by Ai2 under the Apache 2.0 license, the model offers a transparent, community-friendly option for instruction-driven applications.

Identity

allenai text-first profile

Positioning

Long-context research / Multimodal with standard context and fast runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • Latency and throughput look responsive enough for interactive loops.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

  • Context window is more comfortable for focused tasks than extremely long sessions.

Best fit
  • Focused chat, retrieval-augmented flows, and narrower production tasks.

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Context
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Input Price
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AllenAI: Olmo 3.1 32B Instruct

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Context
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Intelligence
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Context
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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
8.2
MMLU Pro
52.2%
GPQA
40.0%
HLE
5.8%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
3.4
LiveCodeBench
0.266
SciCode
10.3%
Math

Formal reasoning, structured problem solving, and competition-style math.

Math Index
41.3
AIME 2025
41.3%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
32.8%
TAU2
12.6%
TerminalBench Hard
0.0%
LCR
0.0%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
66K Tokens
Vision
Text-first
Modalities
text->text, text
Tokenizer
Other
Max Completion
65536
Moderation
No
Supported Parameters
frequency_penaltylogit_biasmax_tokenspresence_penaltyrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetop_ktop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.10
Output
per 1M output tokens
$0.20
Blended
AA 3:1 mix
$0.13

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.