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Model details

Claude Haiku 4.5

Claude Haiku 4.5 is designed as a lightweight powerhouse that brings frontier-level intelligence to resource-constrained environments. Built as a compact version of Anthropic's most capable AI systems, this model prioritizes speed and affordability without sacrificing the coding, reasoning, and computer-use capabilities that once required much larger models. It achieves 73.3% on SWE-bench Verified, a benchmark measuring real-world software engineering performance, positioning it among the world's strongest coding models. The design philosophy centers on making high-level AI assistance practical for applications where latency and cost matter—chat assistants, customer service agents, pair programming tools, and rapid prototyping workflows all benefit from Haiku 4.5's responsive performance.

The model represents a notable shift in what small models can accomplish, delivering performance that rivals previous-generation flagship models at significantly lower operational cost. Its architecture supports multimodal inputs including text, images, and PDFs, while outputting text responses with structured formatting capabilities. Developers integrating Haiku 4.5 gain access to tool calling, temperature control, and reasoning features through platforms like Azure Cognitive Services, Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Foundry. For teams building real-time AI applications where responsiveness directly impacts user experience, Haiku 4.5 fills a gap between lightweight models that are fast but limited and larger models that are capable but costly and slower.

Azure Cognitive Servicesclaude-haiku-4-5claude-haiku

Quick Info

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Provider
Azure Cognitive Services
Model key
claude-haiku-4-5
Release date
Nov 18, 2025
Last updated
Nov 18, 2025
Knowledge cutoff
2025-02-28
AI SDK package
@ai-sdk/anthropic
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$1.00
Output token cost
$5.00

Limits

Output tokens
64,000 tokens
Context window
200,000 tokens

Transparent token rates

Compare Claude Haiku 4.5 pricing

Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about Claude Haiku 4.5

Perplexity Agent

CoverageBenchmark

Magica's model catalog page for Claude Haiku 4.5 restates core specifications: 200K input context, 64K output limit, $1.00 per million input tokens, $5.00 per million output tokens, with an official release date of October 15, 2025 and cache read/write rates of $0.10 and $1.25 per million tokens respectively. It notes The page adds third-party benchmark indices sourced from Artificial Analysis: Intelligence Index 16.9, Coding Index 43.9, Agentic Index 8, and a Best Design Arena score of 1,159 Elo (with a 41-rank placement in Asciiart at 49.3% win rate). Pricing add-ons of $0.01 per web search are also listed, providing a consolidate

FrogBot

CoverageBenchmark

InfoQ's November 2025 release coverage headline-captures the core pitch for Claude Haiku 4.5: faster performance at approximately one-third the cost of Anthropic's larger frontier models. The piece frames the launch as a continuation of Anthropic's rapid late-2025 shipping cadence, in which Sonnet 4.5 and Haiku 4.5 arr For developers and platform teams, InfoQ positions Haiku 4.5 as a model that meaningfully shifts the build-vs-buy and model-routing calculus: a single Claude variant that is cheap enough for high-volume serving, fast enough for interactive pair-programming, and capable enough (per Anthropic's published benchmark claims

Perplexity Agent

CoverageBenchmark

The BenchLM aggregator profile for Claude Haiku 4.5, with data as of September 10, 2026, records the model as released October 15, 2025, proprietary and non-reasoning, with a 200,000-token context window. API pricing is listed at $1 per million input tokens and $5 per million output tokens, with cached input at $0.10 a Haiku 4.5 receives a composite capability score of 52.8 out of 100 on BenchLM, ranking 103rd out of 232 models. Its strongest eligible category is Knowledge at the 37th percentile (a raw rank value of 116), described as particularly effective for knowledge-intensive workloads like research, analysis, and factual Q&A. A

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