Nova Pro

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Amazon Nova Pro is a powerful multimodal model that delivers exceptional performance in understanding text, images, and video. It demonstrates advanced capabilities in language comprehension, mathematical problem-solving, and various multimodal applications. Furthermore, it provides unparalleled speed and cost-effectiveness, setting a new standard in the industry.

Model Specifications

Technical details and capabilities of Nova Pro

Core Specifications

300.0K / 300.0K

Input / Output tokens

November 19, 2024

Release date

Capabilities & License

Multimodal Support

Supported

Web Hydrated

Yes

License

Proprietary

Resources

Research Paper

https://www.amazon.science/publications/the-amazon-nova-family-of-models-technical-report-and-model-card

API Reference

https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-nova.html

Code Repository

https://huggingface.co/amazon-agi

Performance Insights

Check out how Nova Pro handles various AI tasks through comprehensive benchmark results.

100

75

50

25

0

94.8

GSM8K

94.8

(95%)

94.8

ARC-C

94.8

(95%)

93.5

Doc VQA

93.5

(94%)

92.1

IFEval

92.1

(92%)

89.2

Chart QA

89.2

(89%)

89.1

Translation en→Set1 COMET22

89.1

(89%)

89

Translation Set1→en COMET22

89

(89%)

89

HumanEval

89

(89%)

86.9

BBH

86.9

(87%)

85.9

MMLU

85.9

(86%)

85.4

DROP

85.4

(85%)

81.5

Text VQA

81.5

(82%)

81.4

GroundUI-1K

81.4

(81%)

79.7

VisualWebBench

79.7

(80%)

77.8

VATEX

77.8

(78%)

77.2

FinQA

77.2

(77%)

76.6

MATH

76.6

(77%)

72.1

Ego Schema

72.1

(72%)

68.4

BFCL

68.4

(68%)

63.7

MM-Mind2Web

63.7

(64%)

61.7

MMMU

61.7

(62%)

50.3

CRAG

50.3

(50%)

46.9

GPQA

46.9

(47%)

44.4

Translation Set1→en spBleu

44.4

(44%)

43.4

Translation en→Set1 spBleu

43.4

(43%)

41.6

LVBench

41.6

(42%)

19.8

SQuALITY

19.8

(20%)

GSM8K

ARC-C

Doc VQA

IFEval

Chart QA

Translation en→Set1 COMET22

Translation Set1→en COMET22

HumanEval

BBH

MMLU

DROP

Text VQA

GroundUI-1K

VisualWebBench

VATEX

FinQA

MATH

Ego Schema

BFCL

MM-Mind2Web

MMMU

CRAG

GPQA

Translation Set1→en spBleu

Translation en→Set1 spBleu

LVBench

SQuALITY

Model Comparison

See how Nova Pro stacks up against other leading models across key performance metrics.

100

80

60

40

20

0

85.9

MMLU - Nova Pro

85.9

(86%)

87.3

MMLU - Llama 3.1 405B Instruct

87.3

(87%)

83.3

MMLU - Qwen2.5 32B Instruct

83.3

(83%)

79.7

MMLU - Qwen2.5 14B Instruct

79.7

(80%)

90.4

MMLU - Claude 3.5 Sonnet

90.4

(90%)

77.6

MMLU - Nova Micro

77.6

(78%)

94.8

GSM8K - Nova Pro

94.8

(95%)

96.8

GSM8K - Llama 3.1 405B Instruct

96.8

(97%)

95.9

GSM8K - Qwen2.5 32B Instruct

95.9

(96%)

94.8

GSM8K - Qwen2.5 14B Instruct

94.8

(95%)

96.4

GSM8K - Claude 3.5 Sonnet

96.4

(96%)

92.3

GSM8K - Nova Micro

92.3

(92%)

46.9

GPQA - Nova Pro

46.9

(47%)

50.7

GPQA - Llama 3.1 405B Instruct

50.7

(51%)

49.5

GPQA - Qwen2.5 32B Instruct

49.5

(50%)

45.5

GPQA - Qwen2.5 14B Instruct

45.5

(46%)

59.4

GPQA - Claude 3.5 Sonnet

59.4

(59%)

40

GPQA - Nova Micro

40

(40%)

76.6

MATH - Nova Pro

76.6

(77%)

73.8

MATH - Llama 3.1 405B Instruct

73.8

(74%)

83.1

MATH - Qwen2.5 32B Instruct

83.1

(83%)

80

MATH - Qwen2.5 14B Instruct

80

(80%)

71.1

MATH - Claude 3.5 Sonnet

71.1

(71%)

69.3

MATH - Nova Micro

69.3

(69%)

89

HumanEval - Nova Pro

89

(89%)

89

HumanEval - Llama 3.1 405B Instruct

89

(89%)

88.4

HumanEval - Qwen2.5 32B Instruct

88.4

(88%)

83.5

HumanEval - Qwen2.5 14B Instruct

83.5

(84%)

92

HumanEval - Claude 3.5 Sonnet

92

(92%)

81.1

HumanEval - Nova Micro

81.1

(81%)

MMLU

GSM8K

GPQA

MATH

HumanEval

Nova Pro

Llama 3.1 405B Instruct

Qwen2.5 32B Instruct

Qwen2.5 14B Instruct

Claude 3.5 Sonnet

Nova Micro

Detailed Benchmarks

Dive deeper into Nova Pro's performance across specific task categories. Expand each section to see detailed metrics and comparisons.

Math

GSM8K

97.1%

95.9%

Qwen2.5 72B Instruct

95.8%

95.1%

95.0%

94.8%

Qwen2.5 14B Instruct

94.8%

Mistral Large 2

93.0%

92.3%

68.6%

Current model

Other models

Avg (92.2%)

Coding

HumanEval

Claude 3.5 Sonnet

93.7%

92.4%

Claude 3.5 Sonnet

92.0%

Mistral Large 2

92.0%

90.2%

89.0%

89.0%

Llama 3.1 405B Instruct

89.0%

Mistral Small 3.1 24B

88.4%

Ministral 8B Instruct

34.8%

Current model

Other models

Avg (85.1%)

Reasoning

DROP

92.2%

91.6%

Claude 3.5 Sonnet

87.1%

Claude 3.5 Sonnet

87.1%

86.0%

85.4%

Llama 3.1 405B Instruct

84.8%

83.4%

Claude 3.5 Haiku

83.1%

Llama 3.1 8B Instruct

59.5%

Current model

Other models

Avg (84.0%)

Knowledge

MMLU

91.8%

86.4%

86.2%

Llama 3.2 90B Instruct

86.0%

Llama 3.3 70B Instruct

86.0%

85.9%

85.9%

85.2%

84.8%

Llama 3.2 3B Instruct

63.4%

Current model

Other models

Avg (84.2%)

GPQA

87.7%

50.4%

Qwen2.5 32B Instruct

49.5%

Qwen2.5 72B Instruct

49.0%

48.0%

46.9%

Llama 3.2 90B Instruct

46.7%

Qwen2.5 14B Instruct

45.5%

Mistral Small 3

45.3%

Qwen2 7B Instruct

25.3%

Current model

Other models

Avg (49.4%)

MATH

97.9%

Claude 3.5 Sonnet

78.3%

78.0%

Gemini 1.5 Flash

77.9%

Llama 3.3 70B Instruct

77.0%

76.6%

76.6%

76.1%

Qwen2.5 7B Instruct

75.5%

32.6%

Current model

Other models

Avg (74.6%)

Non categorized

MMMU

Gemini Pro 2.5 Experimental

81.7%

64.0%

63.2%

Mistral Small 3.1 24B

62.8%

Gemini 1.5 Flash

62.3%

61.7%

Llama 3.2 90B Instruct

60.3%

59.4%

56.2%

0.0%

Current model

Other models

Avg (57.2%)

ARC-C

94.8%

92.4%

90.2%

Llama 3.2 3B Instruct

78.6%

Qwen2.5 32B Instruct

70.4%

Qwen2.5 14B Instruct

67.3%

Current model

Other models

Avg (82.3%)

IFEval

Claude 3.7 Sonnet

93.2%

92.1%

Llama 3.3 70B Instruct

92.1%

90.4%

90.0%

89.7%

Llama 3.1 405B Instruct

88.6%

Llama 3.1 70B Instruct

87.5%

Current model

Other models

Avg (90.5%)

BBH

87.6%

86.9%

Qwen2.5 32B Instruct

84.5%

84.3%

82.4%

Qwen2 72B Instruct

82.4%

79.5%

Qwen2.5 14B Instruct

78.2%

Current model

Other models

Avg (83.2%)

Translation en→Set1 spBleu

43.4%

41.5%

40.2%

Current model

Other models

Avg (41.7%)

Translation en→Set1 COMET22

89.1%

88.8%

88.5%

Current model

Other models

Avg (88.8%)

Translation Set1→en spBleu

44.4%

43.1%

42.6%

Current model

Other models

Avg (43.4%)

Translation Set1→en COMET22

89.0%

88.8%

88.7%

Current model

Other models

Avg (88.8%)

Chart QA

89.2%

86.8%

Current model

Other models

Avg (88.0%)

Doc VQA

93.5%

92.4%

Current model

Other models

Avg (93.0%)

Text VQA

81.5%

80.2%

Llama 3.2 90B Instruct

73.5%

Current model

Other models

Avg (78.4%)

VATEX

77.8%

77.8%

Current model

Other models

Avg (77.8%)

Ego Schema

72.1%

71.4%

Current model

Other models

Avg (71.8%)

BFCL

Llama 3.1 405B Instruct

88.5%

Llama 3.1 70B Instruct

84.8%

Llama 3.1 8B Instruct

76.1%

74.0%

68.4%

66.6%

66.4%

65.0%

56.2%

Current model

Other models

Avg (71.8%)

VisualWebBench

79.7%

77.7%

Current model

Other models

Avg (78.7%)

MM-Mind2Web

63.7%

60.7%

Current model

Other models

Avg (62.2%)

GroundUI-1K

81.4%

80.2%

Current model

Other models

Avg (80.8%)

SQuALITY

Phi-3.5-mini-instruct

24.3%

Phi-3.5-MoE-instruct

24.1%

19.8%

19.2%

18.8%

Current model

Other models

Avg (21.2%)

LVBench

41.6%

40.4%

Current model

Other models

Avg (41.0%)

FinQA

77.2%

73.6%

65.2%

Current model

Other models

Avg (72.0%)

CRAG

Jamba 1.6 Large

78.2%

Mistral Large 2

67.0%

Llama 3.3 70B Instruct

61.7%

Llama 3.1 8B Instruct

60.0%

Ministral 8B Instruct

52.0%

50.3%

49.4%

43.8%

43.1%

Current model

Other models

Avg (56.2%)

Providers Pricing Coming Soon

We're working on gathering comprehensive pricing data from all major providers for Nova Pro. Compare costs across platforms to find the best pricing for your use case.

OpenAI

Anthropic

Google

Mistral AI

Cohere

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