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Gemini 2.5 Flash

Googlemid

Google's fast and affordable thinking model with native multimodal support and a 1M token context window. Combines reasoning capabilities with exceptional speed and low cost. One of the best value models available for multimodal and long-context workloads.

Released 2025-10-08Knowledge cutoff: 2025-06
Medium confidence|Updated 57d ago|88% source confidence

Specifications

Context Window

1.0M tokens

Max Output

65.5K tokens

Input Price

$0.150 / 1M tokens

Output Price

$0.600 / 1M tokens

Latency Tier

Ultra Fast (speed score: 9.5/10)

Capability Profile

Speed
9.5/10
Long Context
9/10
Cost Efficiency
9/10
Multimodal
8.5/10
Structured Output
8/10
Instruction Following
8/10
Tool Use
8/10
Reasoning
7.5/10
Coding
7.5/10
Factuality
7.5/10
Safety & Enterprise
7.5/10
Conversational
7.5/10
Creativity
6.5/10

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 No
Realtime No
Computer Use No
Web Search No

Best Use Cases

High-volume multimodal processing — images, audio, video — at low cost
Long document analysis with 1M token context at budget pricing
Real-time applications needing multimodal understanding with low latency
Agentic workflows requiring speed and tool use at scale
Video and audio content analysis and summarization

Not Ideal For

The hardest reasoning or coding problems where Pro/frontier models are needed
Nuanced creative writing requiring depth
Enterprise deployments requiring the strongest safety alignment
Tasks where you need the absolute best structured output compliance

Strengths

Extraordinary value — multimodal + reasoning + 1M context at $0.15/$0.60
Built-in thinking mode brings reasoning to a mid-tier price point
Native video and audio understanding at Flash-tier pricing
Very fast inference even with thinking enabled
1M token context with reasonable recall quality

Weaknesses

Reasoning quality below Gemini 2.5 Pro on hard problems
Structured output compliance is good but not Claude-level
Thinking overhead can be wasteful on simple tasks
Safety filtering is less predictable than Anthropic models

Edge Cases & Notes

Thinking can be disabled via API for pure speed on simple tasks
Video analysis works well on short clips but degrades on long content
Quality at the extremes of 1M context is lower than Gemini 2.5 Pro
Free tier is available with generous rate limits for development

Provider Notes

The best value multimodal model in the market. Available through Gemini API and Vertex AI. Free tier available. Recommended as the default for cost-sensitive multimodal workloads.

Benchmarks

MMLU86.5%
HumanEval84.2%
Arena Elo1310

Benchmark Notes

MMLU-Pro 86.5%. Impressive for its price tier. Multimodal benchmarks are especially strong relative to cost. SWE-bench ~40%.

Research Meta

Last Evaluated

2026-04-01

Source Confidence

88%

Evaluation Method

LMSYS Arena, MMLU-Pro, multimodal evaluations, cost-quality Pareto analysis

Needs Re-evaluation

No

Sources

  • Google Gemini 2.5 Flash technical report
  • LMSYS Chatbot Arena
  • Artificial Analysis

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