Model
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Qwen2-7B

OPEN
Alibaba · Qwen2 family · released Jun 7, 2024

Widely-used small Gen-2 dense model; Apache 2.0, 128K context on the Instruct variant.

ReasoningCodingVisionFunction callingTool useAgentic
485.5
Elo · rank #309
Parameters
7.07B
Active params
7.07B (dense)
Context
128K tokens
Architecture
Dense decoder-only Transformer (GQA)
License
Apache 2.0
Languages
30+
API price (in/out)
No hosted API
Modalities
text
Benchmark results
Bar shows position within the tracked field; marker = field best
ARC-ChallengeReasoning60.6%#87
best: Llama 3.1 405B · 96.9%
BIG-Bench HardReasoning62.6%#76
best: ERNIE 4.5 300B-A47B · 94.3%
C-EvalKnowledge83.2%#29
best: Qwen3.6-Plus · 93.3%
CMMLUKnowledge83.9%#7
best: Doubao-1.5-Pro · 90.9%
Gaokao MathMath35.1%#8
best: Doubao-Seed-1.6 · 96.0%
GPQA DiamondReasoning31.8%#255
best: GPT-5.6 · 94.6%
GSM8KMath79.9%#82
best: Llama 3.1 405B · 96.8%
HellaSwagReasoning80.7%#76
best: Claude 3 Opus · 95.4%
HumanEvalCoding51.2%#120
best: Claude Opus 4.5 · 99.4%
IFEvalReasoning54.7%#129
best: Gemma 4 26B A4B · 98.5%
KoMT-BenchHuman preference7.69#4
best: LG EXAONE 3.0 7.8B Instruct · 8.92
LiveCodeBenchCoding26.6%#157
best: DeepSeek-V4-Pro (Think Max) · 93.5%
LogicKorHuman preference6.3#25
best: GPT-4o · 9.33
MATH-500Math44.2%#143
best: GPT-5 · 99.4%
MBPPCoding65.9%#46
best: Llama-3.3-Nemotron-Super-49B v1 (Reasoning On) · 91.3%
MGSMMath57.0%#43
best: OpenAI o4-mini · 93.7%
MMLU (EU-21 languages)Knowledge58.4%#4
best: Llama 3.1 70B · 77.1%
MMLU-ProKnowledge40.0%#150
best: Claude Fable 5 · 91.5%
MMLUKnowledge70.3%#144
best: OpenAI o3 · 92.9%
MT-BenchHuman preference8.41#30
best: Hunyuan-Large (A52B) · 9.4
TruthfulQAKnowledge54.2%#40
best: Phi-3.5-MoE (16x3.8B, 6.6B active) · 77.5%
WinoGrandeReasoning77.0%#52
best: PaLM 2 · 90.9%
Run it locally
VRAM @ Q4
5 GB
VRAM @ FP16
16 GB
Fits on (Q4)
RTX 3060 12GBRTX 4070 Ti 16GBRTX 3090 24GBRTX 4090 24GBRTX 5090 32GBM4 Pro 48GBM3 Max 128GBM3 Ultra 512GBA100 80GBH100 80GBH200 141GBB200 192GB
Throughput data unavailable.
Quantizations
GGUF · AWQ · GPTQ · MLX
API price weights · each benchmark row carries its own source badge (see methodology)