Model profile
Alibaba

Qwen: Qwen3 14B

Qwen: Qwen3 14B is a budget-priced text-first model from Alibaba with partial runtime data, extended context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsN/A latencyExtended contextBudget pricing
Intelligence
10.4

Benchmark blend

Coding
13.8

Dev workflow signal

Context
131K Tokens

Extended

Input Price
$0.35

Budget tier

Decision snapshot
39

Qwen: Qwen3 14B currently reads as a budget text-first option with extended context and a partially published runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Agent workflows
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
10.4
10

General reasoning and benchmark headroom.

Limited
Speed
N/A
N/A

Latency data is partial.

Unavailable
Context
131K Tokens
76

How much prompt and task state can stay in view.

Competitive
Price
$0.35
86

$4.20 output / 1M

Efficient

Editorial Profile

Qwen: Qwen3 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 14Math score 56

Qwen3-14B is a dense 14.8B parameter causal language model from the Qwen3 series, designed for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for...

Identity

Alibaba text-first profile

Positioning

Long-context research / Agent workflows with extended context and partially published runtime.

Cost posture

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

Strengths
  • Large context headroom supports repo-wide prompts and long research sessions.

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.

Best fit
  • Long-context summarization, repo analysis, and policy or document review.

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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
10.4
MMLU Pro
77.4%
GPQA
60.4%
HLE
4.5%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
13.8
LiveCodeBench
0.523
SciCode
31.6%
Math

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

Math Index
55.7
AIME
76.3%
AIME 2025
55.7%
Math 500
96.1%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
40.5%
TAU2
34.5%
TerminalBench Hard
3.8%
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
131K Tokens
Vision
Text-first
Modalities
text
Tokenizer
Qwen3
Max Completion
16384
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoninglogit_biaslogprobsmax_tokensmin_ppresence_penaltyreasoningrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.35
Output
per 1M output tokens
$4.20
Blended
AA 3:1 mix
$1.31

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.