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Qwen3.5 27B (Reasoning)

AlibabaQwenOpen WeightApache 2.0 · Usage Commercial

Description

Qwen3.5-27B is a multimodal dense foundation model with 27 billion parameters. It combines strong reasoning, coding, multilingual, long-context, and visual understanding performance in a production-friendly open-weight package with a native 262K context window.

Date de sortie
2026-02-24
Paramètres
27.0B
Longueur du contexte
262K
Modalités
audio, image, text, video

Radar de capacités

32
general
40
coding
86
reasoning
57
science
60
agents
80
multimodal

Classements

Domaine#RangScoreSource
Capacité agentique105
38.0
LS
Classement codage131
68.0
AA
Classement général92
73.0
AA
Classement multimodal36
54.0
LS
Science122
66.0
AA

Scores de benchmarks (LLM Stats)

(LLM Stats (zeroeval))

3d

SUNRGBD0.35 / 100Aut.
Hypersim0.13 / 100Aut.

Agents

t2-bench79.0%Aut.
BFCL-V468.5%Aut.
AndroidWorld_SR64.2%Aut.
WideSearch61.1%Aut.
BrowseCompOpenAI (2025)61.0%Aut.
FullStackBench en60.1%Aut.
TIR-Bench59.8%Aut.
FullStackBench zh57.4%Aut.
OSWorld-Verified56.2%Aut.
VITA-Bench41.9%Aut.
Terminal-Bench 2.0Stanford × Laude Institute (2026)41.6%Aut.
DeepPlanning22.6%Aut.

Biology

GPQANYU + Cohere + Anthropic (2023)85.5%Aut.

Chemistry

SuperGPQA65.6%Aut.

Code

SWE-Bench Verified72.4%Aut.

Communication

Multi-Challenge60.8%Aut.

Embodied

EmbSpatialBench0.84 / 100Aut.

Finance

MMLU-Pro86.1%Aut.
MMLU-ProX82.2%Aut.

General

IFEvalGoogle Research (2023)95.0%Aut.
MMLU-Redux93.2%Aut.
C-Eval90.5%Aut.
MAXIFE88.0%Aut.
Global PIQA87.5%Aut.
MMMLU85.9%Aut.
MMMU82.3%Aut.
Include81.6%Aut.
MMStar81.0%Aut.
LiveCodeBench v680.7%Aut.
IFBench76.5%Aut.
MMMU-Pro75.0%Aut.
LongBench v260.6%Aut.
NOVA-6358.1%Aut.
SimpleVQA0.56 / 100Aut.

Grounding

RefCOCO-avg0.91 / 100Aut.
ScreenSpot Pro70.3%Aut.
RefSpatialBench0.68 / 100Aut.

Healthcare

VideoMMMU82.3%Aut.
SlakeVQA80.0%Aut.
MedXpertQA62.4%Aut.
PMC-VQA62.4%Aut.

Image To Text

OCRBench89.4%Aut.

Language

LingoQA82.0%Aut.
WMT24++77.6%Aut.

Long Context

MLVU85.9%Aut.
LVBench73.6%Aut.
AA-LCR66.1%Aut.
MMLongBench-Doc0.60 / 100Aut.

Math

HMMT 202592.0%Aut.
HMMT2589.8%Aut.
MathVista-Mini87.8%Aut.
DynaMath87.7%Aut.
MathVision86.0%Aut.
CodeForces0.81 / 3000Aut.
PolyMATH71.2%Aut.
Humanity's Last Exam48.5%Aut.

Multimodal

VLMsAreBlind96.9%Aut.
V*93.7%Aut.
AI2D92.9%Aut.
MMBench-V1.192.6%Aut.
OmniDocBench 1.588.9%Aut.
VideoMME w sub.87.0%Aut.
VideoMME w/o sub.82.8%Aut.
CC-OCR81.0%Aut.
CharXiv-R79.5%Aut.
MVBench74.6%Aut.
MMVU73.3%Aut.
BabyVision44.6%Aut.
ZEROBench-Sub0.36 / 100Aut.
Nuscene15.2%Aut.
ZEROBench0.10 / 100Aut.

Reasoning

CountBench0.98 / 100Aut.
Hallusion Bench70.0%Aut.
BrowseComp-zh62.1%Aut.
ERQA60.5%Aut.
Seal-047.2%Aut.
OJBench40.1%Aut.

Spatial Reasoning

RealWorldQA83.7%Aut.

Vision

ODinW41.1%Aut.

Indices d'évaluation AA

(Artificial Analysis)
Intelligence Index(Artificial Analysis)
34.6
Tau2(Sierra + U Toronto + Vector Institute (2025))
0.9
Gpqa(NYU + Cohere + Anthropic (2023))
0.9
Ifbench(Google Research (2023))
0.8
Lcr(Artificial Analysis)
0.7
Scicode(UIUC + Argonne National Lab (2024))
0.4
Terminalbench Hard(Stanford × Laude Institute (2026))
0.3
Hle(Center for AI Safety + Scale AI (2025))
0.2

Scores par catégorie LLM Stats

(LLM Stats (zeroeval))
Instruction Following
90
Biology
90
Legal
80
Math
80
Physics
80
Structured Output
80
Image To Text
80
Language
80
Embodied
80
Finance
80
Grounding
80
Chemistry
80
Text-to-image
80
Video
80
Long Context
70
Multimodal
70
Reasoning
70
Spatial Reasoning
70
Frontend Development
70
General
70
Healthcare
70
Economics
70
Vision
70
Search
60
Agents
60
Code
60
Communication
60
Tool Calling
60
Spatial
20
3d
20

Tarification

Prix d'entrée$0.3 / 1M tokens
Prix de sortie$2.4 / 1M tokens
Prix mixte (3:1)$0.825 / 1M tokens

Vitesse

Tokens/sec0.0
Délai du premier token0.00s
Temps de réponse0.00s

Classement des Prix par Fournisseur

Classement des Prix par Fournisseur

4 fournisseurs

Moins cher: NovitaPlus cher: Alibaba
FournisseurEntréeSortie
1NovitaMoins cher
$0
$0
2EmpirioLabs AI
$0.086
$0.688
3NanoGPT
$0.27
$2.16
4AlibabaPRINCIPAL
$0.3
$2.4

Comparer les prix entre différents fournisseurs API pour ce modèle.

Sources externes

LLM StatsArtificial Analysis