Gemini 2.5 Pro

Name: Gemini 2.5 Pro
Price: 1.25 USD
Author: Google

Googlefrontier

Google's thinking model that combines strong reasoning with native multimodal understanding and a 1M+ token context window. Features built-in Google Search grounding and code execution. Excels at long-context analysis, multimodal reasoning, and complex STEM tasks.

Released 2025-09-20Knowledge cutoff: 2025-06

Medium confidence|Updated 55d ago|90% source confidence

Specifications

Context Window

1.0M tokens

Max Output

65.5K tokens

Input Price

$1.25 / 1M tokens

Output Price

$10.00 / 1M tokens

Latency Tier

Moderate (speed score: 5.5/10)

Capability Profile

Long Context

10/10

Multimodal

9.5/10

Reasoning

9/10

Factuality

9/10

Coding

8.5/10

Structured Output

8.5/10

Instruction Following

8.5/10

Tool Use

8.5/10

Safety & Enterprise

8/10

Conversational

8/10

Creativity

7.5/10

Cost Efficiency

7/10

Speed

5.5/10

Feature Support

Vision Yes

Audio In Yes

Audio Out No

Video Yes

Image Generation No

Image Editing No

Function Calling Yes

JSON Mode Yes

Structured Output Yes

Streaming Yes

Reasoning Yes

Realtime No

Computer Use No

Web Search No

Best Use Cases

Analyzing entire codebases, books, or video corpora within its 1M context

Multimodal reasoning combining video, audio, and text in a single query

STEM research requiring thinking-mode reasoning with grounding

Multi-document synthesis and comparison across hundreds of sources

Data analysis with native code execution for verification

Not Ideal For

Simple tasks where its thinking overhead is wasteful and slow

Latency-sensitive interactive chat (thinking adds 5-20s)

Pure coding tasks where Claude Opus 4.6 is demonstrably better

Safety-critical enterprise use requiring Anthropic-level alignment

Strengths

Largest effective context window with strong recall — 1M tokens with good needle-in-haystack

Best-in-class multimodal understanding across text, images, audio, and video

Built-in thinking mode rivals o3 on many reasoning benchmarks

Google Search grounding reduces hallucination on factual queries

Native code execution allows self-verification of analytical results

Very competitive pricing for a frontier thinking model

Weaknesses

Thinking mode increases latency significantly (5-20s for complex queries)

Coding quality is strong but measurably below Claude Opus/Sonnet 4.6

Structured output compliance is less consistent than Anthropic models

Instruction following can be imprecise on complex multi-constraint prompts

Safety filtering can be unpredictable — occasionally over-refuses, occasionally under-refuses

Edge Cases & Notes

Thinking tokens are billed at the output rate — effective cost can be 2-4x visible output on hard problems

Google Search grounding adds latency but dramatically improves factuality on current events

Video analysis works best with clips under 30 minutes; quality degrades on very long videos

1M context pricing is tiered — significantly cheaper under 200K tokens

Provider Notes

Available through the Gemini API and Google Cloud Vertex AI. Free tier available with rate limits. Thinking mode can be controlled via API parameters. Google Search grounding requires separate API enablement.

Benchmarks

MMLU91.8%

HumanEval90.5%

Arena Elo1390

Benchmark Notes

MMLU-Pro 91.8%. Strong on GPQA Diamond (~68%). Best-in-class on long-context benchmarks (RULER, needle-in-haystack). Multimodal benchmarks are its strongest area. SWE-bench ~55%.

Research Meta

Last Evaluated

2026-04-01

Source Confidence

90%

Evaluation Method

LMSYS Arena, MMLU-Pro, GPQA, RULER long-context, multimodal evaluations, SWE-bench

Needs Re-evaluation

Sources

Google Gemini 2.5 Pro technical report
LMSYS Chatbot Arena
RULER long-context benchmark
Artificial Analysis

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