Model profile
Alibaba

Qwen2.5 Coder 32B Instruct

Qwen2.5 Coder 32B Instruct is a budget-priced text-first model from Alibaba with partial runtime data, standard context posture, and the clearest fit around long-context research / coding.

Best for: Long-context research / CodingN/A latencyStandard contextBudget pricing
Intelligence
12.9

Benchmark blend

Coding
0.295

Dev workflow signal

Context
33K Tokens

Standard

Input Price
$0.00

Budget tier

Decision snapshot
37

Qwen2.5 Coder 32B Instruct currently reads as a budget text-first option with standard context and a partially published runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Coding
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
12.9
13

General reasoning and benchmark headroom.

Limited
Speed
N/A
N/A

Latency data is partial.

Unavailable
Context
33K Tokens
48

How much prompt and task state can stay in view.

Situational
Price
$0.00
86

$0.00 output / 1M

Efficient

Editorial Profile

Qwen2.5 Coder 32B Instruct 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 30Math score 77

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in **code generation**, **code reasoning**...

Identity

Alibaba text-first profile

Positioning

Long-context research / Coding with standard 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 window is more comfortable for focused tasks than extremely long sessions.

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
12.9
MMLU Pro
63.5%
GPQA
41.7%
HLE
3.8%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.295
SciCode
27.1%
Math

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

AIME
12.0%
Math 500
76.7%

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
Tokenizer
Qwen
Moderation
No
Supported Parameters
frequency_penaltymax_tokenspresence_penaltyrepetition_penaltyseedtemperaturetop_ktop_p
Input Modalities
text
Output Modalities
text
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.