Gemini 2.5 Pro: Pricing, Context, and Benchmarks

Gemini 2.5 Pro is Google's Gemini 2 model released on 2025-03-25. It runs on a 2.0M-token context window, priced at $1.25/M input and $10.00/M output. Google's frontier reasoning model. 2M-token context window and built-in thinking mode.

Input
$1.25/M
Output
$10.00/M
Cached input
$0.3100/M
Context
2.0M
Max output
66K

Specifications

ProviderGoogle
FamilyGemini 2
Released2025-03-25
Statusactive
Context window2.0M tokens
Max output66K tokens
Modalitiestext image audio video
Capabilities function calling json mode vision streaming audio video thinking
Tokenizergemini
API endpointhttps://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-pro:generateContent
DocumentationOfficial docs ↗
Last updated2026-05-29

Benchmark scores

BenchmarkCategoryScoreMeasuredSource
AIME 2025 math 86.7 2025-03-25
MMLU-Pro general 86.4 2025-03-25
GPQA Diamond reasoning 84.0 2025-03-25
SWE-bench Verified coding 63.8 2025-03-25

Scores reflect single measurements on the date shown. Reproducibility varies; consult the linked source for methodology. Use the comparison tool for side-by-side analysis.

What this model is good for

Based on benchmark scores and capability fit, Gemini 2.5 Pro ranks well for:

Cost calculator

For 1,000 tokens of input and 1,000 tokens of output per call, this model costs $0.0113 per call. For typical usage:

WorkloadTokens / call (in/out)$/call$/1K calls$/1M calls
Short Q&A 200 / 100 $0.001250 $1.25 $1,250
Standard chat 1K / 500 $0.006250 $6.25 $6,250
RAG with retrieval 4K / 500 $0.010000 $10.00 $10,000
Long doc summary 20K / 1K $0.035000 $35.00 $35,000
Long context (100K input) 100K / 2K $0.140000 $140.00 $140,000

For more precise estimates including caching discounts, use the LLM API Cost Calculator.

Other Google models

Common comparisons

API setup

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-pro:generateContent \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "gemini-2-5-pro", "messages": [{"role": "user", "content": "Hello"}]}'

See the official documentation for the full request/response schema. For pre-flight token counting before paying, use the appropriate token counter.