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.
Changez et Comparez les Modèles
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DeepSeek V4.1-Flash
Tueur de cache KV763B total / 16B actif causal encoder-decoder MoE avec 1/4 KV-cache usage, natif vision, MIT license, et monde-record faible off-sommet tarification.
$0.30 / 1M
$1.20 / 1M
1M tokens
74.2%
GLM-5.2
Contexte long économiquemassif millions-token open source phare offrant sustained multitour raisonnement, vast dépôt synthèse, et de pointe autonomy.
$0.00 / 1M
$0.00 / 1M
1M tokens
67.5%
Différences Clés en un Coup d’Œil
- 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%).
| Caractéristique | 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 |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
DeepSeek V4.1-Flash · Strengths & Key Advantages
- MIT permissive ouvert license avec complet weights, architecture, et code
- Revolutionary causal encoder-decoder architecture: KV cache seulement 890 bytes/token (fonctionne sur commodity NVMe SSDs)
- de monde le plus bas tarification: off-sommet cached entrée à just $0.003/1M et $0.27 coût par benchmark tâche
- Colossal 1M contexte entrée paired avec 384,000 maximum sortie tokens et natif vision
Considerations & Trade-offs
- Extremely verbose génération (averages 8.9K sortie tokens par inquiry sans explicit caps)
- à long terme performances degrades sur Terminal-Bench 4.0 (31.2%)
- Factual knowledge recall dips sur SimpleQA (42.3) et vision résolution est capped à 672x672
GLM-5.2 · Strengths & Key Advantages
- complet 1M token fenêtre de contexte paired avec un staggering 1440 Arena Elo score
- 67.5% SWE-bench score rivaling le la plupart capable commercial propriétaire modèles
- Complete à poids ouverts freedom avec extensive d'entreprise customization options
Considerations & Trade-offs
- L'empreinte mémoire colossale nécessite des clusters d'accélérateurs multinœuds
- texte-seulement modèle; visuel tâches require intégration avec GLM vision pipelines
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 |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | 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: Réponses Rapides (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.
Verdict
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.
Questions fréquentes
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.
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs