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gemini-3-pro-preview

Vertex AI · chat model

gemini-3-pro-preview is listed here as a chat model from Vertex AI. This page shows simple API pricing, token limits, and capability flags so you can compare it with similar options.

Input
$2.0000 / 1M tokens
Output
$12.0000 / 1M tokens
Cached input
$0.2000 / 1M tokens
Context
65.5K
Compare similar models Open a side-by-side table using this route and the closest match from the current catalog. Estimate workload cost Use token counts, request volume, and cache assumptions to check a realistic monthly bill. Explore benchmark family Review source-backed benchmark rows for the attached base model family. Check data sources See how pricing data is generated and what still needs provider verification.

Quick read

Best for

Use this page when you need a fast view of cost, context size, and supported features before testing the model in your own workload.

Things to verify

Always check the provider page for discounts, cache pricing, region rules, and any model limits that may not appear in public metadata.

Pricing

Item Price
Input
$2.0000 / 1M tokens
Output
$12.0000 / 1M tokens
Cached input
$0.2000 / 1M tokens
Embedding
$2.0000 / 1M tokens
Batch input
$1.0000 / 1M tokens
Batch output
$6.0000 / 1M tokens
Priority input
$3.6000 / 1M tokens
Priority output
$21.6000 / 1M tokens

Token limits

Context window
65.5K
Max input tokens
1.0M
Max output tokens
65.5K
Max tokens
65.5K

Capabilities

Capability Supported
Vision ✅
Function calling ✅
Parallel function calling —
Tool choice ✅
Prompt caching ✅
Reasoning ✅
Response schema ✅
System messages ✅
Audio input ✅
Audio output —
Web search ✅
PDF input ✅
Video input ✅

Benchmarks

Most benchmark rows are attached to the base model family rather than this provider route. Open benchmark explorer

Benchmark Score Metric Scope Checked Source
Humanity's Last Exam 37.5% (no tools) accuracy Base model: Gemini 3 Pro (Gemini 3 Pro Thinking (High)) 2026-05-31 Link
ARC-AGI-2 31.1% accuracy Base model: Gemini 3 Pro (Gemini 3 Pro Thinking (High)) 2026-05-31 Link
GPQA Diamond 91.9% accuracy Base model: Gemini 3 Pro (Gemini 3 Pro Thinking (High)) 2026-05-31 Link
Terminal-Bench 2.0 56.9% accuracy Base model: Gemini 3 Pro (Gemini 3 Pro Thinking (High)) 2026-05-31 Link
SWE-bench Verified 76.2% (single attempt) accuracy Base model: Gemini 3 Pro (Gemini 3 Pro Thinking (High)) 2026-05-31 Link
LiveCodeBench Pro 2439 Elo Elo Base model: Gemini 3 Pro (Gemini 3 Pro Thinking (High)) 2026-05-31 Link
MMMU-Pro 81.0% accuracy Base model: Gemini 3 Pro (Gemini 3 Pro Thinking (High)) 2026-05-31 Link
MRCR v2 77.0% (128k average) accuracy Base model: Gemini 3 Pro (Gemini 3 Pro Thinking (High)) 2026-05-31 Link
MRCR v2 26.3% (1M pointwise) accuracy Base model: Gemini 3 Pro (Gemini 3 Pro Thinking (High)) 2026-05-31 Link
SWE-bench Verified 69.60% % resolved Base model: Gemini 3 Pro (Gemini 3 Pro) 2026-05-31 Link
LMArena Text Arena (English) 1489±5 Arena Elo Base model: Gemini 3 Pro (gemini-3-pro) 2026-05-31 Link
MMLU-Pro 89.8% accuracy Base model: Gemini 3 Pro Preview (Gemini 3 Pro Preview (high)) 2026-05-31 Link
MMLU-Pro 89.5% accuracy Base model: Gemini 3 Pro Preview (Gemini 3 Pro Preview (low)) 2026-05-31 Link

Sources

Source links
Pricing data LiteLLM model cost map
Synced at 2026-05-28

Similar models

This list is ranked by overall similarity. Use filters to emphasize the lens that matters most for the replacement you are making.

Comparing from
Model Cost Input shape Features Context Why it is close
gemini-3-pro-preview
Vertex AI
In $2.0000 / 1M tokens
Out $12.0000 / 1M tokens
pdf
Output: text
VisionFunction callingTool choicePrompt caching
65.5K
Current model
Reference row

Overall blends cost, modality overlap, capabilities, and context.

Model Cost Input shape Features Context Why it is close
gemini-3-pro-preview
vertex_ai-language-models
In $2.0000 / 1M tokens
Out $12.0000 / 1M tokens
pdf audioimage
Output: text
VisionFunction callingTool choicePrompt caching
65.5K
Partial I/O overlap
Overall 76%
gemini-3-pro-preview
Google
In $2.0000 / 1M tokens
Out $12.0000 / 1M tokens
pdf audioimage
Output: text
VisionFunction callingTool choicePrompt caching
65.5K
Partial I/O overlap
Overall 76%
gemini-3-pro-preview
OpenRouter
In $2.0000 / 1M tokens
Out $12.0000 / 1M tokens
pdf
Output: text
VisionFunction callingTool choicePrompt caching
65.5K
Exact I/O shape
Overall 76%
gemini-3.1-pro-preview
vertex_ai-language-models
In $2.0000 / 1M tokens
Out $12.0000 / 1M tokens
pdf audioimage
Output: text
VisionFunction callingTool choicePrompt caching
65.5K
Partial I/O overlap
Overall 74%
gemini-3.1-pro-preview-customtools
vertex_ai-language-models
In $2.0000 / 1M tokens
Out $12.0000 / 1M tokens
pdf audioimage
Output: text
VisionFunction callingTool choicePrompt caching
65.5K
Partial I/O overlap
Overall 74%
gemini-3.1-pro-preview
Vertex AI
In $2.0000 / 1M tokens
Out $12.0000 / 1M tokens
pdf url
Output: text
VisionFunction callingTool choicePrompt caching
65.5K
Same provider
Overall 74%
gemini-3.1-pro-preview-customtools
Vertex AI
In $2.0000 / 1M tokens
Out $12.0000 / 1M tokens
pdf url
Output: text
VisionFunction callingTool choicePrompt caching
65.5K
Same provider
Overall 74%
gemini-pro-latest
Google
In $1.2500 / 1M tokens
Out $10.0000 / 1M tokens
pdf
Output: text
VisionFunction callingTool choicePrompt caching
65.5K
Exact I/O shape
Overall 71%
gemini-pro-latest
Google
In $1.2500 / 1M tokens
Out $10.0000 / 1M tokens
pdf
Output: text
VisionFunction callingTool choicePrompt caching
65.5K
Exact I/O shape
Overall 71%
gemini-2.5-pro
vertex_ai-language-models
In $1.2500 / 1M tokens
Out $10.0000 / 1M tokens
pdf audioimage
Output: text
VisionFunction callingTool choicePrompt caching
65.5K
Partial I/O overlap
Overall 71%
gemini-2.5-pro
Google
In $1.2500 / 1M tokens
Out $10.0000 / 1M tokens
pdf audioimage
Output: text
VisionFunction callingTool choicePrompt caching
65.5K
Partial I/O overlap
Overall 71%
gemini-3.5-flash
Vertex AI
In $1.5000 / 1M tokens
Out $9.0000 / 1M tokens
pdf url
Output: text
VisionFunction callingTool choicePrompt caching
65.5K
Same provider
Overall 67%
gemini-3-flash-preview
Vertex AI
In $0.5000 / 1M tokens
Out $3.0000 / 1M tokens
pdf
Output: text
VisionFunction callingTool choicePrompt caching
65.5K
Same provider
Overall 61%
gpt-5.5
Azure
In $5.0000 / 1M tokens
Out $30.0000 / 1M tokens
pdf
Output: text
VisionFunction callingTool choicePrompt caching
128.0K
Exact I/O shape
Overall 50%
gpt-5.5-2026-04-23
Azure
In $5.0000 / 1M tokens
Out $30.0000 / 1M tokens
pdf
Output: text
VisionFunction callingTool choicePrompt caching
128.0K
Exact I/O shape
Overall 50%
grok-4.20-reasoning
Vertex AI
In $2.0000 / 1M tokens
Out $6.0000 / 1M tokens
pdf
Output: unknown
VisionFunction callingTool choiceReasoning
2.0M
Same provider
Overall 29%
grok-4.20-non-reasoning
Vertex AI
In $2.0000 / 1M tokens
Out $6.0000 / 1M tokens
pdf
Output: unknown
VisionFunction callingTool choiceResponse schema
2.0M
Same provider
Overall 26%