DeepSeek V4.1-Flash vs GLM-5.2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
DeepSeek V4.1-Flash by DeepSeek and GLM-5.2 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-5.2 holds a notable pricing advantage for token ingestion. On output generation, GLM-5.2 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 1,000K tokens. In latency-sensitive production environments, DeepSeek V4.1-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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DeepSeek V4.1-Flash
Open-Weights-ModellDeepSeek V4.1-Flash von DeepSeek ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 1M Token zu API-Preisen von $0.3/1M Input- und $1.2/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.30 / 1M
$1.20 / 1M
1M tokens
74.2%
GLM-5.2
Open-Weights-ModellGLM-5.2 von Zhipu AI ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 1M Token zu API-Preisen von $0/1M Input- und $0/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.00 / 1M
$0.00 / 1M
1M tokens
67.5%
Wichtigste Unterschiede auf einen Blick
- 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 |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkGeprüfte Benchmark-Ergebnisse im Vergleich
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.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
DeepSeek V4.1-Flash · Strengths & Key Advantages
- Fortschrittliche Causal Encoder-Decoder (20 enc + 20 dec layers)-Architektur von DeepSeek mit 1M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0.3/1M Input, $1.2/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
GLM-5.2 · Strengths & Key Advantages
- Fortschrittliche Massive Million-Token Open MoE Frontier Transformer-Architektur von Zhipu AI mit 1M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0/1M Input, $0/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
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 |
Technische Spezifikationen
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: Schnelle Antworten (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.
Fazit
Verdict & Model Selection Recommendation: Choose DeepSeek V4.1-Flash if your priority is Extreme budget batch processing, self-hosted commodity hardware clusters, and massive long-form reasoning tasks. Its performance in demanding tasks justifies its operational token cost. Choose GLM-5.2 if your workflow prioritizes Large-scale repository refactoring, national research initiatives, and sovereign enterprise intelligence. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Häufig gestellte Fragen (FAQ)
How does DeepSeek V4.1-Flash compare to GLM-5.2 in API token pricing and cost per token?+
DeepSeek V4.1-Flash costs $0.30 per 1M input tokens and $1.20 per 1M output tokens, whereas GLM-5.2 costs $0.00 input and $0.00 output. Overall, GLM-5.2 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: DeepSeek V4.1-Flash or GLM-5.2?+
Both DeepSeek V4.1-Flash and GLM-5.2 offer an identical context window of 1,000K tokens (~750k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: DeepSeek V4.1-Flash or GLM-5.2?+
DeepSeek V4.1-Flash scores 74.2% on SWE-bench Verified while GLM-5.2 scores 67.5%. For autonomous software engineering and multi-file debugging, DeepSeek V4.1-Flash holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between DeepSeek V4.1-Flash and GLM-5.2?+
DeepSeek V4.1-Flash features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GLM-5.2 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between DeepSeek V4.1-Flash and GLM-5.2?+
DeepSeek V4.1-Flash generates approximately 110 tokens per second, while GLM-5.2 delivers 60 tokens per second. In latency-sensitive production environments, DeepSeek V4.1-Flash provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining DeepSeek V4.1-Flash and GLM-5.2 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GLM-5.2 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to DeepSeek V4.1-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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- Mistral Medium 3.5 vs DeepSeek V4.1-Flash: Benchmarks, Pricing & Speed ComparisonVergleich
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.
Offizielle Quellen: GetInfoUs Independent AI Benchmarks & Official API Specs