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Model profile
Qwen

Qwen: Qwen2.5 VL 72B Instruct

Qwen: Qwen2.5 VL 72B Instruct is a budget-priced multimodal generalist from Qwen with partial runtime data, standard context posture, and the clearest fit around multimodal / long-context research.

Best for: Multimodal / Long-context researchN/A latencyStandard contextBudget pricing
Vision models
Intelligence
N/A

Benchmark blend

Coding
N/A

Dev workflow signal

Context
33K Tokens

Standard

Input Price
$0.80

Budget tier

Decision snapshot
67

Qwen: Qwen2.5 VL 72B Instruct currently reads as a budget multimodal option with standard context and a partially published runtime profile.

Overall profile
Selective fit
Best for
Multimodal / Long-context research
Latency tier
N/A
Price tier
Budget
Source coverage
OpenRouterVision signal
Open compare hubSee leaderboard

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
N/A
N/A

General reasoning and benchmark headroom.

Unavailable
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
$0.80
86

$0.80 output / 1M

Efficient

Editorial Profile

Qwen: Qwen2.5 VL 72B Instruct in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Selective fitCoding score N/AMath score N/AVision enabled

Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.

Identity

Qwen multimodal profile

Positioning

Multimodal / Long-context research 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

  • Vision-capable routing opens up multimodal review and extraction workflows.

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.

  • Context window is more comfortable for focused tasks than extremely long sessions.

Best fit

  • Image-grounded review, multimodal extraction, and UI audit workflows.

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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.

No benchmark data is available for this model yet.

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
Enabled
Modalities
image, text
Tokenizer
Qwen
Max Completion
32768
Moderation
No
Supported Parameters
frequency_penaltylogit_biasmax_tokenspresence_penaltyrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetop_ktop_p
Input Modalities
imagetext
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.80
Output
per 1M output tokens
$0.80
Blended
AA 3:1 mix
N/A

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.