Model profile
qwen

Qwen: Qwen2.5 Coder 7B Instruct

Qwen: Qwen2.5 Coder 7B Instruct is a budget text-first model from qwen with a heavy runtime profile, standard context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsHeavy latencyStandard contextBudget pricing
Intelligence
10.0

Benchmark blend

Coding
0.126

Dev workflow signal

Context
33K Tokens

Standard

Input Price
$0.00

Budget tier

Decision snapshot
35

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

Overall profile
Use-case specific
Best for
Long-context research / Agent workflows
Latency tier
Heavy
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
10.0
10

General reasoning and benchmark headroom.

Limited
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
$0.00
86

$0.00 output / 1M

Efficient

Editorial Profile

Qwen: Qwen2.5 Coder 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 13Math score 66

Qwen2.5-Coder-7B-Instruct is a 7B parameter instruction-tuned language model optimized for code-related tasks such as code generation, reasoning, and bug fixing. Based on the Qwen2.5 architecture, it incorporates enhancements like RoPE, SwiGLU, RMSNorm, and GQA attention with support for up to 128K tokens using YaRN-based extrapolation. It is trained on a large corpus of source code, synthetic data, and text-code grounding, providing robust performance across programming languages and agentic coding workflows. This model is part of the Qwen2.5-Coder family and offers strong compatibility with tools like vLLM for efficient deployment. Released under the Apache 2.0 license.

Identity

qwen text-first profile

Positioning

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

  • 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
10.0
MMLU Pro
47.3%
GPQA
33.9%
HLE
4.8%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.126
SciCode
14.8%
Math

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

AIME
5.3%
Math 500
66.0%

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
Moderation
No
Supported Parameters
frequency_penaltymax_tokenspresence_penaltyrepetition_penaltyresponse_formatstructured_outputstemperaturetop_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.