Phi-4 vs DeepSeek-R1 Distill Llama 70B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Phi-4 by Microsoft and DeepSeek-R1 Distill Llama 70B 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, Phi-4 holds a notable pricing advantage for token ingestion. On output generation, Phi-4 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, DeepSeek-R1 Distill Llama 70B provides an expanded memory horizon with 131K tokens. In latency-sensitive production environments, Phi-4 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.
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Phi-4
Modello a Pesi ApertiPhi-4 di Microsoft è un modello di intelligenza artificiale con finestra di contesto da 16K token e tariffazione API di $0/1M token in input e $0/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.00 / 1M
$0.00 / 1M
16.4K tokens
N/A
DeepSeek-R1 Distill Llama 70B
Modello a Pesi ApertiDeepSeek-R1 Distill Llama 70B di DeepSeek è un modello di intelligenza artificiale con finestra di contesto da 131K token e tariffazione API di $0.35/1M token in input e $1.05/1M in output, ideale per coding, ragionamento e automazione azie
$0.35 / 1M
$1.05 / 1M
131.1K tokens
57.5%
Differenze Chiave a Colpo d’Occhio
- Phi-4 is 1.0x cheaper for API usage (input + output tokens combined).
- DeepSeek-R1 Distill Llama 70B has a larger context window (131K vs 16K tokens).
- Phi-4 is faster at 135 tok/s vs 48 tok/s.
| Feature | Phi-4 | DeepSeek-R1 Distill Llama 70B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 80 | 48 |
Provider | Microsoft | DeepSeek |
Input Price Cost per million input tokens | $0.00 / 1M | $0.35 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $1.05 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.20 / 1M |
Context Window Maximum input + output tokens | 16.4K tokens~25 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 4K | 8K |
Throughput Speed Tokens generated per second | 135 tok/s | 48 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 57.5% |
GPQA Diamond Graduate-level reasoning | 55% | 70% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.04 / 1M |
Chi vince in ogni categoria?
At-a-glance decision frameworkEvaluations ongoing
Punteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Phi-4 · Strengths & Key Advantages
- Architettura Synthetic-Data Dense Reasoning Transformer avanzata di Microsoft con supporto fino a 16K token di contesto
- Ottimo rapporto qualità-prezzo ($0/1M input, $0/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
DeepSeek-R1 Distill Llama 70B · Strengths & Key Advantages
- Architettura Dense Distilled Reasoning Transformer avanzata di DeepSeek con supporto fino a 131K token di contesto
- Ottimo rapporto qualità-prezzo ($0.35/1M input, $1.05/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Phi-4 | DeepSeek-R1 Distill Llama 70B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.42 |
Output Tokens / Task Tokens generated per task | 12K | 27K |
Reasoning Tokens / Task Thinking tokens burned per task | 3K | 21K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Phi-4 | DeepSeek-R1 Distill Llama 70B |
|---|---|---|
| Official API Model ID (SDK) | phi-4 | deepseek-reasoner |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-12 | 2025-01 |
| Knowledge Cutoff | June 2024 | July 2024 |
| Architecture | Synthetic-Data Dense Reasoning Transformer | Dense Distilled Reasoning Transformer |
| Parameters | 14B | 70B |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($0.35/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix caching at $0.035/1M (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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 9 GB VRAM (4-bit Quantized) · 30 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text |
| Output Modalities | text | text |
Phi-4 vs DeepSeek-R1 Distill Llama 70B: Risposte Rapide (FAQ)
Which is more intelligent, Phi-4 or DeepSeek-R1 Distill Llama 70B?
Phi-4 is more intelligent. It scores 80 compared with 48 on the composite Intelligence Index.
Which model is faster?
Phi-4 is faster, generating 135 tokens per second compared with 48 tokens per second.
Which model is cheaper to run?
Phi-4 is cheaper at $0.00 per 1M tokens blended, compared with $0.20 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Phi-4 has lower latency, with a time to first token of 0.27s compared with 0.88s.
Which model has a larger context window?
DeepSeek-R1 Distill Llama 70B has a larger context window, supporting 131.1K tokens compared with 16.4K tokens.
Verdetto
Verdict & Model Selection Recommendation: Choose Phi-4 if your priority is Local edge developer assistants, offline math problem-solving, and efficient workstation tasks. Its performance in demanding tasks justifies its operational token cost. Choose DeepSeek-R1 Distill Llama 70B if your workflow prioritizes Enterprise research, academic scientific reasoning, and autonomous multi-agent pipelines. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Domande Frequenti (FAQ)
How does Phi-4 compare to DeepSeek-R1 Distill Llama 70B in API token pricing and cost per token?+
Phi-4 costs $0.00 per 1M input tokens and $0.00 per 1M output tokens, whereas DeepSeek-R1 Distill Llama 70B costs $0.35 input and $1.05 output. Overall, Phi-4 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: Phi-4 or DeepSeek-R1 Distill Llama 70B?+
Phi-4 supports a context window of 16K tokens (~12k words), while DeepSeek-R1 Distill Llama 70B supports 131K tokens (~98k words). Therefore, DeepSeek-R1 Distill Llama 70B can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Phi-4 or DeepSeek-R1 Distill Llama 70B?+
Phi-4 scores at the frontier of coding benchmarks, while DeepSeek-R1 Distill Llama 70B scores 57.5%. For complex autonomous debugging and agentic engineering, DeepSeek-R1 Distill Llama 70B provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Phi-4 and DeepSeek-R1 Distill Llama 70B?+
Phi-4 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, DeepSeek-R1 Distill Llama 70B provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Phi-4 and DeepSeek-R1 Distill Llama 70B?+
Phi-4 generates approximately 135 tokens per second, while DeepSeek-R1 Distill Llama 70B delivers 48 tokens per second. In latency-sensitive production environments, Phi-4 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Phi-4 and DeepSeek-R1 Distill Llama 70B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Phi-4 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to DeepSeek-R1 Distill Llama 70B. 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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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Fonti ufficiali: GetInfoUs Independent AI Benchmarks & Official API Specs