DeepSeek V4 vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare DeepSeek V4 by DeepSeek against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
Changez et Comparez les Modèles
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Live comparison: no in-depth editorial review has been published for this pair yet.
DeepSeek V4
Disrupteur de coûtsTrained à 1/10th le computational coût de propriétaire de pointe modèles, DeepSeek V4 brings massif MoE codage et 1M contexte à ouvert AI.
$0.30 / 1M
$1.20 / 1M
1M tokens
62.8%
Gemini 4 Argon
Roi multimodal avec sortie 1MGoogle de DeepMind phare de pointe raisonnement moteur intégrant 1-millions-token sortie limits, autonome cybersecurity defense, et en temps réel audio/vision ingestion.
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Différences Clés en un Coup d’Œil
- DeepSeek V4 is 7.7x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon is faster at 115 tok/s vs 78 tok/s.
- Gemini 4 Argon leads on SWE-bench coding (81.9% vs 62.8%).
| Caractéristique | DeepSeek V4 | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 56 | 97 |
Provider | DeepSeek | |
Input Price Cost per million input tokens | $0.30 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $1.20 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.20 / 1M | $1.47 / 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 | 66K | 1000K |
Throughput Speed Tokens generated per second | 78 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | 62.8% | 81.9% |
GPQA Diamond Graduate-level reasoning | 78.4% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $0.10 / 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 · Strengths & Key Advantages
- Trained à une fraction de Western de pointe cluster budgets, proving extreme efficacité
- 1M token fenêtre de contexte avec fiable multidocument needle recall
- Fully à poids ouverts sous permissive licensing pour unrestricted mondial déploiement
Considerations & Trade-offs
- Trails fermé billions-paramètre de pointe leaders sur niche multimodal visuel raisonnement
- Sensitive à prompt framing lorsque coordinating multitour agentic outils
Gemini 4 Argon · Strengths & Key Advantages
- Groundbreaking 1,000,000 token maximum sortie génération capacité via long Decode Continuation
- 1M-token entrée window avec 99.7% GraphWalks recall (84.2% dans le 256K-1M range)
- natif traitement de complet audio, vidéo streams, et visuel données (91.7% sur LVBench)
- Half le token prix de d'Anthropic Opus 5.5 et #1 sur Chatbot Arena (1525 Elo)
Considerations & Trade-offs
- massif token outputs peut incur élevé absolute coûts if unconstrained (~62K sortie tokens par index tâche)
- $2/$10 tarification est un introductory 50% discount; standard rates revert à $4/$20 once le promotion ends
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | DeepSeek V4 | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.38 | $2.45 |
Output Tokens / Task Tokens generated per task | 48K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 22K | 36K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | DeepSeek V4 | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | deepseek-reasoner | models/gemini-4-argon |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-07 | 2026-09 |
| Knowledge Cutoff | April 2026 | June 2026 |
| Architecture | Next-Gen Sparse MoE with MLA | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | MoE (Colossal) | 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 ($0.3/1M in) across full 1000K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.03/1M (no extra cache-write fee) | Implicit + Explicit Context Caching at $0.1/1M 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 | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | 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 | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 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) | Google DeepMind Frontier Safety Framework Verified |
| 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 | text, vision, audio, video |
| Output Modalities | text | text |
DeepSeek V4 vs Gemini 4 Argon: Réponses Rapides (FAQ)
Which is more intelligent, DeepSeek V4 or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 56 on the composite Intelligence Index.
Which model is faster?
Gemini 4 Argon is faster, generating 115 tokens per second compared with 78 tokens per second.
Which model is cheaper to run?
DeepSeek V4 is cheaper at $0.20 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
DeepSeek V4 has lower latency, with a time to first token of 0.60s compared with 0.85s.
Verdict
Choose DeepSeek V4 for open source d'entreprise déploiements, grand dépôt migrations, et à long terme développeur agents, or choose Gemini 4 Argon for whole-dépôt génération de code, complet-book synthèse, vidéo compréhension, et multi-hour audio traitement.
Questions fréquentes
Which is cheaper: DeepSeek V4 or Gemini 4 Argon?+
DeepSeek V4 costs $0.3/1M input and $1.2/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: DeepSeek V4 or Gemini 4 Argon?+
DeepSeek V4 supports 1M tokens whereas Gemini 4 Argon supports 1M tokens.
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: DeepSeek & Google Official Documentation