The best LLMs for your use case:

1DeepSeek-V4-ProDeepSeek

DeepSeek's frontier 1.6T-parameter Mixture-of-Experts model (49B active per token) with hybrid attention built for long-context, low-cost reasoning. Runs in FP4 with a 512K-token context window.

Speed:

Intelligence:

Price: (1M Tokens)

$2.10 / 4.40

Inputs:

ImageText

JSON Mode:

Function Calling:

Benchmarks:

#1

WebDevArena

Code

1459
#1

LiveCodeBench

Code

93.5
#1

SimpleQA

General Knowledge

57.9
#1

LongBenchv2

Summarization

70
#2

GPQA-Diamond

General Knowledge

90.1
#2

MMLU-Pro

General Knowledge

87.5
#1

WebDevArena

Code

1459
#1

LiveCodeBench

Code

93.5
#1

SimpleQA

General Knowledge

57.9
#1

LongBenchv2

Summarization

70
#2

GPQA-Diamond

General Knowledge

90.1
#2

MMLU-Pro

General Knowledge

87.5
Try it out
2Kimi K2.6Moonshot

1T-parameter MoE flagship from Moonshot with long-horizon coding, agent swarms scaling to 300 sub-agents, and state-of-the-art reasoning.

Speed:

Intelligence:

Price: (1M Tokens)

$1.20 / 4.50

Inputs:

ImageText

JSON Mode:

Function Calling:

Benchmarks:

#3

WebDevArena

Code

1446
#2

LiveCodeBench

Code

89.6
#1

GPQA-Diamond

General Knowledge

90.5
#1

EQBench

Creative Writing

1565.3
#2

LMArena

Chat

1447
#2

MMMU

Multimodal - Vision

84.3
#3

MMLU-Pro

General Knowledge

87.1
#3

LongBenchv2

Summarization

61
#3

BFCL

Agents and Function Calling

71.1
#5

SimpleQA

General Knowledge

36.9
#3

WebDevArena

Code

1446
#2

LiveCodeBench

Code

89.6
#1

GPQA-Diamond

General Knowledge

90.5
#1

EQBench

Creative Writing

1565.3
#2

LMArena

Chat

1447
#2

MMMU

Multimodal - Vision

84.3
#3

MMLU-Pro

General Knowledge

87.1
#3

LongBenchv2

Summarization

61
#3

BFCL

Agents and Function Calling

71.1
#5

SimpleQA

General Knowledge

36.9
Try it out

Use case:

Code

Features:

Function Calling