Comparisons of Small Open Source AI Models (4B-40B)

Open source AI models with between 4B to 40B parameters.

Models are considered open source (also commonly referred to as open weights) where their weights are accessible to download. This allows self-hosting on your own infrastructure and enables customizing the model such as through fine-tuning.

For more details including relating to our methodology, see our FAQs.

Alibaba logoQwen3.6 27B and Alibaba logoQwen3.5 27B are the highest intelligence Small open source models, defined as those with 4B-40B parameters, followed by Alibaba logoQwen3.6 35B A3B & AI9Stars logoG9v3-39A5B.

Highlights

Artificial Analysis Openness Index · Higher is better
Artificial Analysis Intelligence Index · Higher is better
Trainable parameters in billions

Openness

Artificial Analysis Openness Index: Score

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)
Reasoning models are indicated by a lightbulb icon

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.1.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR
Estimate (independent evaluation forthcoming)
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better
See more

Agentic real-world work tasks, (Elo-500)/2000

Agentic tool use

Agentic coding & terminal use

Coding

Reasoning & knowledge

Scientific reasoning

Physics reasoning

Knowledge

1 - hallucination rate

AA-LCRUpdated

Long context reasoning

Agentic knowledge work, Elo

Agentic SaaS workflows

No data available

Legal agentic work, criterion pass rate

Agentic business operations

Instruction following

Long-horizon agentic tasks

No data available

Kubernetes incident root-cause analysis

Visual reasoning

Reasoning models are indicated by a lightbulb icon

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Size

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference
Reasoning models are indicated by a lightbulb icon

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

The number of parameters actually executed during each inference forward pass, expressed in billions. For Mixture of Experts (MoE) models, a routing mechanism selects a subset of experts per token, resulting in fewer active than total parameters. Dense models use all parameters, so active equals total.

Intelligence Index vs. Active Parameters

Artificial Analysis Intelligence Index · Active parameters at inference time
Most attractive quadrant
Pareto line
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

The number of parameters actually executed during each inference forward pass, expressed in billions. For Mixture of Experts (MoE) models, a routing mechanism selects a subset of experts per token, resulting in fewer active than total parameters. Dense models use all parameters, so active equals total.

Intelligence Index vs. Total Parameters

Artificial Analysis Intelligence Index · Size in parameters (billions)
Most attractive quadrant
Pareto line
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

Context Window

Context Window

Context window: tokens limit · Higher is better
Reasoning models are indicated by a lightbulb icon

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Further details

Weights
Provider Benchmarks
Qwen3.6 27B (Reasoning)
Alibaba logoAlibaba
38
27.8B
262k
$0.9
52
SiliconFlowAlibaba CloudDeepInfra
+2
Qwen3.6 35B A3B (Reasoning)
Alibaba logoAlibaba
32
36B
3B active at inference time
262k
$0.4
119
NovitaDeepInfraGMI
+5
G9v3-39A5B
AI9Stars logoAI9Stars
31
39B
5B active at inference time
131k
-
-
AI9Stars
Qwen3.6 27B (Non-reasoning)
Alibaba logoAlibaba
31
27.8B
262k
$0.9
56
GroqDeepInfraNovitaAlibaba Cloud
Gemma 4 31B (Reasoning)
Google logoGoogle
30
30.7B
256k
-
35
CoreWeaveGMIDeepInfra
+10
Gemma 4 26B A4B (Reasoning)
Google logoGoogle
26
25.2B
3.8B active at inference time
256k
$0.1
-
CloudflareMakoraDeepInfra
+5
Qwen3.6 35B A3B (Non-reasoning)
Alibaba logoAlibaba
25
36B
3B active at inference time
262k
$0.6
144
Alibaba CloudNovitaDeepInfra
+4
Qwen3.5 35B A3B (Non-reasoning)
Alibaba logoAlibaba
24
36B
3B active at inference time
262k
$0.4
170
Alibaba CloudDeepInfra
Gemma 4 12B (Reasoning)
Google logoGoogle
22
12B
256k
$0.1
42
SiliconFlow
Gemma 4 31B (Non-reasoning)
Google logoGoogle
22
30.7B
256k
$0.2
64
SambaNovaNovitaParasail
+5
Qwen3.5 9B (Reasoning)
Alibaba logoAlibaba
22
9.7B
262k
$0.1
78
Together AISiliconFlow
Apriel-v1.6-15B-Thinker
ServiceNow logoServiceNow
21
15B
128k
-
-
Together AI