The best open LLMs for your use case:

1Kimi K3Moonshot

Moonshot AI's 2.8-trillion-parameter open-weight frontier model with native vision, 1M-token context, and state-of-the-art agentic coding and tool use.

Speed:

Intelligence:

Price: (1M Tokens)

$3.00 / 15.00

Cached input: (1M Tokens)

$0.30

Context: (tokens)

1,048,576

Inputs:

ImageText

Benchmarks:

#1

GPQA-Diamond

General Knowledge

93.5
#1

HLE

General Knowledge

43.5
#1

MMMU-Pro

Multimodal - Vision

81.6
#1

Terminal-Bench 2.1

Coding Agents

88.3
#1

FrontierSWE

Coding Agents

81.2
#1

DeepSWE

Coding Agents

69
#1

Program Bench

Coding Agents

77.8
#1

MCP-Atlas

Agents and Function Calling

84.2
#1

GPQA-Diamond

General Knowledge

93.5
#1

HLE

General Knowledge

43.5
#1

MMMU-Pro

Multimodal - Vision

81.6
#1

Terminal-Bench 2.1

Coding Agents

88.3
#1

FrontierSWE

Coding Agents

81.2
#1

DeepSWE

Coding Agents

69
#1

Program Bench

Coding Agents

77.8
#1

MCP-Atlas

Agents and Function Calling

84.2
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2Qwen3.5 397B-A17BQwen

Qwen's native multimodal MoE model with 397B total parameters and 17B active, featuring hybrid Gated Delta Networks for strong reasoning and vision capabilities.

Speed:

Intelligence:

Price: (1M Tokens)

$0.60 / 3.60

Context: (tokens)

262,144

Inputs:

ImageText

Benchmarks:

#1

MMLU-Pro

General Knowledge

87.8
#1

AA-LCR

Summarization

68.7
#1

LongBenchv2

Summarization

63.2
#1

Multilingual MMLU

Multilingual

88.5
#1

MMMU

Multimodal - Vision

85
#1

TAU2-Bench

Agents and Function Calling

86.7
#1

BFCL

Agents and Function Calling

72.9
#2

MCP-Mark

Agents and Function Calling

46.1
#1

MMLU-Pro

General Knowledge

87.8
#1

AA-LCR

Summarization

68.7
#1

LongBenchv2

Summarization

63.2
#1

Multilingual MMLU

Multilingual

88.5
#1

MMMU

Multimodal - Vision

85
#1

TAU2-Bench

Agents and Function Calling

86.7
#1

BFCL

Agents and Function Calling

72.9
#2

MCP-Mark

Agents and Function Calling

46.1
Try it out

Use case:

Creative Writing