Model profile
Alibaba

Qwen: QwQ 32B

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

Best for: Long-context research / CodingBalanced latencyExtended contextBudget pricing
Intelligence
19.7

Benchmark blend

Coding
0.631

Dev workflow signal

Context
131K Tokens

Extended

Input Price
$0.66

Budget tier

Decision snapshot
54

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

Overall profile
Use-case specific
Best for
Long-context research / Coding
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
32 tok/s
60

TTFT 0.43s

Competitive
Context
131K Tokens
76

How much prompt and task state can stay in view.

Competitive
Price
$0.66
86

$1.00 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,...

Identity

Alibaba text-first profile

Positioning

Long-context research / Coding with extended context and balanced 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 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.

Best fit
  • Code generation, refactors, test writing, and tool-assisted debugging.

  • 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
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
131K Tokens
Vision
Text-first
Modalities
text
Tokenizer
Qwen
Max Completion
131072
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoningreasoningtemperaturetool_choicetoolstop_ktop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.66
Output
per 1M output tokens
$1.00
Blended
AA 3:1 mix
$0.74

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.