Model profile
qwen

Qwen: QwQ 32B

Qwen: QwQ 32B is a budget text-first model from qwen with a balanced runtime profile, standard context posture, and the clearest fit around coding / long-context research.

Best for: Coding / Long-context researchBalanced latencyStandard contextBudget pricing
Intelligence
19.7

Benchmark blend

Coding
0.631

Dev workflow signal

Context
33K Tokens

Standard

Input Price
$0.43

Budget tier

Decision snapshot
49

Qwen: QwQ 32B currently reads as a budget text-first option with standard context and a balanced runtime profile.

Overall profile
Use-case specific
Best for
Coding / Long-context research
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
19.7
20

General reasoning and benchmark headroom.

Limited
Speed
33 tok/s
60

TTFT 0.45s

Competitive
Context
33K Tokens
48

How much prompt and task state can stay in view.

Situational
Price
$0.43
86

$0.60 output / 1M

Efficient

Editorial Profile

Qwen: QwQ 32B 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 63Math score 29

QwQ is the reasoning model of the Qwen series. Compared with conventional instruction-tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems. QwQ-32B is the medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini.

Identity

qwen text-first profile

Positioning

Coding / Long-context research with standard context and balanced 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 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
  • Code generation, refactors, test writing, and tool-assisted debugging.

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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
19.7
MMLU Pro
76.4%
GPQA
59.3%
HLE
8.2%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.631
SciCode
35.8%
Math

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

Math Index
29.0
AIME
78.0%
AIME 2025
29.0%
Math 500
95.7%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
38.8%
LCR
25.0%

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
33K Tokens
Vision
Text-first
Modalities
text->text, text
Tokenizer
Qwen
Max Completion
32768
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoninglogprobsmax_tokenspresence_penaltyreasoningresponse_formatstopstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.43
Output
per 1M output tokens
$0.60
Blended
AA 3:1 mix
$0.47

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.