LLM Reference

Amazon Nova 2 Omni

Released
2025-12-02
Last refreshed
2026-06-04
Status
Researched 22d ago
ProprietaryCommercial use: conditionalMultimodalLong contextVision

Amazon Nova 2 Omni is a released long context and vision model with 1m context; evaluate it while provider pricing coverage matures.

Use it for

  • Teams evaluating long context and vision
  • Workloads that can use a 1m context window

Do not use it for

  • Cost-sensitive launches that need sourced token pricing
  • Strict JSON or tool-calling flows
  • Teams that need a tracked hosted API route today
Specifications
Released
2025-12-02
Context
1m
Specialization
audio
Openness
Proprietary
License
ProprietaryCommercial use: conditional
Created by

Democratizing AI for all businesses

Seattle, Washington, United States
Founded 2006
Website
Pricing

No tracked provider token pricing is available yet.

About

Amazon Nova 2 Omni is an early-access Nova Forge multimodal reasoning model supporting text, images, video, and speech inputs plus text and image outputs. It is not available as a standard public Bedrock pay-per-token API model, so it should not be used for standard pricing or frontier-provider shortlist rows.

Amazon Nova 2 Omni is a proprietary model in the Amazon Nova family. The structured metadata tracks a 1m-token context window, multimodal input, and audio. Headline tracked benchmarks include MMMU Pro 61.4.

Top use-case fit

Long context

Included by capability and metadata signals in the decision map.

Vision

Included by capability and metadata signals in the decision map.

Provider price ladder

No tracked provider token pricing is available for this model yet.

Capabilities

VisionMultimodalAudio

Benchmark peer barsfor Long context

No task-mapped benchmark peers are available for this model yet.

Benchmark scores(1)

Scores are benchmark-specific and are direction-aware: the same numeric gap can mean very different outcomes across suites. Use the leaderboard context and this model's provider route to decide whether the winning margin is meaningful for your workload.
BenchmarkScoreVersionSource
MMMU Pro61.4LLM-Stats aggregatorhttps://llm-stats.com/benchmarks/mmmu-pro

Migration checks

No linked migration route is available for this model yet.