Mistral Small 4 vs GPT-4.1 mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Mistral AI 的 Mistral Small 4 与 OpenAI 的 GPT-4.1 mini 是当前备受关注的两款主力 AI 模型。Mistral Small 4 提供 256K Token 上下文窗口，API 定价为每百万 Token 输入 $0.10 / 输出 $0.30；而 GPT-4.1 mini 提供 1.05M Token 上下文窗口，定价为每百万 Token 输入 $0.40 / 输出 $1.60。以下为两者的基准测试、延迟与成本全面对比。
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Mistral Small 4
超高速低延迟推理Mistral AI 推出的 Mistral Small 4 是一款面向实时交互对话、高并发路由与极低延迟生产环境的超高速大模型，具备 256K 上下文窗口、120 Token/秒生成速度与 48/100 综合智能指数（每百万 Token $0.1 输入 / $0.3 输出）。
$0.10 / 1M
$0.30 / 1M
256K tokens
44.5%
GPT-4.1 mini
轻量级高性价比模型OpenAI 推出的 GPT-4.1 mini 是一款兼顾极低 API 调用成本与高吞吐效率的轻量级大模型，具备 1.0M 上下文窗口、165 Token/秒生成速度与 36/100 综合智能指数（每百万 Token $0.4 输入 / $1.6 输出）。
$0.40 / 1M
$1.60 / 1M
1.05M tokens
36.5%
核心差异一览
- Mistral Small 4 is 4.7x cheaper for API usage (input + output tokens combined).
- GPT-4.1 mini has a larger context window (1M vs 256K tokens).
- GPT-4.1 mini is faster at 165 tok/s vs 120 tok/s.
- Mistral Small 4 leads on SWE-bench coding (44.5% vs 36.5%).
| Feature | Mistral Small 4 | GPT-4.1 mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 36 |
Provider | Mistral AI | OpenAI |
Input Price Cost per million input tokens | $0.10 / 1M | $0.40 / 1M |
Output Price Cost per million output tokens | $0.30 / 1M | $1.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.07 / 1M | $0.31 / 1M |
Context Window Maximum input + output tokens | 256K tokens~384 A4 pages of text | 1.05M tokens~1,571 A4 pages of text |
Max Output Tokens Per single request | 16K | 33K |
Throughput Speed Tokens generated per second | 120 tok/s | 165 tok/s |
SWE-bench Verified Real-world software engineering | 44.5% | 36.5% |
GPQA Diamond Graduate-level reasoning | 56% | 49.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $0.10 / 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.
Mistral Small 4 · Strengths & Key Advantages
- 提供 256K 上下文窗口与最高 16K 输出容量，核心基准表现优异（SWE-bench Verified 44.5% · GPQA Diamond 56%）
- 高达 120 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 340 毫秒
- 开放模型权重 (24B)，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
GPT-4.1 mini · Strengths & Key Advantages
- 提供 1.0M 上下文窗口与最高 33K 输出容量，核心基准表现优异（SWE-bench Verified 36.5% · GPQA Diamond 49.5%）
- 高达 165 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 300 毫秒
- 极具竞争力的 API 定价（每百万 Token $0.4 输入 / $1.6 输出），原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
- 闭源专有云端模型，不支持下载权重进行离线本地物理隔离部署
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Mistral Small 4 | GPT-4.1 mini |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.12 | $0.14 |
Output Tokens / Task Tokens generated per task | 28K | 48K |
Reasoning Tokens / Task Thinking tokens burned per task | 12K | 0K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Mistral Small 4 | GPT-4.1 mini |
|---|---|---|
| Official API Model ID (SDK) | mistral-small-4-latest | gpt-4-1-mini |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-03 | 2025-04 |
| Knowledge Cutoff | January 2026 | June 2024 |
| Architecture | Next-Gen Compact Multimodal Transformer | Dense multimodal transformer (small) |
| Parameters | 24B | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.1/1M in) across full 256K window | Flat rate ($0.4/1M in) across full 1048K window |
| Prompt Cache Policy & TTL | Cached input at $0.03/1M tokens | Automatic prefix caching at $0.1/1M (no extra cache-write fee) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 15 GB VRAM (4-bit Quantized) · 52 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
Mistral Small 4 vs GPT-4.1 mini: 常见问题解答 (FAQ)
Which is more intelligent, Mistral Small 4 or GPT-4.1 mini?
Mistral Small 4 is more intelligent. It scores 48 compared with 36 on the composite Intelligence Index.
Which model is faster?
GPT-4.1 mini is faster, generating 165 tokens per second compared with 120 tokens per second.
Which model is cheaper to run?
Mistral Small 4 is cheaper at $0.07 per 1M tokens blended, compared with $0.31 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-4.1 mini has lower latency, with a time to first token of 0.30s compared with 0.34s.
Which model has a larger context window?
GPT-4.1 mini has a larger context window, supporting 1.05M tokens compared with 256K tokens.
综合结论
综合来看，Mistral Small 4（适合：面向实时交互对话、高并发路由与极低延迟生产环境的超高速任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）与 GPT-4.1 mini（适合：兼顾极低 API 调用成本与高吞吐效率的轻量级任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）各具优势：Mistral Small 4 在综合智能与编程基准测试中表现更强，GPT-4.1 mini 拥有更快的 Token 生成速度，而 Mistral Small 4 则在规模化 API 部署中具备更高的成本效益。
常见问题 (FAQ)
在编程和深度推理方面，Mistral Small 4 和 GPT-4.1 mini 哪个更强？+
在综合智能指数与编程基准评测中，Mistral Small 4 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。
Mistral Small 4 与 GPT-4.1 mini 的 API 价格哪个更便宜？+
Mistral Small 4 的 API 价格为输入 $0.10 / 输出 $0.30（每百万 Token），GPT-4.1 mini 为输入 $0.40 / 输出 $1.60。综合混合成本来看，Mistral Small 4 更具性价比。
哪款模型拥有更大的上下文窗口和更快的响应速度？+
Mistral Small 4 支持 256K Token 上下文窗口，而 GPT-4.1 mini 支持 1.05M Token。在流式生成速度方面，GPT-4.1 mini 的吞吐表现更快。
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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- Mistral Small 4 vs Claude Haiku 5.5：基准测试、API 价格与速度全面对比 (2026)对比
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Mistral Small 4：基准测试、API 价格与速度全面对比 (2026)对比
- Nemotron Cascade 2 30B A3B vs Mistral Small 4：基准测试、API 价格与速度全面对比 (2026)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: Mistral AI 与 OpenAI 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。