Ministral 3B vs Gemini 1.5 Flash-8B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Ministral 3B by Mistral AI and Gemini 1.5 Flash-8B by Google 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, Gemini 1.5 Flash-8B holds a notable pricing advantage for token ingestion. On output generation, Ministral 3B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Gemini 1.5 Flash-8B provides an expanded memory horizon with 1,049K tokens. In latency-sensitive production environments, Ministral 3B 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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Ministral 3B
高性能 AI 模型Mistral AI 推出的 Ministral 3B 人工智能模型，具备 128K 上下文窗口，输入价格为 $0.04/百万 Token，输出价格为 $0.04/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.04 / 1M
$0.04 / 1M
128K tokens
22.1%
Gemini 1.5 Flash-8B
高性能 AI 模型Google 推出的 Gemini 1.5 Flash-8B 人工智能模型，具备 1.0M 上下文窗口，输入价格为 $0.0375/百万 Token，输出价格为 $0.15/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.04 / 1M
$0.15 / 1M
1.05M tokens
24.5%
核心差异一览
- Ministral 3B is 1.6x cheaper for API usage (input + output tokens combined).
- Gemini 1.5 Flash-8B has a larger context window (1M vs 128K tokens).
- Ministral 3B is faster at 180 tok/s vs 160 tok/s.
- Gemini 1.5 Flash-8B leads on SWE-bench coding (24.5% vs 22.1%).
| Feature | Ministral 3B | Gemini 1.5 Flash-8B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 33 | 37 |
Provider | Mistral AI | |
Input Price Cost per million input tokens | $0.04 / 1M | $0.04 / 1M |
Output Price Cost per million output tokens | $0.04 / 1M | $0.15 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.03 / 1M | $0.03 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 1.05M tokens~1,573 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
Throughput Speed Tokens generated per second | 180 tok/s | 160 tok/s |
SWE-bench Verified Real-world software engineering | 22.1% | 24.5% |
GPQA Diamond Graduate-level reasoning | 32.5% | 38.2% |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 1M | $0.01 / 1M |
各项指标谁更胜一筹？
At-a-glance decision framework权威基准测试得分对比
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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Ministral 3B · Strengths & Key Advantages
- 由 Mistral AI 研发的高精度 Ultra-Compact Dense Transformer 架构，支持 128K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.04/1M，输出 $0.04/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Gemini 1.5 Flash-8B · Strengths & Key Advantages
- 由 Google 研发的高精度 High-Throughput Multimodal Transformer 架构，支持 1.0M 上下文窗口
- 极具竞争力的 API 定价（输入 $0.0375/1M，输出 $0.15/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Ministral 3B | Gemini 1.5 Flash-8B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.03 | $0.04 |
Output Tokens / Task Tokens generated per task | 15K | 18K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Ministral 3B | Gemini 1.5 Flash-8B |
|---|---|---|
| Official API Model ID (SDK) | ministral-3b-latest | models/gemini-1-5-flash-8b |
| API Protocol | Mistral AI REST API (OpenAI-Compatible) | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2024-10 | 2024-10 |
| Knowledge Cutoff | October 2024 | May 2024 |
| Architecture | Ultra-Compact Dense Transformer | High-Throughput Multimodal Transformer |
| Parameters | 3B | 8B |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.04/1M in) across full 128K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.02/1M tokens | Implicit + Explicit Context Caching at $0.01/1M with configurable TTL |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| 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 | Mistral AI Production Safety & Alignment Verified | Google DeepMind Frontier Safety Framework 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 | text, vision, audio, video |
| Output Modalities | text | text |
Ministral 3B vs Gemini 1.5 Flash-8B: 常见问题解答 (FAQ)
Which is more intelligent, Ministral 3B or Gemini 1.5 Flash-8B?
Gemini 1.5 Flash-8B is more intelligent. It scores 37 compared with 33 on the composite Intelligence Index.
Which model is faster?
Ministral 3B is faster, generating 180 tokens per second compared with 160 tokens per second.
Which model has lower latency?
Ministral 3B has lower latency, with a time to first token of 0.21s compared with 0.25s.
Which model has a larger context window?
Gemini 1.5 Flash-8B has a larger context window, supporting 1.05M tokens compared with 128K tokens.
综合结论
Verdict & Model Selection Recommendation: Choose Ministral 3B if your priority is Smartphone native apps, IoT controllers, and instant prompt classification. Its performance in demanding tasks justifies its operational token cost. Choose Gemini 1.5 Flash-8B if your workflow prioritizes Large-scale multimodal ingestion, video summarization, real-time transcription parsing, and high-frequency classification. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
常见问题 (FAQ)
How does Ministral 3B compare to Gemini 1.5 Flash-8B in API token pricing and cost per token?+
Ministral 3B costs $0.04 per 1M input tokens and $0.04 per 1M output tokens, whereas Gemini 1.5 Flash-8B costs $0.04 input and $0.15 output. Overall, Gemini 1.5 Flash-8B 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: Ministral 3B or Gemini 1.5 Flash-8B?+
Ministral 3B supports a context window of 128K tokens (~96k words), while Gemini 1.5 Flash-8B supports 1,049K tokens (~786k words). Therefore, Gemini 1.5 Flash-8B can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Ministral 3B or Gemini 1.5 Flash-8B?+
Ministral 3B scores 22.1% on SWE-bench Verified while Gemini 1.5 Flash-8B scores 24.5%. For autonomous software engineering and multi-file debugging, Gemini 1.5 Flash-8B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Ministral 3B and Gemini 1.5 Flash-8B?+
Ministral 3B is optimized for low-latency direct generation. In contrast, Gemini 1.5 Flash-8B focuses on standard fast inference.
How does inference speed (tokens per second) compare between Ministral 3B and Gemini 1.5 Flash-8B?+
Ministral 3B generates approximately 180 tokens per second, while Gemini 1.5 Flash-8B delivers 160 tokens per second. In latency-sensitive production environments, Ministral 3B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Ministral 3B and Gemini 1.5 Flash-8B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemini 1.5 Flash-8B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Ministral 3B. 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
- Gemini 1.5 Flash-8B vs Ministral 8B: Benchmarks, Pricing & Speed Comparison对比
- Gemini 2.0 Flash-Lite vs Gemini 1.5 Flash-8B: Benchmarks, Pricing & Speed Comparison对比
- Ministral 8B vs Ministral 3B: Benchmarks, Pricing & Speed Comparison对比
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- Aion-3.0 vs Gemini 3.6 Flash: Benchmarks, Pricing & Speed Comparison对比
- Aion-Mini vs Laguna XS 2.1: Benchmarks, Pricing & Speed Comparison对比
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.
数据来源: GetInfoUs Independent AI Benchmarks & Official API Specs