Model profile
DeepSeek

DeepSeek R1 Distill Qwen 14B

DeepSeek R1 Distill Qwen 14B is a budget-priced text-first model from DeepSeek with partial runtime data, partial context coverage, and the clearest fit around coding / reasoning.

Best for: Coding / ReasoningN/A latencyN/A contextBudget pricing
Intelligence
15.8

Benchmark blend

Coding
0.376

Dev workflow signal

Context
N/A

N/A

Input Price
$0.00

Budget tier

Decision snapshot
38

DeepSeek R1 Distill Qwen 14B currently reads as a budget text-first option with partially published context and a partially published runtime profile.

Overall profile
Use-case specific
Best for
Coding / Reasoning
Latency tier
N/A
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
15.8
16

General reasoning and benchmark headroom.

Limited
Speed
N/A
N/A

Latency data is partial.

Unavailable
Context
N/A
N/A

How much prompt and task state can stay in view.

Unavailable
Price
$0.00
86

$0.00 output / 1M

Efficient

Editorial Profile

DeepSeek R1 Distill Qwen 14B 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 38Math score 56

The DeepSeek R1 Distill Qwen 14B AI model by DeepSeek.

Identity

DeepSeek text-first profile

Positioning

Coding / Reasoning with partially published context and partially published runtime.

Cost posture

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

Strengths
  • The available source data suggests a balanced profile rather than one dominant edge.

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

  • Latency data is incomplete, so interactive responsiveness is harder to rank confidently.

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

  • Context limits are only partially published, so long-session planning needs extra validation.

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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Context
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Input Price
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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
15.8
MMLU Pro
74.0%
GPQA
48.4%
HLE
4.4%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.376
SciCode
23.9%
Math

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

Math Index
55.7
AIME
66.7%
AIME 2025
55.7%
Math 500
94.9%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
22.1%
LCR
7.0%

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.