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gemini-3-pro-preview

Google · Chat model

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

Provider and model identifiers are kept in their original form for accuracy.

gemini-gemini-gemini-3-pro-preview

Lifecycle Deprecation date: March 9, 2026
Input
$2.0000 / 1M tokens
Output
$12.0000 / 1M tokens
Cached input
$0.2000 / 1M tokens
Context window
65.5K

Catalog generated: Jul 13, 2026

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

Limits

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

Capabilities

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

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

Similar models

Candidates below have the same known input/output modality shape and usable token pricing. Use the filters to change the ranking lens before opening a comparison.

Comparing from
Model Cost Input shape Features Context Why it is close
gemini-3-pro-preview
Google
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, exact modality shape, 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
Output: text
VisionFunction callingTool choicePrompt caching
65.5K
Exact I/O shape
Overall 76%
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
Exact I/O shape
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
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 71%
gemini-2.5-pro
vertex_ai-language-models
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
Google
In $1.2500 / 1M tokens
Out $10.0000 / 1M tokens
pdf
Output: text
VisionFunction callingTool choicePrompt caching
65.5K
Same provider
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
Same provider
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
Same provider
Overall 71%
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
Exact I/O shape
Overall 61%
gpt-5.2-chat
Azure
In $1.7500 / 1M tokens
Out $14.0000 / 1M tokens
pdf
Output: text
VisionFunction callingTool choicePrompt caching
16.4K
Exact I/O shape
Overall 54%
gpt-5.2-chat-2025-12-11
Azure
In $1.7500 / 1M tokens
Out $14.0000 / 1M tokens
pdf
Output: text
VisionFunction callingTool choicePrompt caching
16.4K
Exact I/O shape
Overall 54%

Sources

Source links
Pricing dataLiteLLM model cost map
Synced at2026-05-28
Catalog generated2026-07-13T14:21:55.110Z