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Head-to-Head Model Comparison

Claude 3.5 Haiku vs Llama 5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

تمت المراجعة بواسطة فريق أبحاث GetInfoUsآخر مراجعة: October 2026السياسة التحريرية
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AnthropicMetalightweightopen-source

Compare Claude 3.5 Haiku by Anthropic against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.

مقارنة مباشرة (VS)

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Anthropic

Claude 3.5 Haiku

نموذج وكيل مستقل وأدوات

نموذج Claude 3.5 Haiku من Anthropic مصمم خصيصاً لـتشغيل الوكلاء المستقلين (Agents) واستدعاء الأدوات وأتمتة المهام متعددة المراحل، حيث يوفر نافذة سياق بسعة 200K وسرعة 130 رمز/ثانية ومؤشر ذكاء 36/100 (بتكلفة $0.8/$4 لكل مليون رمز).

Intelligence Index36 / 100
Input Token:

$0.80 / 1M

Output Token:

$4.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

40.6%

Meta

Llama 5

نموذج أساسي مفتوح الأوزان

نموذج Llama 5 من Meta مصمم خصيصاً لـالاستضافة الذاتية على الخوادم الخاصة والضبط الدقيق والسيادة الكاملة على البيانات، حيث يوفر نافذة سياق بسعة 5M وسرعة 65 رمز/ثانية ومؤشر ذكاء 94/100 (وصول مجاني بأوزان مفتوحة).

Intelligence Index94 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

5M tokens

SWE-bench Verified:

74%

أهم الفروقات في لمحة

  • Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
  • Llama 5 has a larger context window (5M vs 200K tokens).
  • Claude 3.5 Haiku is faster at 130 tok/s vs 65 tok/s.
  • Llama 5 leads on SWE-bench coding (74.0% vs 40.6%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureClaude 3.5 HaikuLlama 5
Intelligence Index
Composite reasoning & knowledge score (0-100)
3694
Provider
AnthropicMeta
Input Price
Cost per million input tokens
$0.80 / 1M$0.00 / 1M
Output Price
Cost per million output tokens
$4.00 / 1M$0.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.62 / 1M$0.00 / 1M
Context Window
Maximum input + output tokens
200K tokens~300 A4 pages of text5M tokens~7,500 A4 pages of text
Max Output Tokens
Per single request
8K128K
Throughput Speed
Tokens generated per second
130 tok/s65 tok/s
SWE-bench Verified
Real-world software engineering
40.6%74%
GPQA Diamond
Graduate-level reasoning
41.6%84%
Cached Input Price
Prompt caching discount per 1M tokens
$0.08 / 1M$0.00 / 1M

من يتفوق في كل مجال؟

At-a-glance decision framework
Coding
Llama 5
+33.4% SWE-bench lead
Cost
Llama 5
1.0x cheaper monthly runtime
Speed
Claude 3.5 Haiku
+100% faster generation
Context
Llama 5
5M tokens window

نتائج الاختبارات المعيارية الموثقة

Official evaluation metrics verified by independent third-party research labs.

Claude 3.5 HaikuLlama 5
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

40.6%vs74%
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

41.6%vs84%
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

69.2%vs87%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1260ptsvs1485pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

6.7%vsN/A
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

26.8%vsN/A
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

24.4%vsN/A
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

43.6%vs68%
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

47.8%vs88%
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

62.3%vsN/A
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

130 tok/s vs65 tok/s

زمن الاستجابة والسرعة

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
Claude 3.5 Haiku0.38s
Llama 50.85s
Est. End-to-End Response (500 tokens)TTFT + generation time
Claude 3.5 Haiku4.23s
Llama 58.54s

محاكي تكلفة الرموز التفاعلي

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Claude 3.5 Haiku Monthly API Bill
$4.92 / month
$0.8/1M in · $4/1M out
Llama 5 Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Llama 5 saves approximately $4.92 / month (100%) under this workload.1.0x Savings

Claude 3.5 Haiku · Strengths & Key Advantages

  • نافذة سياق بسعة 200K وحد أقصى للمخرجات يصل إلى 8K رمز (SWE-bench Verified 40.6% · GPQA Diamond 41.6%)
  • سرعة توليد عالية تبلغ 130 رمز/ثانية مع زمن استجابة لأول رمز (TTFT) قدره 380 مللي ثانية
  • تسعير API تنافسي (بتكلفة $0.8/$4 لكل مليون رمز) مع دعم التخزين المؤقت للأوامر والمخرجات المهيكلة بصيغة JSON
Considerations & Trade-offs
  • نموذج سحابي مغلق الأوزان لا يدعم تنزيل الأوزان للتشغيل المحلي المعزول عن الإنترنت
  • في المسائل البحثية فائقة التعقيد، قد تتفوق نماذج الاستدلال الثقيلة المتخصصة في عمق التحليل

Llama 5 · Strengths & Key Advantages

  • نافذة سياق بسعة 5M وحد أقصى للمخرجات يصل إلى 128K رمز (SWE-bench Verified 74% · GPQA Diamond 84%)
  • سرعة توليد عالية تبلغ 65 رمز/ثانية مع زمن استجابة لأول رمز (TTFT) قدره 850 مللي ثانية
  • أوزان مفتوحة (600B+ total (~40-80B )) تتيح الاستضافة الذاتية محلياً أو عبر السحابة الخاصة باستخدام vLLM وOllama
Considerations & Trade-offs
  • يتطلب خوادم GPU بذاكرة VRAM كافية عند التشغيل المحلي بالدقة الكاملة
  • في المسائل البحثية فائقة التعقيد، قد تتفوق نماذج الاستدلال الثقيلة المتخصصة في عمق التحليل

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricClaude 3.5 HaikuLlama 5
Cost per Task
Estimated USD to finish one task
$0.90$0.00
Output Tokens / Task
Tokens generated per task
45K40K
Reasoning Tokens / Task
Thinking tokens burned per task
0K20K

المواصفات التقنية

Architecture, release timeline, capabilities, and modalities.

FeatureClaude 3.5 HaikuLlama 5
Official API Model ID (SDK)claude-3-5-haiku-20241101meta-llama/Llama-5
API ProtocolAnthropic Messages API + AWS Bedrock + Vertex AIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2024-112026-04
Knowledge CutoffJuly 2024January 2026
ArchitectureOptimized Lightweight TransformerMixture-of-Experts with recursive self-improvement (~600B total, 40-80B active)
ParametersConfidential600B+ total (~40-80B active)
Enterprise Rate Limits (RPM / TPM)4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 800,000 – 2,000,000 TPMUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Standard rate up to 200K tokens; 2x input/output tier above 200K tokensFree across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL$0.08/1M cached read (90% off); 5-min default & 1-hour extended TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxNative Web Search Citations + Code Execution Tool + MCP ConnectorsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportCustom Model Tuning via Amazon Bedrock & Prompt DistillationFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)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 VRAMCloud-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) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16)
Safety & Frontier ClassificationAnthropic RSP ASL-2 / ASL-3 Constitutional Safety EvaluatedOpen-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 Tier100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext, vision, video, audio
Output Modalitiestexttext

Claude 3.5 Haiku vs Llama 5: إجابات سريعة (FAQ)

Which is more intelligent, Claude 3.5 Haiku or Llama 5?

Llama 5 is more intelligent. It scores 94 compared with 36 on the composite Intelligence Index.

Which model is faster?

Claude 3.5 Haiku is faster, generating 130 tokens per second compared with 65 tokens per second.

Which model is cheaper to run?

Llama 5 is cheaper at $0.00 per 1M tokens blended, compared with $0.62 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Claude 3.5 Haiku has lower latency, with a time to first token of 0.38s compared with 0.85s.

Which model has a larger context window?

Llama 5 has a larger context window, supporting 5M tokens compared with 200K tokens.

الحكم النهائي

Choose Claude 3.5 Haiku for مثالي لـتشغيل الوكلاء المستقلين (agents) واستدعاء الأدوات وأتمتة المهام متعددة المراحل وأنظمة rag المؤسسية وربط واجهات البرمجة (api) القابلة للتوسع., or choose Llama 5 for مثالي لـالاستضافة الذاتية على الخوادم الخاصة والضبط الدقيق والسيادة الكاملة على البيانات وأنظمة rag المؤسسية وربط واجهات البرمجة (api) القابلة للتوسع..

الأسئلة الشائعة (FAQ)

Which is cheaper: Claude 3.5 Haiku or Llama 5?+

Claude 3.5 Haiku costs $0.8/1M input and $4/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.

Which model has a larger context window: Claude 3.5 Haiku or Llama 5?+

Claude 3.5 Haiku supports 200K tokens whereas Llama 5 supports 5M tokens.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

المصادر الرسمية: Anthropic & Meta Official Documentation