Google 发布了 Gemini 2.5 Flash 和 Gemini 2.5 Flash-Lite 预览模型的更新版本，在 AI Studio 和 Vertex AI 上提供。这些更新包括改进的代理工具使用、更高效的推理（Flash 模型），以及针对更严格指令遵循、减少冗余输出和增强多模态/翻译能力（Flash-Lite 模型）的优化。外部基准测试显示，Gemini 2.5 Flash-Lite 在速度和效率方面取得了显著提升，成为目前最快的专有模型之一。Google 还引入了滚动别名（如 gemini-flash-latest）方便用户快速迭代，但建议在生产环境中使用固定模型字符串以确保稳定性。新模型在长上下文处理、工具调用和成本效益方面也带来了改进。

✨ **模型性能提升**：Gemini 2.5 Flash 在代理工具使用和多轮推理方面得到增强，SWE-Bench Verified 分数显著提高，表明其在长距离规划和代码导航能力上有所突破。Gemini 2.5 Flash-Lite 则专注于更精准的指令遵循，减少输出冗余，并加强了多模态和翻译功能，内部测试显示其输出 token 数量大幅减少。

🚀 **速度与效率突破**：外部独立基准测试显示，Gemini 2.5 Flash-Lite 预览版（09-2025）在 AI Studio 的吞吐量测试中，输出速度高达约 887 token/s，成为其追踪的最快专有模型。同时，Gemini 2.5 Flash 的输出 token 减少了约 24%，Flash-Lite 减少了约 50%，这直接降低了输出 token 的成本和处理时间，尤其是在吞吐量受限的服务中。

💡 **成本与上下文优化**：Gemini 2.5 Flash-Lite 的 GA 基础价格为每 100 万输入 token 0.10 美元，每 100 万输出 token 0.40 美元。其支持高达 100 万 token 的上下文窗口，并具备可配置的“思考预算”和工具连接能力（如搜索 grounding、代码执行），非常适合需要交错阅读、规划和多工具调用的代理系统。

🔄 **别名管理与生产建议**：Google 推出了 `gemini-flash-latest` 和 `gemini-flash-lite-latest` 等滚动别名，方便用户快速接入最新预览版。但为确保生产环境的稳定性和可预测性，Google 建议用户固定使用特定的模型字符串（如 `gemini-2.5-flash`），并在切换别名时注意可能的变化。Google 会提前两周通过电子邮件通知别名指向的更新。

Google released an updated version of Gemini 2.5 Flash and Gemini 2.5 Flash-Lite preview models across AI Studio and Vertex AI, plus rolling aliases—gemini-flash-latest and gemini-flash-lite-latest—that always point to the newest preview in each family. For production stability, Google advises pinning fixed strings (gemini-2.5-flash, gemini-2.5-flash-lite). Google will give a two-week email notice before retargeting a -latest alias, and notes that rate limits, features, and cost may vary across alias updates.

What actually changed?

Flash: Improved agentic tool use and more efficient “thinking” (multi-pass reasoning). Google reports a +5 point lift on SWE-Bench Verified vs. the May preview (48.9% → 54.0%), indicating better long-horizon planning/code navigation.Flash-Lite: Tuned for stricter instruction following, reduced verbosity, and stronger multimodal/translation. Google’s internal chart shows ~50% fewer output tokens for Flash-Lite and ~24% fewer for Flash, which directly cuts output-token spend and wall-clock time in throughput-bound services.

Independent Stats from the community thread

Artificial Analysis (the account behind the AI benchmarking site) received pre-release access and published external measurements across intelligence and speed. Highlights from the thread and companion pages:

Throughput: In endpoint tests, Gemini 2.5 Flash-Lite (Preview 09-2025, reasoning) is reported as the fastest proprietary model they track, around ~887 output tokens/s on AI Studio in their setup. Intelligence index deltas: The September previews for Flash and Flash-Lite improve on Artificial Analysis’ aggregate “intelligence” scores compared with prior stable releases (site pages break down reasoning vs. non-reasoning tracks and blended price assumptions). Token efficiency: The thread reiterates Google’s own reduction claims (−24% Flash, −50% Flash-Lite) and frames the win as cost-per-success improvements for tight latency budgets.

Google shared pre-release access for the new Gemini 2.5 Flash & Flash-Lite Preview 09-2025 models. We’ve independently benchmarked gains in intelligence (particularly for Flash-Lite), output speed and token efficiency compared to predecessors

Key takeaways from our intelligence… pic.twitter.com/ybzKvZBH5A

— Artificial Analysis (@ArtificialAnlys) September 25, 2025

Cost surface and context budgets (for deployment choices)

Flash-Lite GA list price is $0.10 / 1M input tokens and $0.40 / 1M output tokens (Google’s July GA post and DeepMind’s model page). That baseline is where verbosity reductions translate to immediate savings.Context: Flash-Lite supports ~1M-token context with configurable “thinking budgets” and tool connectivity (Search grounding, code execution)—useful for agent stacks that interleave reading, planning, and multi-tool calls.

Browser-agent angle and the o3 claim

A circulating claim says the “new Gemini Flash has o3-level accuracy, but is 2× faster and 4× cheaper on browser-agent tasks.” This is community-reported, not in Google’s official post. It likely traces to private/limited task suites (DOM navigation, action planning) with specific tool budgets and timeouts. Use it as a hypothesis for your own evals; don’t treat it as a cross-bench truth.

This is insane! The new Gemini Flash model released yesterday has the same accuracy as o3, but it is 2x faster and 4x cheaper for browser agent tasks.

I ran evaluations the whole day and could not believe this. The previous gemini-2.5-flash had only 71% on this benchmark. https://t.co/KdgkuAK30W pic.twitter.com/F69BiZHiwD

— Magnus Müller (@mamagnus00) September 26, 2025

Practical guidance for teams

Pin vs. chase -latest: If you depend on strict SLAs or fixed limits, pin the stable strings. If you continuously canary for cost/latency/quality, the -latest aliases reduce upgrade friction (Google provides two weeks’ notice before switching the pointer). High-QPS or token-metered endpoints: Start with Flash-Lite preview; the verbosity and instruction-following upgrades shrink egress tokens. Validate multimodal and long-context traces under production load.Agent/tool pipelines: A/B Flash preview where multi-step tool use dominates cost or failure modes; Google’s SWE-Bench Verified lift and community tokens/s figures suggest better planning under constrained thinking budgets.

Model strings (current)

Previews: gemini-2.5-flash-preview-09-2025, gemini-2.5-flash-lite-preview-09-2025Stable: gemini-2.5-flash, gemini-2.5-flash-liteRolling aliases: gemini-flash-latest, gemini-flash-lite-latest (pointer semantics; may change features/limits/pricing).

Summary

Google’s new release update tightens tool-use competence (Flash) and token/latency efficiency (Flash-Lite) and introduces -latest aliases for faster iteration. External benchmarks from Artificial Analysis indicate meaningful throughput and intelligence-index gains for the Sept 2025. previews, with Flash-Lite now testing as the fastest proprietary model in their harness. Validate on your workload—especially browser-agent stacks—before committing to the aliases in production.

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