Model profile
Allen Institute for AI
New in 2026

AllenAI: Olmo 3.1 32B Instruct

AllenAI: Olmo 3.1 32B Instruct is a budget-priced text-first model from Allen Institute for AI with balanced runtime profile, standard context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsBalanced latencyStandard contextBudget pricing
Intelligence
12.2

Benchmark blend

Coding
5.6

Dev workflow signal

Context
66K Tokens

Standard

Input Price
$0.20

Budget tier

Decision snapshot
41

AllenAI: Olmo 3.1 32B Instruct currently reads as a budget text-first option with standard context and a balanced runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Agent workflows
Latency tier
Balanced
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
12.2
12

General reasoning and benchmark headroom.

Limited
Speed
54 tok/s
69

TTFT 0.24s

Competitive
Context
66K Tokens
64

How much prompt and task state can stay in view.

Competitive
Price
$0.20
86

$0.60 output / 1M

Efficient

Editorial Profile

AllenAI: Olmo 3.1 32B 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 6Math score N/A

Olmo 3.1 32B Instruct is a large-scale, 32-billion-parameter instruction-tuned language model engineered for high-performance conversational AI, multi-turn dialogue, and practical instruction following. As part of the Olmo 3.1 family, this...

Identity

Allen Institute for AI text-first profile

Positioning

Long-context research / Agent workflows with standard context and balanced 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.

  • Latency is balanced rather than ultra-fast, which is fine for most workflows but not the snappiest tier.

  • 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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Intelligence
12.1
Context
66K Tokens
Input Price
$0.00

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
12.2
GPQA
53.9%
HLE
4.9%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
5.6
SciCode
16.7%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
39.2%
TAU2
21.3%
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
Tokenizer
Other
Moderation
No
Supported Parameters
frequency_penaltymax_tokensmin_ppresence_penaltyrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.20
Output
per 1M output tokens
$0.60
Blended
AA 3:1 mix
$0.30

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.