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Model profile
Unknown

Apriel-v1.5-15B-Thinker

Apriel-v1.5-15B-Thinker is a budget text-first model from Unknown with a fast runtime profile, unknown context posture, and the clearest fit around agent workflows / long-context research.

Best for: Agent workflows / Long-context researchFast latencyUnknown contextBudget pricing
Intelligence
28.3

Benchmark blend

Coding
18.7

Dev workflow signal

Context
N/A

Unknown

Input Price
$0.00

Budget tier

Decision snapshot
50

Apriel-v1.5-15B-Thinker currently reads as a budget text-first option with unknown context and a fast runtime profile.

Overall profile
Use-case specific
Best for
Agent workflows / Long-context research
Latency tier
Fast
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis
Compare this modelSee leaderboard

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
28.3
28

General reasoning and benchmark headroom.

Limited
Speed
149 tok/s
100

TTFT 0.17s

Above average
Context
N/A
34

How much prompt and task state can stay in view.

Limited
Price
$0.00
86

$0.00 output / 1M

Efficient

Editorial Profile

Apriel-v1.5-15B-Thinker 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 19Math score 88

The Apriel-v1.5-15B-Thinker AI model by Unknown.

Identity

Unknown text-first profile

Positioning

Agent workflows / Long-context research with unknown 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.

Best fit

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

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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
28.3
MMLU Pro
77.3%
GPQA
71.3%
HLE
12.0%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
18.7
LiveCodeBench
0.728
SciCode
34.8%
Math

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

Math Index
87.5
AIME 2025
87.5%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
61.7%
TAU2
68.4%
TerminalBench Hard
10.6%
LCR
20.0%

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
N/A
Vision
Text-first
Price architecture
Input
per 1M input tokens
$0.00
Output
per 1M output tokens
$0.00
Blended
AA 3:1 mix
$0.00

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.