Grok 4.7 vs DeepSeek V4.1-Flash
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Grok 4.7 by xAI and DeepSeek V4.1-Flash 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 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, DeepSeek V4.1-Flash provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, both models deliver balanced throughput and low time-to-first-token latency for interactive conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
Cambia y Compara Cualquier Modelo
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Grok 4.7
Mejora al mismo precio2.1 billones parámetro potencia con four-nivel adjustable razonamiento, superior legal agente scores, y unchanged $2/$6 precios.
$2.00 / 1M
$6.00 / 1M
500K tokens
77.2%
DeepSeek V4.1-Flash
Asesino de caché KV763B total / 16B activo causal encoder-decoder MoE con 1/4 KV-cache usage, nativo visión, MIT license, y mundo-record bajo off-pico precios.
$0.30 / 1M
$1.20 / 1M
1M tokens
74.2%
Diferencias Clave de un Vistazo
- DeepSeek V4.1-Flash is 5.9x cheaper for API usage (input + output tokens combined).
- DeepSeek V4.1-Flash has a larger context window (1M vs 500K tokens).
- DeepSeek V4.1-Flash is faster at 110 tok/s vs 110 tok/s.
- Grok 4.7 leads on SWE-bench coding (77.2% vs 74.2%).
| Característica | Grok 4.7 | DeepSeek V4.1-Flash |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 46 | 58 |
Provider | xAI | DeepSeek |
Input Price Cost per million input tokens | $2.00 / 1M | $0.30 / 1M |
Output Price Cost per million output tokens | $6.00 / 1M | $1.20 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.35 / 1M | $0.18 / 1M |
Context Window Maximum input + output tokens | 500K tokens~750 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 66K | 384K |
Throughput Speed Tokens generated per second | 110 tok/s | 110 tok/s |
SWE-bench Verified Real-world software engineering | 77.2% | 74.2% |
GPQA Diamond Graduate-level reasoning | 78.5% | 90.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.50 / 1M | $0.01 / 1M |
¿Quién gana en cada área?
At-a-glance decision frameworkPuntuaciones de Benchmarks Verificadas
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.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Grok 4.7 · Strengths & Key Advantages
- masivo 2.1T MoE parámetro escala con unchanged base precios ($2 en / $6 out por 1M)
- 4-level razonamiento effort control con superior legal agente score (Harvey legal 19.6%)
- alto biology y científico síntesis rendimiento (LatchBio 62.4%)
- sólido official CursorBench (46.3) y Terminal-Bench (38.0) rendimiento
Considerations & Trade-offs
- Independent pruebas de rendimiento show menor scores que official claims (Terminal-Bench ~26% vs 38%)
- de contexto largo queries (>=200K) double el effective API precios ($4 en / $12 out) un través del entire request
- multimodal imagen razonamiento remains somewhat weaker que competing visión flagships
DeepSeek V4.1-Flash · Strengths & Key Advantages
- MIT permissive abierto license con completo weights, arquitectura, y code
- Revolutionary causal encoder-decoder arquitectura: KV cache solo 890 bytes/token (se ejecuta en commodity NVMe SSDs)
- de mundo más bajo precios: off-pico cached entrada un just $0.003/1M y $0.27 costo por benchmark tarea
- Colossal 1M contexto entrada paired con 384,000 maximum salida tokens y nativo visión
Considerations & Trade-offs
- Extremely verbose generación (averages 8.9K salida tokens por inquiry sin explicit caps)
- de largo alcance rendimiento degrades en Terminal-Bench 4.0 (31.2%)
- Factual knowledge recall dips en SimpleQA (42.3) y visión resolución es capped un 672x672
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Grok 4.7 | DeepSeek V4.1-Flash |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.68 | $0.27 |
Output Tokens / Task Tokens generated per task | 46K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 23K | 28K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Grok 4.7 | DeepSeek V4.1-Flash |
|---|---|---|
| Official API Model ID (SDK) | grok-4-7-latest | deepseek-reasoner |
| API Protocol | xAI REST API (OpenAI-Compatible) | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-09 | 2026-09 |
| Knowledge Cutoff | April 2026 | June 2026 |
| Architecture | 2.1T Multimodal Sparse MoE | Causal Encoder-Decoder (20 enc + 20 dec layers) |
| Parameters | 2.1T (MoE) | 763B total (8B prefill / 16B decode active) |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 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 ($2/1M in) across full 500K window | Flat rate ($0.3/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.5/1M tokens | Automatic prefix caching at $0.006/1M (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) · Vision (~258–765 tokens per image tile) |
| 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) · 473 GB VRAM (4-bit Quantized) · 1640 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, vision |
| Output Modalities | text | text |
Grok 4.7 vs DeepSeek V4.1-Flash: Respuestas Rápidas (FAQ)
Which is more intelligent, Grok 4.7 or DeepSeek V4.1-Flash?
DeepSeek V4.1-Flash is more intelligent. It scores 58 compared with 46 on the composite Intelligence Index.
Which model is cheaper to run?
DeepSeek V4.1-Flash is cheaper at $0.18 per 1M tokens blended, compared with $1.35 (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 0.62s.
Which model has a larger context window?
DeepSeek V4.1-Flash has a larger context window, supporting 1M tokens compared with 500K tokens.
Veredicto
Verdict & Model Selection Recommendation: Choose Grok 4.7 if your priority is Affordable second opinions in coding and legal workflows, enterprise domain tasks, and balanced agentic automation. Its performance in demanding tasks justifies its operational token cost. Choose DeepSeek V4.1-Flash if your workflow prioritizes Extreme budget batch processing, self-hosted commodity hardware clusters, and massive long-form reasoning tasks. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Preguntas frecuentes
How does Grok 4.7 compare to DeepSeek V4.1-Flash in API token pricing and cost per token?+
Grok 4.7 costs $2.00 per 1M input tokens and $6.00 per 1M output tokens, whereas DeepSeek V4.1-Flash costs $0.30 input and $1.20 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: Grok 4.7 or DeepSeek V4.1-Flash?+
Grok 4.7 supports a context window of 500K tokens (~375k words), while DeepSeek V4.1-Flash supports 1,000K tokens (~750k words). Therefore, DeepSeek V4.1-Flash can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Grok 4.7 or DeepSeek V4.1-Flash?+
Grok 4.7 scores 77.2% on SWE-bench Verified while DeepSeek V4.1-Flash scores 74.2%. For autonomous software engineering and multi-file debugging, Grok 4.7 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Grok 4.7 and DeepSeek V4.1-Flash?+
Grok 4.7 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, DeepSeek V4.1-Flash provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Grok 4.7 and DeepSeek V4.1-Flash?+
Both Grok 4.7 and DeepSeek V4.1-Flash deliver matching throughput at approximately 110 tokens per second. In latency-sensitive production environments, streaming response velocity is comparable, with real-world time-to-first-token (TTFT) determined primarily by provider routing and concurrency load.
Can you implement a tiered LLM routing setup combining Grok 4.7 and DeepSeek V4.1-Flash 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 Grok 4.7. 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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Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: GetInfoUs Independent AI Benchmarks & Official API Specs