DeepSeek-Coder-V2 236B vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare DeepSeek-Coder-V2 236B by DeepSeek against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
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DeepSeek-Coder-V2 236B
Modèle de Base à Poids OuvertsDeepSeek-Coder-V2 236B de DeepSeek est un modèle d’IA conçu pour l’auto-hébergement sur infrastructure privée, le fine-tuning et la souveraineté des données, offrant une fenêtre de contexte de 128K, une vitesse de 74 tokens/s, un indice d’i
$0.14 / 1M
$0.28 / 1M
128K tokens
42.1%
Gemini 4 Argon
IA Multimodale NativeGemini 4 Argon de Google est un modèle d’IA conçu pour la compréhension multimodale native (texte, vision) et l’analyse de documents complexes, offrant une fenêtre de contexte de 1M, une vitesse de 115 tokens/s, un indice d’intelligence de
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Différences Clés en un Coup d’Œil
- DeepSeek-Coder-V2 236B is 25.2x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 128K tokens).
- Gemini 4 Argon is faster at 115 tok/s vs 74 tok/s.
- Gemini 4 Argon leads on SWE-bench coding (81.9% vs 42.1%).
| Caractéristique | DeepSeek-Coder-V2 236B | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 42 | 97 |
Provider | DeepSeek | |
Input Price Cost per million input tokens | $0.14 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.28 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.07 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 1000K |
Throughput Speed Tokens generated per second | 74 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | 42.1% | 81.9% |
GPQA Diamond Graduate-level reasoning | 49.5% | 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-Coder-V2 236B · Strengths & Key Advantages
- Fenêtre de contexte de 128K et capacité de sortie maximale de 8K tokens (SWE-bench Verified 42.1% · GPQA Diamond 49.5%)
- Débit rapide de 74 tokens/seconde avec une latence au premier token (TTFT) de 510 ms
- Poids ouverts (236B) permettant un déploiement sur site ou cloud privé via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Nécessite des serveurs GPU dotés de suffisamment de mémoire VRAM pour l’inférence locale à pleine précision
- Sur les problèmes de recherche fondamentale très complexes, les modèles de raisonnement lourd restent supérieurs
Gemini 4 Argon · Strengths & Key Advantages
- Fenêtre de contexte de 1M et capacité de sortie maximale de 1M tokens (SWE-bench Verified 81.9% · GPQA Diamond 81.5%)
- Débit rapide de 115 tokens/seconde avec une latence au premier token (TTFT) de 850 ms
- Tarification API optimisée ($2/$10 par million de tokens) avec prise en charge du cache de prompts et des schémas JSON stricts
Considerations & Trade-offs
- Modèle cloud propriétaire ne permettant pas le téléchargement local des poids sur serveur isolé
- Pour les charges à très fort volume, l’activation du cache de prompts et de l’API Batch est recommandée
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | DeepSeek-Coder-V2 236B | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.14 | $2.45 |
Output Tokens / Task Tokens generated per task | 30K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | DeepSeek-Coder-V2 236B | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | deepseek-chat | models/gemini-4-argon |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2024-06 | 2026-09 |
| Knowledge Cutoff | December 2023 | June 2026 |
| Architecture | 236B Mixture-of-Experts (21B active) | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | 236B | 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 ($0.14/1M in) across full 128K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.014/1M (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) · 146 GB VRAM (4-bit Quantized) · 507 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-Coder-V2 236B vs Gemini 4 Argon: Réponses Rapides (FAQ)
Which is more intelligent, DeepSeek-Coder-V2 236B or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 42 on the composite Intelligence Index.
Which model is faster?
Gemini 4 Argon is faster, generating 115 tokens per second compared with 74 tokens per second.
Which model is cheaper to run?
DeepSeek-Coder-V2 236B is cheaper at $0.07 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
DeepSeek-Coder-V2 236B has lower latency, with a time to first token of 0.51s compared with 0.85s.
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 128K tokens.
Verdict
Choose DeepSeek-Coder-V2 236B for conçu pour l’auto-hébergement sur infrastructure privée, le fine-tuning et la souveraineté des données, l’intégration rag et les pipelines de production évolutifs., or choose Gemini 4 Argon for conçu pour la compréhension multimodale native (texte, vision) et l’analyse de documents complexes, l’intégration rag et les pipelines de production évolutifs..
Questions fréquentes
Which is cheaper: DeepSeek-Coder-V2 236B or Gemini 4 Argon?+
DeepSeek-Coder-V2 236B costs $0.14/1M input and $0.28/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: DeepSeek-Coder-V2 236B or Gemini 4 Argon?+
DeepSeek-Coder-V2 236B supports 128K 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