Grok 4.20 (Reasoning) vs DeepSeek R1
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Grok 4.20 (Reasoning) by xAI and DeepSeek R1 by DeepSeek 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 R1 holds a notable pricing advantage for token ingestion. On output generation, DeepSeek R1 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Grok 4.20 (Reasoning) provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, Grok 4.20 (Reasoning) 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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Grok 4.20 (Reasoning)
Analyse de documentsde xAI modèle de raisonnement développé pour heavy document analyse, complexe logique deduction, et à long terme agentic utilisation d'outils.
$3.00 / 1M
$15.00 / 1M
1M tokens
68.2%
DeepSeek R1
Révolution du raisonnement open sourceModèle de raisonnement novateur à poids ouverts entraîné par apprentissage par renforcement à grande échelle, rivalisant avec les géants propriétaires pour une fraction du coût.
$0.55 / 1M
$2.19 / 1M
128K tokens
49.2%
Différences Clés en un Coup d’Œil
- DeepSeek R1 is 6.4x cheaper for API usage (input + output tokens combined).
- Grok 4.20 (Reasoning) has a larger context window (1M vs 128K tokens).
- Grok 4.20 (Reasoning) is faster at 95 tok/s vs 45 tok/s.
- Grok 4.20 (Reasoning) leads on SWE-bench coding (68.2% vs 49.2%).
| Caractéristique | Grok 4.20 (Reasoning) | DeepSeek R1 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 68 | 49 |
Provider | xAI | DeepSeek |
Input Price Cost per million input tokens | $3.00 / 1M | $0.55 / 1M |
Output Price Cost per million output tokens | $15.00 / 1M | $2.19 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.63 / 1M | $0.43 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 66K | 8K |
Throughput Speed Tokens generated per second | 95 tok/s | 45 tok/s |
SWE-bench Verified Real-world software engineering | 68.2% | 49.2% |
GPQA Diamond Graduate-level reasoning | 72.8% | 71.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.75 / 1M | $0.14 / 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.
Grok 4.20 (Reasoning) · Strengths & Key Advantages
- Expansive 1M-token fenêtre de contexte tailored pour dense dépôt et juridique document intake
- Robust multi-étapes chaîne de pensée raisonnement avec persistent à long terme outil exécution
- multimodal ingestion à travers technique diagrams, multi-page PDFs, et complexe charts
- Direct en temps réel knowledge synthèse de xAI Colossus cluster
Considerations & Trade-offs
- Outperformed par Grok 4.7 et late-2026 de pointe leaders sur compétitif codage benchmarks
- supérieur sortie latence during étendu chaîne de pensée génération cycles
DeepSeek R1 · Strengths & Key Advantages
- Phenomenal mathématiques et logique benchmark results (71.5% sur GPQA Diamond)
- Extremely abordable ouvert API tarification ($0.55 dans / $2.19 out)
- Permissive à poids ouverts license pour auto-hébergement sur local clusters
- Visible chaîne de pensée internal raisonnement process
Considerations & Trade-offs
- Slower latence due à étendu internal raisonnement génération
- texte-seulement (no natif image ou audio modality)
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Grok 4.20 (Reasoning) | DeepSeek R1 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.45 | $0.65 |
Output Tokens / Task Tokens generated per task | 45K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 28K | 25K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Grok 4.20 (Reasoning) | DeepSeek R1 |
|---|---|---|
| Official API Model ID (SDK) | grok-4-20-reasoning-latest | deepseek-reasoner |
| API Protocol | xAI REST API (OpenAI-Compatible) | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-03 | 2025-01 |
| Knowledge Cutoff | November 2025 | July 2024 |
| Architecture | Colossus-Scale Reasoning Sparse MoE | Open-Weights Reasoning MoE |
| Parameters | Confidential | 671B Total (37B Active) |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM | 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 ($3/1M in) across full 1000K window | Flat rate ($0.55/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Cached input at $0.75/1M tokens | Automatic prefix caching at $0.14/1M (no extra cache-write fee) |
| 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 | Real-time Web & X (Twitter) Search Grounding + Code Execution | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | 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 | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 416 GB VRAM (4-bit Quantized) · 1443 GB VRAM (Native BF16) |
| Safety & Frontier Classification | xAI Production Safety & Alignment Verified | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
Grok 4.20 (Reasoning) vs DeepSeek R1: Réponses Rapides (FAQ)
Which is more intelligent, Grok 4.20 (Reasoning) or DeepSeek R1?
Grok 4.20 (Reasoning) is more intelligent. It scores 68 compared with 49 on the composite Intelligence Index.
Which model is faster?
Grok 4.20 (Reasoning) is faster, generating 95 tokens per second compared with 45 tokens per second.
Which model is cheaper to run?
DeepSeek R1 is cheaper at $0.43 per 1M tokens blended, compared with $2.63 (7:2:1 cache:input:output ratio).
Which model has lower latency?
DeepSeek R1 has lower latency, with a time to first token of 0.95s compared with 0.98s.
Which model has a larger context window?
Grok 4.20 (Reasoning) has a larger context window, supporting 1M tokens compared with 128K tokens.
Verdict
Verdict & Model Selection Recommendation: Choose Grok 4.20 (Reasoning) if your priority is Deep document analysis, multi-stage enterprise research, and extensive tool-driven workflows. Its performance in demanding tasks justifies its operational token cost. Choose DeepSeek R1 if your workflow prioritizes Competitive coding, mathematical research, and privacy-first on-premise deployments. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does Grok 4.20 (Reasoning) compare to DeepSeek R1 in API token pricing and cost per token?+
Grok 4.20 (Reasoning) costs $3.00 per 1M input tokens and $15.00 per 1M output tokens, whereas DeepSeek R1 costs $0.55 input and $2.19 output. Overall, DeepSeek R1 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: Grok 4.20 (Reasoning) or DeepSeek R1?+
Grok 4.20 (Reasoning) supports a context window of 1,000K tokens (~750k words), while DeepSeek R1 supports 128K tokens (~96k words). Therefore, Grok 4.20 (Reasoning) can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Grok 4.20 (Reasoning) or DeepSeek R1?+
Grok 4.20 (Reasoning) scores 68.2% on SWE-bench Verified while DeepSeek R1 scores 49.2%. For autonomous software engineering and multi-file debugging, Grok 4.20 (Reasoning) holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Grok 4.20 (Reasoning) and DeepSeek R1?+
Grok 4.20 (Reasoning) features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, DeepSeek R1 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Grok 4.20 (Reasoning) and DeepSeek R1?+
Grok 4.20 (Reasoning) generates approximately 95 tokens per second, while DeepSeek R1 delivers 45 tokens per second. In latency-sensitive production environments, Grok 4.20 (Reasoning) provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Grok 4.20 (Reasoning) and DeepSeek R1 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to DeepSeek R1 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Grok 4.20 (Reasoning). 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