Claude Haiku 4.5 vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Claude Haiku 4.5 by Anthropic against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
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Live comparison: no in-depth editorial review has been published for this pair yet.
Claude Haiku 4.5
Inférence Ultra-RapideClaude Haiku 4.5 de Anthropic est un modèle d’IA conçu pour les applications en temps réel à très faible latence et le routage à haut débit, offrant une fenêtre de contexte de 200K, une vitesse de 140 tokens/s, un indice d’intelligence de 5
$1.00 / 1M
$5.00 / 1M
200K tokens
58.2%
Llama 5
Modèle de Base à Poids OuvertsLlama 5 de Meta est un modèle d’IA conçu pour l’auto-hébergement sur infrastructure privée, le fine-tuning et la souveraineté des données, offrant une fenêtre de contexte de 5M, une vitesse de 65 tokens/s, un indice d’intelligence de 94/100
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Différences Clés en un Coup d’Œil
- 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 Haiku 4.5 is faster at 140 tok/s vs 65 tok/s.
- Llama 5 leads on SWE-bench coding (74.0% vs 58.2%).
| Caractéristique | Claude Haiku 4.5 | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 54 | 94 |
Provider | Anthropic | Meta |
Input Price Cost per million input tokens | $1.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $5.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.77 / 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 | 64K | 128K |
Throughput Speed Tokens generated per second | 140 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 58.2% | 74% |
GPQA Diamond Graduate-level reasoning | 62.1% | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.00 / 1M |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
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.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Tokens emitted per second during active generation streaming.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Claude Haiku 4.5 · Strengths & Key Advantages
- Fenêtre de contexte de 200K et capacité de sortie maximale de 64K tokens (SWE-bench Verified 58.2% · GPQA Diamond 62.1%)
- Débit rapide de 140 tokens/seconde avec une latence au premier token (TTFT) de 280 ms
- Tarification API optimisée ($1/$5 par million de tokens) avec prise en charge du cache de prompts et des schémas JSON stricts
Considerations & Trade-offs
- Modèle cloud propriétaire ne permettant pas le téléchargement local des poids sur serveur isolé
- Sur les problèmes de recherche fondamentale très complexes, les modèles de raisonnement lourd restent supérieurs
Llama 5 · Strengths & Key Advantages
- Fenêtre de contexte de 5M et capacité de sortie maximale de 128K tokens (SWE-bench Verified 74% · GPQA Diamond 84%)
- Débit rapide de 65 tokens/seconde avec une latence au premier token (TTFT) de 850 ms
- Poids ouverts (600B+ total (~40-80B )) permettant un déploiement sur site ou cloud privé via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Nécessite des serveurs GPU dotés de suffisamment de mémoire VRAM pour l’inférence locale à pleine précision
- Sur les problèmes de recherche fondamentale très complexes, les modèles de raisonnement lourd restent supérieurs
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Claude Haiku 4.5 | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.70 | $0.00 |
Output Tokens / Task Tokens generated per task | 20K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 10K | 20K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Claude Haiku 4.5 | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | claude-haiku-4-5-20251001 | meta-llama/Llama-5 |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-10 | 2026-04 |
| Knowledge Cutoff | July 2025 | January 2026 |
| Architecture | Lightweight High-Speed Multimodal 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 | $0.1/1M 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 Haiku 4.5 vs Llama 5: Réponses Rapides (FAQ)
Which is more intelligent, Claude Haiku 4.5 or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 54 on the composite Intelligence Index.
Which model is faster?
Claude Haiku 4.5 is faster, generating 140 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 $0.77 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Claude Haiku 4.5 has lower latency, with a time to first token of 0.28s 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.
Verdict
Choose Claude Haiku 4.5 for conçu pour les applications en temps réel à très faible latence et le routage à haut débit, l’intégration rag et les pipelines de production évolutifs., or choose Llama 5 for conçu pour l’auto-hébergement sur infrastructure privée, le fine-tuning et la souveraineté des données, l’intégration rag et les pipelines de production évolutifs..
Questions fréquentes
Which is cheaper: Claude Haiku 4.5 or Llama 5?+
Claude Haiku 4.5 costs $1/1M input and $5/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: Claude Haiku 4.5 or Llama 5?+
Claude Haiku 4.5 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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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: Anthropic & Meta Official Documentation