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Model details

Claude Haiku 4.5

Claude Haiku 4.5 marks a turning point for Anthropic's budget-friendly model line, introducing capabilities that were previously exclusive to higher-tier models. This release brings extended thinking, computer use, and context awareness to the Haiku family for the first time, representing frontier-level functionality compressed into an efficient architecture. The model achieves 73.3% on SWE-bench Verified, a benchmark score that places it among the world's strongest coding models and demonstrates that the efficiency focus did not come at the cost of genuine capability. By bringing these advanced features to a lightweight model, Anthropic has made near-frontier intelligence practical for applications where running larger models would be prohibitive.

The rapid capability diffusion visible in Haiku 4.5 reflects a broader trend in Anthropic's recent model development. Following Opus 4.1 and Sonnet 4.5, Haiku 4.5 delivers performance roughly on par with Sonnet 4 while operating at a significantly lower price point, which the company describes as roughly one-third the cost. This positioning makes Haiku 4.5 particularly well-suited for multi-agent orchestration, scaled deployments, and workflows requiring high-volume inference across complex tasks. The combination of extended thinking with the model's efficiency creates opportunities for developers building automated pipelines that previously required more expensive infrastructure, opening near-frontier AI capabilities to a wider range of production use cases.

Azureclaude-haiku-4-5claude-haiku

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Provider
Azure
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

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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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