DeepSeek V4.1-Flash vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
DeepSeek V4.1-Flash by DeepSeek and Gemini 4 Argon by Google 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, DeepSeek V4.1-Flash holds a notable pricing advantage for token ingestion. On output generation, DeepSeek V4.1-Flash 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, Gemini 4 Argon 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%
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 V4.1-Flash is 7.9x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon is faster at 115 tok/s vs 110 tok/s.
- Gemini 4 Argon leads on SWE-bench coding (81.9% vs 74.2%).
| Caractéristique | DeepSeek V4.1-Flash | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 58 | 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 | $0.18 / 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 | 384K | 1000K |
Throughput Speed Tokens generated per second | 110 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | 74.2% | 81.9% |
GPQA Diamond Graduate-level reasoning | 90.9% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 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 V4.1-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
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 V4.1-Flash | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.27 | $2.45 |
Output Tokens / Task Tokens generated per task | 58K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 28K | 36K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | DeepSeek V4.1-Flash | 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 | 2026-09 | 2026-09 |
| Knowledge Cutoff | June 2026 | June 2026 |
| Architecture | Causal Encoder-Decoder (20 enc + 20 dec layers) | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | 763B total (8B prefill / 16B decode active) | 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 $0.006/1M (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) · Vision (~258–765 tokens per image tile) | 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) · 473 GB VRAM (4-bit Quantized) · 1640 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, vision | text, vision, audio, video |
| Output Modalities | text | text |
DeepSeek V4.1-Flash vs Gemini 4 Argon: Réponses Rapides (FAQ)
Which is more intelligent, DeepSeek V4.1-Flash or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 58 on the composite Intelligence Index.
Which model is faster?
Gemini 4 Argon is faster, generating 115 tokens per second compared with 110 tokens per second.
Which model is cheaper to run?
DeepSeek V4.1-Flash is cheaper at $0.18 per 1M tokens blended, compared with $1.47 (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 0.85s.
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 Gemini 4 Argon if your workflow prioritizes Whole-repository code generation, full-book synthesis, video understanding, and multi-hour audio processing. 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 Gemini 4 Argon 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 Gemini 4 Argon costs $2.00 input and $10.00 output. Overall, DeepSeek V4.1-Flash 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 Gemini 4 Argon?+
Both DeepSeek V4.1-Flash and Gemini 4 Argon 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 Gemini 4 Argon?+
DeepSeek V4.1-Flash scores 74.2% on SWE-bench Verified while Gemini 4 Argon scores 81.9%. For autonomous software engineering and multi-file debugging, Gemini 4 Argon holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between DeepSeek V4.1-Flash and Gemini 4 Argon?+
DeepSeek V4.1-Flash features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Gemini 4 Argon 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 Gemini 4 Argon?+
DeepSeek V4.1-Flash generates approximately 110 tokens per second, while Gemini 4 Argon delivers 115 tokens per second. In latency-sensitive production environments, Gemini 4 Argon 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 Gemini 4 Argon to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to DeepSeek V4.1-Flash to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Gemini 4 Argon. 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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- DeepSeek V4.1-Flash vs GLM-5.2: Benchmarks, Pricing & Speed ComparisonComparer
- Gemini 4 Argon vs Claude Fable 5.1: Benchmarks, Pricing & Speed ComparisonComparer
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