Phi-4 vs Qwen2.5-VL 72B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Phi-4 by Microsoft and Qwen2.5-VL 72B by Alibaba 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, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, Qwen2.5-VL 72B 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
Pequeño y potentede Microsoft 14B de pesos abiertos modelo trained extensively en alto-calidad synthetic datasets un deliver matemáticas y codificación density rivaling grande modelos.
$0.00 / 1M
$0.00 / 1M
16.4K tokens
N/A
Qwen2.5-VL 72B
Visión 72B abiertaEl modelo de visión y lenguaje de código abierto insignia de Alibaba líder en comprensión de diagramas, OCR de documentos y razonamiento visual.
$0.00 / 1M
$0.00 / 1M
131.1K tokens
38%
Diferencias Clave de un Vistazo
- Both models cost the same under this workload — choose on capability, not price.
- Qwen2.5-VL 72B has a larger context window (131K vs 16K tokens).
- Phi-4 is faster at 135 tok/s vs 55 tok/s.
| Característica | Phi-4 | Qwen2.5-VL 72B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 80 | 79 |
Provider | Microsoft | Alibaba |
Input Price Cost per million input tokens | $0.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.00 / 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 | 55 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 38% |
GPQA Diamond Graduate-level reasoning | 55% | 58% |
¿Quién gana en cada área?
At-a-glance decision frameworkEvaluations ongoing
Puntuaciones 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.
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.
Phi-4 · Strengths & Key Advantages
- Remarkable STEM y codificación rendimiento packed en un compacto 14B parámetro huella
- Easily deployable en único desktop GPUs (RTX 4070/4080) y Apple Silicon Macs
- de pesos abiertos con permissive MIT licensing
Considerations & Trade-offs
- Relatively compacto 16K ventana de contexto limits grande documento ingesta
- Max salida tokens capped un 4096 tokens
Qwen2.5-VL 72B · Strengths & Key Advantages
- superior-nivel de pesos abiertos visual razonamiento y video comprensión
- Dynamic resolución visual encoding preservando aspect ratios
- completo alojamiento propio liberty con zero propietario API datos retention
Considerations & Trade-offs
- Demands multi-GPU hardware (e.g. 2x A100/H100 80GB) para comfortable inferencia
- inferencia rendimiento es slower que hosted ligero modelos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Phi-4 | Qwen2.5-VL 72B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 12K | 20K |
Reasoning Tokens / Task Thinking tokens burned per task | 3K | 6K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Phi-4 | Qwen2.5-VL 72B |
|---|---|---|
| Official API Model ID (SDK) | phi-4 | qwen2-5-vl-72b-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-12 | 2024-09 |
| Knowledge Cutoff | June 2024 | June 2024 |
| Architecture | Synthetic-Data Dense Reasoning Transformer | Open-Weights Multimodal Vision-Language Transformer |
| Parameters | 14B | 72B |
| 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) | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| 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 | 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) · Vision (~258–765 tokens per image tile) |
| 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) · 45 GB VRAM (4-bit Quantized) · 155 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, vision |
| Output Modalities | text | text |
Phi-4 vs Qwen2.5-VL 72B: Respuestas Rápidas (FAQ)
Which is more intelligent, Phi-4 or Qwen2.5-VL 72B?
Phi-4 is more intelligent. It scores 80 compared with 79 on the composite Intelligence Index.
Which model is faster?
Phi-4 is faster, generating 135 tokens per second compared with 55 tokens per second.
Which model has lower latency?
Phi-4 has lower latency, with a time to first token of 0.27s compared with 0.72s.
Which model has a larger context window?
Qwen2.5-VL 72B has a larger context window, supporting 131.1K tokens compared with 16.4K tokens.
Veredicto
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 Qwen2.5-VL 72B if your workflow prioritizes Self-hosted visual intelligence, private document parsing, and research benchmarks. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Preguntas frecuentes
How does Phi-4 compare to Qwen2.5-VL 72B in API token pricing and cost per token?+
Both Phi-4 and Qwen2.5-VL 72B are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: Phi-4 or Qwen2.5-VL 72B?+
Phi-4 supports a context window of 16K tokens (~12k words), while Qwen2.5-VL 72B supports 131K tokens (~98k words). Therefore, Qwen2.5-VL 72B 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 Qwen2.5-VL 72B?+
Phi-4 scores at the frontier of coding benchmarks, while Qwen2.5-VL 72B scores 38%. For complex autonomous debugging and agentic engineering, Qwen2.5-VL 72B provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Phi-4 and Qwen2.5-VL 72B?+
Phi-4 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Qwen2.5-VL 72B provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Phi-4 and Qwen2.5-VL 72B?+
Phi-4 generates approximately 135 tokens per second, while Qwen2.5-VL 72B delivers 55 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 Qwen2.5-VL 72B to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Phi-4, and route deep architectural or complex reasoning queries to Phi-4. This balanced routing strategy maximizes system throughput and answer quality.
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