DeepSeek V4.1-Flash vs GLM-5.2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
DeepSeek 的 DeepSeek V4.1-Flash 与 Zhipu AI 的 GLM-5.2 是当前备受关注的两款主力 AI 模型。DeepSeek V4.1-Flash 提供 1M Token 上下文窗口，API 定价为每百万 Token 输入 $0.30 / 输出 $1.20；而 GLM-5.2 提供 1M Token 上下文窗口，定价为每百万 Token 输入 $0.00 / 输出 $0.00。以下为两者的基准测试、延迟与成本全面对比。
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DeepSeek V4.1-Flash
开源权重基础大模型DeepSeek 推出的 DeepSeek V4.1-Flash 是一款支持私有云服务器自托管、微调与完全数据主权的开源权重基础大模型，具备 1M 上下文窗口、110 Token/秒生成速度与 58/100 综合智能指数（每百万 Token $0.3 输入 / $1.2 输出）。
$0.30 / 1M
$1.20 / 1M
1M tokens
74.2%
GLM-5.2
开源权重基础大模型Zhipu AI 推出的 GLM-5.2 是一款支持私有云服务器自托管、微调与完全数据主权的开源权重基础大模型，具备 1M 上下文窗口、60 Token/秒生成速度与 91/100 综合智能指数（免费开源权重）。
$0.00 / 1M
$0.00 / 1M
1M tokens
67.5%
核心差异一览
- GLM-5.2 is 1.0x cheaper for API usage (input + output tokens combined).
- DeepSeek V4.1-Flash is faster at 110 tok/s vs 60 tok/s.
- DeepSeek V4.1-Flash leads on SWE-bench coding (74.2% vs 67.5%).
| Feature | DeepSeek V4.1-Flash | GLM-5.2 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 58 | 91 |
Provider | DeepSeek | Zhipu AI |
Input Price Cost per million input tokens | $0.30 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $1.20 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.18 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 384K | 32K |
Throughput Speed Tokens generated per second | 110 tok/s | 60 tok/s |
SWE-bench Verified Real-world software engineering | 74.2% | 67.5% |
GPQA Diamond Graduate-level reasoning | 90.9% | 73.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.00 / 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.
DeepSeek V4.1-Flash · Strengths & Key Advantages
- 提供 1M 上下文窗口与最高 384K 输出容量，核心基准表现优异（SWE-bench Verified 74.2% · GPQA Diamond 90.9%）
- 高达 110 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 460 毫秒
- 开放模型权重 (763B total (8B prefill / 16B decode ))，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
GLM-5.2 · Strengths & Key Advantages
- 提供 1M 上下文窗口与最高 32K 输出容量，核心基准表现优异（SWE-bench Verified 67.5% · GPQA Diamond 73.5%）
- 高达 60 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 650 毫秒
- 开放模型权重 (350B MoE)，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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | DeepSeek V4.1-Flash | GLM-5.2 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.27 | $0.00 |
Output Tokens / Task Tokens generated per task | 58K | 30K |
Reasoning Tokens / Task Thinking tokens burned per task | 28K | 12K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | DeepSeek V4.1-Flash | GLM-5.2 |
|---|---|---|
| Official API Model ID (SDK) | deepseek-reasoner | glm-5-2 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-09 | 2026-06 |
| Knowledge Cutoff | June 2026 | February 2026 |
| Architecture | Causal Encoder-Decoder (20 enc + 20 dec layers) | Massive Million-Token Open MoE Frontier Transformer |
| Parameters | 763B total (8B prefill / 16B decode active) | 350B MoE |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($0.3/1M in) across full 1000K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.006/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 473 GB VRAM (4-bit Quantized) · 1640 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 217 GB VRAM (4-bit Quantized) · 753 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
DeepSeek V4.1-Flash vs GLM-5.2: 常见问题解答 (FAQ)
Which is more intelligent, DeepSeek V4.1-Flash or GLM-5.2?
GLM-5.2 is more intelligent. It scores 91 compared with 58 on the composite Intelligence Index.
Which model is faster?
DeepSeek V4.1-Flash is faster, generating 110 tokens per second compared with 60 tokens per second.
Which model is cheaper to run?
GLM-5.2 is cheaper at $0.00 per 1M tokens blended, compared with $0.18 (7:2:1 cache:input:output ratio).
Which model has lower latency?
DeepSeek V4.1-Flash has lower latency, with a time to first token of 0.46s compared with 0.65s.
综合结论
综合来看，DeepSeek V4.1-Flash（适合：支持私有云服务器自托管、微调与完全数据主权的开源权重基础任务、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 GLM-5.2（适合：支持私有云服务器自托管、微调与完全数据主权的开源权重基础任务、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：GLM-5.2 在综合智能与编程基准测试中表现更强，DeepSeek V4.1-Flash 拥有更快的 Token 生成速度，而 GLM-5.2 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，DeepSeek V4.1-Flash 和 GLM-5.2 哪个更强？+
在综合智能指数与编程基准评测中，GLM-5.2 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。
DeepSeek V4.1-Flash 与 GLM-5.2 的 API 价格哪个更便宜？+
DeepSeek V4.1-Flash 的 API 价格为输入 $0.30 / 输出 $1.20（每百万 Token），GLM-5.2 为输入 $0.00 / 输出 $0.00。综合混合成本来看，GLM-5.2 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？+
DeepSeek V4.1-Flash 支持 1M Token 上下文窗口，而 GLM-5.2 支持 1M Token。在流式生成速度方面，DeepSeek V4.1-Flash 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.
Related reading
- DeepSeek V4.1-Flash vs Gemini 4 Argon：基准测试、API 价格与速度全面对比 (2026)对比
- GLM-5.1 vs GLM-5.2：基准测试、API 价格与速度全面对比 (2026)对比
- GPT-6 Luna vs DeepSeek V4.1-Flash：基准测试、API 价格与速度全面对比 (2026)对比
- Grok 4.7 vs DeepSeek V4.1-Flash：基准测试、API 价格与速度全面对比 (2026)对比
- Kimi K3 vs GLM-5.2：基准测试、API 价格与速度全面对比 (2026)对比
- Mistral Medium 3.5 vs DeepSeek V4.1-Flash：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: DeepSeek 与 Zhipu AI 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。