Gemini 2.5 Flash vs GLM-4.5-Flash
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Gemini 2.5 Flash by Google and GLM-4.5-Flash by Zhipu AI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, GLM-4.5-Flash holds a notable pricing advantage for token ingestion. On output generation, GLM-4.5-Flash is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Gemini 2.5 Flash provides an expanded memory horizon with 1,049K tokens. In latency-sensitive production environments, GLM-4.5-Flash leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Gemini 2.5 Flash
高度な推論モデルGoogle が提供する Gemini 2.5 Flash は、最大 1.0M トークンのコンテキストウィンドウを備え、入力 $0.3/100万トークン・出力 $2.5/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.30 / 1M
$2.50 / 1M
1.05M tokens
58.2%
GLM-4.5-Flash
高度な推論モデルZhipu AI が提供する GLM-4.5-Flash は、最大 131K トークンのコンテキストウィンドウを備え、入力 $0.01/100万トークン・出力 $0.02/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.01 / 1M
$0.02 / 1M
131.1K tokens
N/A
ひと目でわかる主な違い
- GLM-4.5-Flash is 81.5x cheaper for API usage (input + output tokens combined).
- Gemini 2.5 Flash has a larger context window (1M vs 131K tokens).
- GLM-4.5-Flash is faster at 170 tok/s vs 160 tok/s.
| Feature | Gemini 2.5 Flash | GLM-4.5-Flash |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 68 | 72 |
Provider | Zhipu AI | |
Input Price Cost per million input tokens | $0.30 / 1M | $0.01 / 1M |
Output Price Cost per million output tokens | $2.50 / 1M | $0.02 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.36 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,573 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 66K | 8K |
Throughput Speed Tokens generated per second | 160 tok/s | 170 tok/s |
SWE-bench Verified Real-world software engineering | 58.2% | N/A |
GPQA Diamond Graduate-level reasoning | 75.4% | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.07 / 1M | $0.00 / 1M |
分野別勝者スコアカード
At-a-glance decision frameworkEvaluations ongoing
検証済みベンチマークスコア比較
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Tokens emitted per second during active generation streaming.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini 2.5 Flash · Strengths & Key Advantages
- Google の高度な High-throughput sparse multimodal transformer with thinking mode アーキテクチャと 1.0M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0.3/1M、出力 $2.5/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
GLM-4.5-Flash · Strengths & Key Advantages
- Zhipu AI の高度な Ultra-High-Speed Distilled Transformer アーキテクチャと 131K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0.01/1M、出力 $0.02/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini 2.5 Flash | GLM-4.5-Flash |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.85 | $0.01 |
Output Tokens / Task Tokens generated per task | 32K | 10K |
Reasoning Tokens / Task Thinking tokens burned per task | 14K | 0K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini 2.5 Flash | GLM-4.5-Flash |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-2-5-flash | glm-4-5-flash |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Zhipu AI REST API (OpenAI-Compatible) |
| Release Date | 2025-06 | 2025-07 |
| Knowledge Cutoff | March 2025 | May 2025 |
| Architecture | High-throughput sparse multimodal transformer with thinking mode | Ultra-High-Speed Distilled Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Flat rate ($0.01/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0.075/1M with configurable TTL | Cached input at $0.001/1M tokens |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | API Prompt Engineering & RAG Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Google DeepMind Frontier Safety Framework Verified | Zhipu AI Production Safety & Alignment Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision, audio, video | text |
| Output Modalities | text | text |
Gemini 2.5 Flash vs GLM-4.5-Flash: よくある質問 (FAQ)
Which is more intelligent, Gemini 2.5 Flash or GLM-4.5-Flash?
GLM-4.5-Flash is more intelligent. It scores 72 compared with 68 on the composite Intelligence Index.
Which model is faster?
GLM-4.5-Flash is faster, generating 170 tokens per second compared with 160 tokens per second.
Which model is cheaper to run?
GLM-4.5-Flash is cheaper at $0.00 per 1M tokens blended, compared with $0.36 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GLM-4.5-Flash has lower latency, with a time to first token of 0.22s compared with 0.45s.
Which model has a larger context window?
Gemini 2.5 Flash has a larger context window, supporting 1.05M tokens compared with 131.1K tokens.
総合評価
Verdict & Model Selection Recommendation: Choose Gemini 2.5 Flash if your priority is Production web applications, interactive agents, and multimodal media indexing. Its performance in demanding tasks justifies its operational token cost. Choose GLM-4.5-Flash if your workflow prioritizes High-frequency classification, customer chatbots, translation, and automated web scrapers. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
よくある質問 (FAQ)
How does Gemini 2.5 Flash compare to GLM-4.5-Flash in API token pricing and cost per token?+
Gemini 2.5 Flash costs $0.30 per 1M input tokens and $2.50 per 1M output tokens, whereas GLM-4.5-Flash costs $0.01 input and $0.02 output. Overall, GLM-4.5-Flash is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Gemini 2.5 Flash or GLM-4.5-Flash?+
Gemini 2.5 Flash supports a context window of 1,049K tokens (~786k words), while GLM-4.5-Flash supports 131K tokens (~98k words). Therefore, Gemini 2.5 Flash can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Gemini 2.5 Flash or GLM-4.5-Flash?+
Gemini 2.5 Flash scores 58.2% on SWE-bench Verified, while GLM-4.5-Flash scores competitively for software generation. For complex autonomous debugging and agentic engineering, Gemini 2.5 Flash provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Gemini 2.5 Flash and GLM-4.5-Flash?+
Gemini 2.5 Flash features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GLM-4.5-Flash provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Gemini 2.5 Flash and GLM-4.5-Flash?+
Gemini 2.5 Flash generates approximately 160 tokens per second, while GLM-4.5-Flash delivers 170 tokens per second. In latency-sensitive production environments, GLM-4.5-Flash provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Gemini 2.5 Flash and GLM-4.5-Flash to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GLM-4.5-Flash to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Gemini 2.5 Flash. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
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