LLM Reference

Gemini 1.0 Ultra

Released
2023-12-13
Last refreshed
2026-06-15
Status
Researched 243d ago
ProprietaryCommercial use: conditionalLong contextVision

Gemini 1.0 Ultra is worth evaluating for long context and vision when its provider route and context window match the workload.

Use it for

  • Teams evaluating long context and vision
  • Workloads that can use a 1m context window
  • Buyers comparing 1 tracked provider route

Do not use it for

  • Vision or document-understanding workloads
  • Strict JSON or tool-calling flows
Specifications
Released
2023-12-13
Context
1m
Architecture
Decoder Only
Specialization
general
Openness
Proprietary
License
ProprietaryCommercial use: conditional
Weights
Not released
Code
Unknown
Training
Fine-tuned
Created by

Pioneering artificial intelligence research.

London, United Kingdom
Founded 2014
Website
Pricing
Output / 1M
$3.00
Input / 1M
$1.00

Cheapest of 1 route · GCP Vertex AI

About

Google's Gemini 1.0 Ultra is a leading large language model designed for tackling highly complex tasks with advanced analytical capabilities. As the largest model in the Gemini 1.0 family, it excels in coding, mathematical reasoning, and multimodal reasoning. Its strength lies in its ability to seamlessly understand and process diverse data types, including text, code, audio, images, and video. Gemini Ultra surpasses human experts on the MMLU benchmark with a 90% score, although it has limitations in image generation and some multimodal tasks.

Top use-case fit

Long context

Included by capability and metadata signals in the decision map.

Vision

Q/$ C

1 relevant benchmark in the decision map.

Provider price ladder

Compare API pricing across 1 providers for input and output tokens, batch, and cached reads when available.

ProviderInput / 1MOutput / 1MRoute
GCP Vertex AI$1.00$3.00
Serverless

Available via routers & gateways(13)

Capabilities

No model capability flags are currently sourced.

Benchmark peer barsfor Vision

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.
BenchmarkScoreVersionEvaluationSource
Massive Multi-discipline Multimodal Understanding59.4Observed 2026-04-14Source

Migration checks

No linked migration route is available for this model yet.