Gemini 2.5 Flash Preview (Sep '25) (Reasoning)
Gemini 2.5 Flash Preview (Sep '25) (Reasoning) is an API model from Google. It’s positioned for general text tasks—work that benefits from iteration, not just one-shot answers.
Intelligence
Medium
Context
1M
Up to 1,000,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/M out).
Intelligence
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
Speed
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
Cost
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Context Window
1M tokens
≈ entire codebase
4k Chat 聊天
32k Long docs 长文档
128k Books 整本书
400k Multi-doc 多文档
1M This model 本模型
10M
Availability
API
Available
Product / App
Not available
Open Source
Not released
Enterprise
Contact sales
Pricing Model
Pay per token
Input and output billed separately.
Pay-per-token Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
Lower 20% Upper 80% This model
Context / memory
Context window size · log-scaled
6.0
9.0
9.0
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this
Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.
What it feels like
- Faster/cheaper Flash tier of Gemini 2.5.
- Long context is the headline — 91.5% on 128k and 83.1% on 1M long-context retrieval, ahead of every other frontier model
- Independent testers confirm reliable retrieval up to ~800K tokens; the last 200K degrades
- Genuine 'put a whole codebase or book in the prompt' workflow — not theatre
Best use cases
- Whole-codebase analysis, refactors across many files at once
- Long-form document QA (books, contracts, research papers)
- Multi-doc RAG replacement when context budget is generous
Tools to try
Not ideal for
- Pure agentic-coding leaderboards — Claude Opus 4.5 and DeepSeek V4 lead SWE-bench
- Cost-bound bulk inference past 200K tokens (price doubles)