Claude 3.5 Haiku vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Claude 3.5 Haiku by Anthropic against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
किसी भी मॉडल को बदलें और तुलना करें
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Live comparison: no in-depth editorial review has been published for this pair yet.
Claude 3.5 Haiku
उच्च-प्रदर्शन AI मॉडलAnthropic द्वारा विकसित Claude 3.5 Haiku एक उन्नत AI मॉडल है, जिसमें 200K टोकन का कॉन्टेक्स्ट विंडो और $0.8/1M इनपुट तथा $4/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।
$0.80 / 1M
$4.00 / 1M
200K tokens
40.6%
Llama 5
ओपन-वेट्स मॉडलMeta द्वारा विकसित Llama 5 एक उन्नत AI मॉडल है, जिसमें 5M टोकन का कॉन्टेक्स्ट विंडो और $0/1M इनपुट तथा $0/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
एक नज़र में मुख्य अंतर
- 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%).
| Feature | Claude 3.5 Haiku | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 36 | 94 |
Provider | Anthropic | Meta |
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 text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 128K |
Throughput Speed Tokens generated per second | 130 tok/s | 65 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 |
किस क्षेत्र में कौन आगे है?
At-a-glance decision frameworkसत्यापित बेंचमार्क स्कोर तुलना
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.
लेटेंसी और रिस्पॉन्स समय
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
रियल-टाइम टोकन लागत सिम्युलेटर
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Claude 3.5 Haiku · Strengths & Key Advantages
- Anthropic की उन्नत Optimized Lightweight Transformer आर्किटेक्चर और 200K कॉन्टेक्स्ट विंडो क्षमता
- किफायती और पारदर्शी API मूल्य ($0.8/1M इनपुट, $4/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
- जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
- बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
- विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है
Llama 5 · Strengths & Key Advantages
- Meta की उन्नत Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) आर्किटेक्चर और 5M कॉन्टेक्स्ट विंडो क्षमता
- किफायती और पारदर्शी API मूल्य ($0/1M इनपुट, $0/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
- जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
- बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
- विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Claude 3.5 Haiku | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.90 | $0.00 |
Output Tokens / Task Tokens generated per task | 45K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 20K |
तकनीकी विशिष्टताएँ (Specs)
Architecture, release timeline, capabilities, and modalities.
| Feature | Claude 3.5 Haiku | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | claude-3-5-haiku-20241101 | meta-llama/Llama-5 |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-11 | 2026-04 |
| Knowledge Cutoff | July 2024 | January 2026 |
| Architecture | Optimized Lightweight Transformer | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | Confidential | 600B+ total (~40-80B active) |
| Enterprise Rate Limits (RPM / TPM) | 4,000 – 10,000 RPM (Tier 4 + Priority 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) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Free 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 TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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 | Native Web Search Citations + Code Execution Tool + MCP Connectors | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Custom Model Tuning via Amazon Bedrock & Prompt 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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| 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) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated | 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, video, audio |
| Output Modalities | text | text |
Claude 3.5 Haiku vs Llama 5: त्वरित प्रश्नोत्तर (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.
निष्कर्ष
Choose Claude 3.5 Haiku for एंटरप्राइज ऑटोमेशन, सॉफ्टवेयर डेवलपमेंट, ai एजेंट्स और anthropic आधारित प्रोडक्शन सिस्टम, or choose Llama 5 for एंटरप्राइज ऑटोमेशन, सॉफ्टवेयर डेवलपमेंट, ai एजेंट्स और meta आधारित प्रोडक्शन सिस्टम.
अक्सर पूछे जाने वाले प्रश्न (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.
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.
स्रोत: Anthropic & Meta Official Documentation