Model profile
qwen

Qwen: Qwen2.5-VL 7B Instruct

Qwen: Qwen2.5-VL 7B Instruct is a budget multimodal generalist from qwen with a heavy runtime profile, standard context posture, and the clearest fit around multimodal / long-context research.

Best for: Multimodal / Long-context researchHeavy latencyStandard contextBudget pricing
Intelligence
N/A

Benchmark blend

Coding
N/A

Dev workflow signal

Context
33K Tokens

Standard

Input Price
$0.20

Budget tier

Decision snapshot
51

Qwen: Qwen2.5-VL 7B Instruct currently reads as a budget multimodal option with standard context and a heavy runtime profile.

Overall profile
Use-case specific
Best for
Multimodal / Long-context research
Latency tier
Heavy
Price tier
Budget
Source coverage
OpenRouterVision signal

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
44

General reasoning and benchmark headroom.

Situational
Speed
N/A
46

Latency data is partial.

Situational
Context
33K Tokens
48

How much prompt and task state can stay in view.

Situational
Price
$0.20
86

$0.20 output / 1M

Efficient

Editorial Profile

Qwen: Qwen2.5-VL 7B 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 40Math score 36Vision enabled

Qwen2.5 VL 7B is a multimodal LLM from the Qwen Team with the following key enhancements: - SoTA understanding of images of various resolution & ratio: Qwen2.5-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc. - Understanding videos of 20min+: Qwen2.5-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc. - Agent that can operate your mobiles, robots, etc.: with the abilities of complex reasoning and decision making, Qwen2.5-VL can be integrated with devices like mobile phones, robots, etc., for automatic operation based on visual environment and text instructions. - Multilingual Support: to serve global users, besides English and Chinese, Qwen2.5-VL now supports the understanding of texts in different languages inside images, including most European languages, Japanese, Korean, Arabic, Vietnamese, etc. For more details, see this [blog post](https://qwenlm.github.io/blog/qwen2-vl/) and [GitHub repo](https://github.com/QwenLM/Qwen2-VL). Usage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).

Identity

qwen multimodal profile

Positioning

Multimodal / Long-context research with standard context and heavy 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 profile is better for deliberate runs than rapid back-and-forth chat.

  • 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
text, image->text, image
Tokenizer
Qwen
Moderation
No
Supported Parameters
frequency_penaltylogit_biasmax_tokensmin_ppresence_penaltyrepetition_penaltyseedstoptemperaturetop_ktop_p
Input Modalities
textimage
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.20
Output
per 1M output tokens
$0.20
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.