--- title: "Gemini 2.5 Flash: Powerful AI Model for Diverse Tasks" canonical: https://telnyx.com/llm-library/gemini-2.5-flash --- # Gemini 2.5 Flash: Powerful AI Model for Diverse Tasks [Start building](https://telnyx.com/sign-up) ### Ask AI * [![GPT](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/chatgpt_icon-ea55e09d.jpeg?width=92&format=webp)](https://chat.openai.com/?q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) * [![Claude](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/claude_icon-c7c8eef3.webp?width=92&format=webp)](https://claude.ai/new?q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) * [![Perplexity](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/perplexity_icon-cd5b74f1.webp?width=92&format=webp)](https://www.perplexity.ai/search?q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) * [![Gemini](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/gemini_icon-4ff1cbf4.webp?width=92&format=webp)](https://www.google.com/search?udm=50&aep=11&q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) * [![Grok](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/grok_icon-e588e5bd.webp?width=92&format=webp)](https://x.com/i/grok?text=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) Gemini 2.5 Flash: Powerful AI Model for Diverse Tasks # Gemini-2.5-Flash Google's thinking model engineered for high-throughput tasks like summarization and data extraction, with controllable reasoning depth for accuracy and speed. [Start building](https://telnyx.com/sign-up)[GET Available Models](https://developers.telnyx.com/docs/inference/models) ## about Google's thinking model lets developers set a reasoning budget from 0 to 24,576 tokens per request, creating a [6x cost spread](https://deepmind.google/technologies/gemini/flash/) between instant responses at $0.60/1M output and full chain-of-thought at $3.50/1M. With thinking enabled, it scores 88.0% on AIME 2024 and 60.4% on SWE-bench Verified, while running at 214.7 tokens per second. Licensegoogle Context window(in thousands) ## Explore Our LLM Library Discover the power and diversity of large language models available with Telnyx. Explore the options below to find the perfect model for your project. ### DeepSeek-V4-Flash-0731 OrganizationTelnyx Model NameDeepSeek-V4-Flash-0731 Taskstext generation Languages SupportedEnglish Context Length1,048,576 Parameters284.0B Model Tierunlisted Licensemit ### DeepSeek-V4.1-Flash ### gemma-2b-it ### gemma-4-26B-A4B-it ### gemma-4-31B-it ### Llama-3.3-70B-Instruct ### Meta-Llama-3.1-70B-Instruct ### Meta-Llama-3.1-8B-Instruct ### MiniMax-M2.7 ### MiniMax-M3-MXFP8 ### Kimi-K2.5 ### Kimi-K2.6 ### Kimi-K3 ### personaplex-7b-v1 ### Laguna-S-2.1 ### Qwen3-235B-A22B ### Qwen3.8-27B ### GLM-5.1-FP8 ### GLM-5.2 ### GLM-5.3 ### GLM-5.3-Flash ### claude-haiku-4-5 ### gemini-2.5-flash ### gemini-3.7-flash ### gpt-oss-120b ### gpt-4.1 ### gpt-4o ### gpt-4o-mini ### gpt-5 ### gpt-5.1 ### gpt-5.2 ### gpt-5.4 ### gpt-5.4-mini ### gpt-5.6-luna ### gpt-5.6-sol ### gpt-live-1 HOW IT WORKS ## Selecting LLMs for Voice AI [GET Available Models](https://developers.telnyx.com/docs/inference/models) ### Create an account Sign-up to the Telnyx platform to get access to our suite of intuitive AI tools. ### Choose Gemini-2.5-Flash In the [AI Playground](https://portal.telnyx.com/#/app/next/ai/ai-playground "Telnyx AI Playground") select Gemini-2.5-Flash from the dropdown. ### Enter your API key If you’re using a proprietary model, you’ll need your API key before prompting the LLM. Our vast selection of OS models allow you to get started right away. ### Prompt the LLM Get responses from an LLM in minutes, and scale with our [OpenAI-compatible APIs](https://developers.telnyx.com/docs/inference/getting-started "Inference API Quickstart"). Step 1/4 **RESOURCES** ## Get started Check out our helpful tools to help get you started. * ### Test in the portal Easily browse and select your preferred model in the AI Playground. [Test today](https://portal.telnyx.com/#/ai/assistants) ## Sign up and start building [Sign up](https://telnyx.com/sign-up)[Contact sales](https://telnyx.com/contact-us) ## faqs ### Is Google Gemini 2.5 Flash free? Gemini 2.5 Flash is available with free usage limits through Google AI Studio. Production API access through [Vertex AI](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-5-flash) and inference providers involves usage-based pricing. ### Is the Gemini 2.5 Flash better? Gemini 2.5 Flash introduces a "thinking" mode for step-by-step reasoning, significantly improving performance on math, coding, and analysis tasks over 2.0 Flash. It is available through and other inference platforms. 1,048,576 ## Use cases for Gemini-2.5-Flash 1. **Budget-controlled deep reasoning:** A tunable thinking budget from 0 to 24,576 tokens creates a 6x cost spread per request, allowing developers to allocate reasoning depth per query based on complexity. 2. **High-throughput STEM processing:** Scoring 88.0% on AIME 2024 and 82.8% on GPQA Diamond at 214 tokens per second, it handles volume math, science, and coding tasks with strong accuracy. 3. **Multimodal data extraction:** Native text, vision, and audio input at $0.30 per million input tokens makes it cost-effective for processing mixed-media documents like PDFs with embedded images, charts, and tables. ## Quality Arena Elo1411 MMLUN/A MT BenchN/A Gemini 2.5 Flash scores 88.4% on Global-MMLU-Lite and 88.0% on AIME 2024 with its thinking budget enabled. Its Arena ELO of 1,411 places it above GPT-4.1 (1,413 is close) and near Qwen3 235B (1,422) on the same sheet. The controllable thinking budget from 0 to 24,576 tokens creates a 6x cost spread, allowing developers to trade reasoning depth for latency per request. **Qwen3 235B A22B** 1422 **gpt-4.1** 1413 **Gemini-2.5-Flash** 1411 **MiniMax-M2.5** 1406 **gpt-5-mini** 1390 ## pricing Running Gemini 2.5 Flash through Telnyx Inference costs $0.30 per million input tokens and $2.50 per million output tokens (non-thinking). Processing 1,000,000 reasoning tasks at 2,000 tokens each would cost approximately $2,800, with the tunable thinking budget allowing developers to reduce cost by up to 6x on simpler queries. ## What's Twitter saying? * **Controllable reasoning depth:** Developers highlight the [tunable thinking budget from 0 to 24,576 tokens](https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025/) as the standout feature, creating a 6x cost spread per request based on task complexity. * **Benchmark leader at speed:** Artificial Analysis ranks Gemini 2.5 Flash at [214 tokens per second with 88% on AIME 2024](https://artificialanalysis.ai/models/gemini-2-5-flash), placing it among the fastest reasoning models available. * **Cost-performance tradeoff:** Developers on r/LocalLLaMA note that the [$0.30 per million input tokens](https://www.reddit.com/r/LocalLLaMA/) pricing makes it competitive with open-weight models for high-volume reasoning workloads. | Organization | Model Name | Tasks | Languages Supported | Context Length | Parameters | Model Tier | License | | ------------ | ----------------------------------------------------------------------- | ---------------- | ------------------- | -------------- | ---------- | ---------- | ------------------------- | | Telnyx | DeepSeek-V4-Flash-0731 | text generation | English | 1,048,576 | 284.0B | unlisted | mit | | Telnyx | DeepSeek-V4.1-Flash | text generation | English | 1,048,576 | 552.0B | small | mit | | google | [gemma-2b-it](https://telnyx.com/llm-library/gemma-2b-it) | text generation | English | 8,192 | 2.5B | small | gemma | | google | gemma-4-26B-A4B-it | text to-text | Multilingual | 262,144 | 25.8B | small | apache-2.0 | | google | gemma-4-31B-it | text generation | Multilingual | 131,072 | 30.7B | small | apache-2.0 | | meta-llama | [Llama-3.3-70B-Instruct](https://telnyx.com/llm-library/llama-3.3-70b-instruct) | text generation | Multilingual | 99,000 | 70.6B | large | llama3.3 | | meta-llama | [Meta-Llama-3.1-70B-Instruct](https://telnyx.com/llm-library/meta-llama-3-1-70b-instruct) | text generation | Multilingual | 99,000 | 70.6B | large | llama3.1 | | meta-llama | [Meta-Llama-3.1-8B-Instruct](https://telnyx.com/llm-library/meta-llama-3.1-8b-instruct) | text generation | Multilingual | 131,072 | 8.0B | small | llama3.1 | | minimaxai | MiniMax-M2.7 | text generation | Multilingual | 200,000 | 0 | large | minimaxai | | minimaxai | MiniMax-M3-MXFP8 | text generation | Multilingual | 1,000,000 | 428.0B | small | minimax-community | | moonshotai | [Kimi-K2.5](https://telnyx.com/llm-library/kimi-k2.5) | text generation | Multilingual | 256,000 | 1.0T | large | modified-mit | | moonshotai | Kimi-K2.6 | text generation | Multilingual | 262,144 | 1.0T | medium | modified-mit | | moonshotai | Kimi-K3 | text generation | English | 1,000,000 | 2.8M | large | MIT Modified | | nvidia | personaplex-7b-v1 | speech to-speech | English | 3,000 | 7.0B | small | nvidia-open-model-license | | poolside | Laguna-S-2.1 | text generation | English | 1,048,576 | 117.6B | unlisted | OpenMDW-1.1 | | Qwen | [Qwen3-235B-A22B](https://telnyx.com/llm-library/qwen3-235b-a22b) | text generation | English | 32,768 | 235.1B | large | apache-2.0 | | Telnyx | Qwen3.8-27B | text generation | English | 262,144 | 27.0B | small | Apache 2.0 | | zai-org | GLM-5.1-FP8 | text generation | Multilingual | 202,752 | 753.9B | large | mit | | zai-org | GLM-5.2 | text generation | English | 1,000,000 | 753.9B | medium | MIT | | Telnyx | GLM-5.3 | text generation | English | 1,048,576 | 753.0B | medium | glm-5.3 | | zai-org | GLM-5.3-Flash | text generation | English | 1,048,576 | 320.0B | small | mit | | anthropic | [claude-haiku-4-5](https://telnyx.com/llm-library/claude-haiku-4-5) | text generation | Multilingual | 400,000 | 0 | large | openai | | google | [gemini-2.5-flash](https://telnyx.com/llm-library/gemini-2.5-flash) | text generation | Multilingual | 400,000 | 0 | large | openai | | google | gemini-3.7-flash | text generation | Multilingual | 400,000 | 0 | large | openai | | groq | [gpt-oss-120b](https://telnyx.com/llm-library/gpt-oss-120b) | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-4.1 | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | [gpt-4o](https://telnyx.com/llm-library/gpt-4-omni) | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | [gpt-4o-mini](https://telnyx.com/llm-library/gpt-4o-mini) | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5 | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.1 | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | [gpt-5.2](https://telnyx.com/llm-library/gpt-5.2) | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.4 | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.4-mini | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.6-luna | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.6-sol | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-live-1 | speech to-speech | Multilingual | 400,000 | 0 | large | openai | * ### Explore the docs Don’t wait to scale, start today with our public API endpoints. [Get started](https://developers.telnyx.com/api-reference/assistants/list-assistants) * ### Stay up to date Keep an eye on our AI changelog so you don't miss a beat. [See updates](https://telnyx.com/release-notes) Telnyx ### Is there a Gemini 2.5 Flash model? Yes, Gemini 2.5 Flash is Google's latest production model, optimized for speed and cost while supporting extended reasoning. It is documented in [Google's model reference](https://ai.google.dev/gemini-api/docs/models/gemini-2.5-flash). ### What is Gemini 2.5 Flash good for? Gemini 2.5 Flash excels at high-throughput production tasks including code generation, document analysis, and multimodal processing. Its thinking mode enables complex reasoning when needed while maintaining [low latency for simpler tasks](https://telnyx.com/resources/voice-ai-optimization-stack-templates). ### How does Gemini 2.5 Flash compare to GPT-4o? Gemini 2.5 Flash is competitive with GPT-4o on many benchmarks while being optimized for higher throughput. Its native multimodal capabilities and thinking mode make it strong for [real-time applications](https://telnyx.com/llm-library/gemini-2.5-flash). ### What is Gemini 2.5 Flash thinking mode? Thinking mode enables Gemini 2.5 Flash to reason step-by-step before generating a response, improving accuracy on complex problems. It can be toggled on or off through [the API](https://telnyx.com/llm-library/gemini-2.5-flash), allowing you to trade latency for quality.