Google • LLM#337 of 579 in intelligence
Google: Gemini 2.0 Flash by Google
Context Window
1.0M tokens
Input Price/1M
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#1 of 714
Output Price/1M
—
Parameters
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Max Output
8K tokens
Gemini 2.0 Flash results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Terminal-Bench Hard | 4.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| SciCode | 33.0 | 100.0 | — |
| LiveCodeBench | 33.0 | 100.0 | Artificial Analysis official API |
| AA Coding Index | 24.1 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU-Pro | 78.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA-LCR | 28.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MATH-500 | 48.0 | 100.0 | Artificial Analysis official API |
| AIME 2025 | 22.0 | 100.0 | Artificial Analysis official API |
| AA Math Index | 21.7 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Intelligence Index | 12.2 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU Pro | 79.8 | 100.0 | Artificial Analysis official API |
| GPQA Diamond | 62.0 | 100.0 | Artificial Analysis official API |
| IFBench | 40.0 | 100.0 | — |
| HLE | 5.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Tau²-Bench | 30.0 | 100.0 | — |
Gemini 2.0 Flash is an AI model developed by Google, classified as a large language model (LLM). It is a multimodal model, capable of processing text, images, and potentially other media types. As a proprietary model, it is available via Google's cloud API. With a context window of 1.0M tokens, it is suitable for processing long documents such as contracts, books, and complete codebases.
Gemini 2.0 Flash does not have public per-token pricing available at this time. Some models offer access via enterprise plans or research programs. Check Google's official website for up-to-date availability and pricing.
Gemini 2.0 Flash was evaluated on 15 different benchmarks, covering categories like Agentic, Coding, Knowledge, Long Context, Math, overall, Reasoning, Tool Use. Results show moderate performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
Gemini 2.0 Flash is suitable for a wide range of AI applications: long document analysis (contracts, legal proceedings, codebases), multimodal processing combining text and images, text generation, summarization, translation, and general assistance.
In the 2026 AI model ecosystem, Gemini 2.0 Flash competes directly with similarly capable models. Key competitors include GPT (OpenAI), Claude (Anthropic), and open source models like Llama (Meta) and Qwen (Alibaba). The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
Gemini 2.0 Flash is an AI model developed by Google. It is a language model (LLM), with multimodal support (text, image and more).
Gemini 2.0 Flash does not have public per-token pricing available at this time. Check Google's official website for up-to-date information.
In available benchmarks, Gemini 2.0 Flash scored: Terminal-Bench Hard: 4/100, SciCode: 33/100, LiveCodeBench: 33/100. See the full table above for a detailed comparison.
No, Gemini 2.0 Flash is a proprietary model from Google. It is available via cloud API. For open source alternatives, check our open source model ranking.
Gemini 2.0 Flash excels at general-purpose language tasks. With its large context window, it handles long documents, codebases, and extended conversations.
Last updated: September 01, 2026 • View methodology →